最新刊期
卷 21 ， 期 4 ， 2017
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摘要：" Pujiang-1” satellite was successfully launched by the ChangZheng-11 vehicle from the JiuQuan Satellite Launch Center, China on September 25, 2015. The camera subsystem is one of the key payloads of the " Pujiang-1” satellite. The optical system, the pointer mirror mechanism, and the electronics of the camera were designed and produced under the concept of integration and miniaturization, which led to the compact, small, and light weight structure of the camera. With the novel design of the off-axis three-mirror reflective optical system, the camera includeda larger field of view and higher MTF value without a cover compared with the traditional coaxial system. By optimizing the layout of the optical elements, small volume was achieved. The difficulty of mechanical design and alignment was also reduced by optimizing the tolerance of the optical system. Additionally, this optical system was designed without a focusing mechanism but it could still address imaging needs because the back focal length was insensitive to the spacing change of the mirror. The camera used the pointer mirror mechanism to realize multi-target rapid pointing, enabling the capturemulti-target in each single imaging cycle, thereby shortening reaction time and improving imaging efficiency. The pointer mirror mechanism adopted the motor and gear set drive mode. The photoelectric encoder was installed coaxially with the mirror, with which the point angle can be precisely and accurately measured. In the electronic part, several functions, such as video imaging, image compression and coding, remote telemetry, temperature control, and power supply and distribution were integrated into one device. Standard plug-and-play 1553B communication and small-shape high-speed SpaceWire transmission interfaces were adopted to achieve rapid assembly and testing equipment in line with the modular design trend. The integrated circuit design reduced the number of devices, the overall system volume, system weight, and power consumption. After the satellite operating in-orbit, the in-orbit commissioning process was optimized combined with user needs. All the test and evaluation work were completed within one month.The camera normallyoperated with superior performance until today. The successful development of this camera and in-orbit stable operation provides a strong technical support for the follow-up high-performance light and smart cameras.关键词：off-axis three-mirror reflective;pointer mirror mechanism;highly-integrated electronic device design;in-orbit commissioning- 239
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发布时间：2021-06-07 -
摘要：The physical meaning of Absorbing Aerosol Index (AAI) is the spectral contrast at two ultraviolet (UV) wavelengths caused by absorbing aerosols; this contrast can be separated from other effects, including molecular Rayleigh scattering, surface reflection, gaseous absorption, and aerosol and cloud scattering. The AAI an indicate the presence of elevated absorbing aerosols; therefore, AAI based on space-borne backscattered UV instrument is a qualitative measure for atmospheric absorbing aerosols, such as desert and Black Carbon (BC) aerosols. The space-borne AAI data can be used for the study of spatial distribution and transport of absorbing aerosols, such as dust storm and environment pollution. Owing to the weak absorption of ozone in 331nm channel, such as the FY-3B Total Ozone Unit (TOU), a few relationships might exist between the AAI and the total column ozone. Thus, the AAI cannot completely separate the gaseous absorption effect from the aerosol absorption effect. The AAI is retrieved by comparing the measured backscattered UV radiances on top of the atmosphere and the UV radiances of pure molecular atmosphere, such as the Rayleigh scattering radiances, which is calculated by the radiative transfer model with the total column ozone as input. Hence, the retrieval of the AAI based on UV instruments might be affected by the uncertainty of ozone retrieval results. The radiative transfer model MODTRAN (MODerate resolution atmospheric TRANs mission) has been used to simulate the relationship between the AAI and the total column ozone, as well as the effect of ozone retrieval error on the retrieval of AAI. The simulation for the relation between the AAI and total column ozone is a sensitivity analysis process, in which the desert aerosol is adopted and the content of aerosol is fixed. The AAI is calculated by changing the total column ozone in the mid-latitude summer atmospheric model. The simulation results show that a 10% change of the total column ozone causes an approximately 15% change in the AAI. The effect of ozone uncertainty on the retrieval of the AAI is simulated through the calculation of the Rayleigh scattering radiance on top of the atmosphere by changing the total column ozone value in the mid-latitude summer atmosphere model. The results show that the AAI changes less than 1% with a 20% uncertainty in the total column ozone. The uncertainty of the total column ozone for FY-3/TOU is less than 5%, and the error of AAI caused by the ozone retrieval error is negligible. The AAI data of FY-3B/TOU for July of 2012 over the Taklimakan desert and Tibet Plateau are analyzed to verify the relationship between the AAI and the total column ozone, and the results show a remarkable correlation between the two. The conclusion cautions that using the AAI data of the spatial distribution of the total column must be considered in monitoring the absorbing aerosol process.关键词：total column ozone;Absorbing Aerosol Index;MODTRAN;FY-3;TOU- 285
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发布时间：2021-06-07 -
摘要：Earthquake-induced landslides are the most common geological disasters caused by large seismic activities in mountainous areas, and they are known for their suddenness, destructiveness, and extensive distribution range. These landslides often result in severe casualties and economic losses. Currently, regional earthquake-induced landslides are mainly obtained by visual interpretation and computer data extraction from remote sensing images. These methods are objective, time-consuming, and low in precision. Thus, they cannot address the requirement of practically conducting emergency surveys and disaster evaluations after earthquakes. In this study, with the main data source of high-resolution remote sensing images from ZY-3 and GF-1, as well as the study area of the Wenchuan earthquake region, objects of multilevel landslides were established using the multi-scale optimum partition method based on an in-depth analysis of seismic landslide features. A recognition rule set of multi-dimensional landslides was also built by combining topographic and image features, such as spectrum, texture, and geometry. Additionally, recognition models for landslide stratification were proposed based on the recognition models of high-resolution images and an understanding of the scenes. Through all of the preceding efforts mentioned, the spatial distribution of the seismic landslide, as well as the sliding source, transport, and depositional areas, can be identified. The analysis results of the experimental area showed a minimum recognition accuracy of 81.89%, with the depositional zone of landslides being the easiest zone to recognize, and the established method can be generalized. These findings may provide technical support for post-earthquake emergency investigations and further promote the application of high-resolution remote sensing data from Chinese satellites for landslides recognition.关键词：earthquake-induced landslides;landslide recognition;high-resolution remote sensing;Chinese satellite;Wenchuan earthquake- 505
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发布时间：2021-06-07 -
摘要：Timely accurate crop type identification and Crop Acreage Estimates (CAE) are essential for food security. Remote sensing technology has been successfully applied to crop identification because of its macro, rapid monitoring capabilities at large scales and its ability to quickly obtain accurate agricultural information. However, when identifying crop types, both simple and too many identifiable features might lead to low classification accuracies. Thus, multi-source and optimally selected features are obviously crucial to crop classification using remotely-sensed images. This paper considered a series of features, including multi-temporal spectra, vegetation indexes, textures, and band differences. Multiple experiments were designed and conducted in Sihong County, Jiangsu Province, China using Gaofen-1 and Huanjing-1 images to evaluate the influence of different features on the identification accuracy and determine the combination of preferred features which can improve the classification effect. The combination of random forest classification and univariate feature selection methods was expected to have a considerably positive effect on distinguishing and extracting the main crops in remote sensing images. In this study, the crop classification was implemented using random forests and univariate feature selection. The random forest method, which constructs many CART decision trees during each classification process, is one of themost effective classification methods. Univariate feature selection is a statistical testing method, which tests each feature to measure the relationship between the feature and the corresponding variable and then removes features that obtain low scores. First, the random forest classifier was applied to classify the images using the preceding multisource features mentioned. Second, we analyzed the contributions of different types of features or feature combinations to the classification accuracy. Third, features were selected by using the univariate feature selection method. Finally, we re-combined the optimal features and random forest to classify the image and distinguish the main crop types with high accuracy. The results showed that overall classification accuracy based on the combination of optimal features reached 97.07% with the corresponding Kappa coefficient being 0.96, which indicated that the feature selection method used in this paper has a considerably positive effect on high classification accuracy because it efficiently reduced feature dimension. The classification results also showed that the crop classification using multi-source features outperformed the one which only used spectral features. In addition, the accuracy of the experiment which simultaneously used spectral and VI features was the second highest among all experiments. The optimal feature combination has 19 features, including five spectral features, six vegetation indexes, seven band difference features, and 1 texture feature, which suggested that vegetation indexes and band differences were more important to the crop identification than the other two. This study demonstrated the following: (1) the addition of different types of features could improve classification accuracy; (2) too many features would decrease classification accuracies; (3) univariate feature selection was effective for choosing the optimal subset of features. The optimally selected features can be relatively beneficial to reduce the computation load and improve the worse accuracies caused by applied features blindly. Therefore, the combination of random forest and univariate feature selection is effective in improving classification accuracy and efficiency.关键词：univariate feature selection;spectrum feature;Vegetation Index (VI) feature;texture feature;band difference features- 356
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发布时间：2021-06-07 -
摘要：Aqueous minerals are either minerals that form in water or formations that are related to water. The type of aqueous mineral depends on temperature, salinity, PH, and composition of the parent rock at their forming time, which provides important clues to understanding the past aqueous environments of Mars, delineating advantageous regions for life activities, and even searching for possible existing Martian life. Therefore, studies on aqueous mineral identification and spatial distribution pattern are of considerable scientific significances. This paper provides a comprehensive overview of the advances of aqueous minerals detection on Martian surface since the 1990s. First, the major specifications of two data sources for mineral detection are introduced, including orbital spectrometers (TES, THEMIS, OMEGA, and CRISM) and in-situ landers/rovers (MPF/Sojourner, Spirit, Opportunity, Phoenix, and Curiosity). The spectrometers utilize either emission features in thermal infrared (TIR) or reflectance absorption characteristics in visible/near-infrared (VNIR) to identify and discriminate mineral types. Landers and rovers equipped with scientific instruments carry out the in-situ measurement to provide detailed component identification, abundance detection, and other analyses on the Martian surface soil, minerals, and rocks. Second, the spectral features, specific types, and distribution patterns of aqueous minerals detected on Martian surface are illustrated in detail as bound water, adsorbed water, and structural OH in aqueous minerals can be remotely detected via their unique spectral characteristics. Although aqueous minerals are widespread on Mars, they are concentrated in the Noachian southern highlands. At present, the aqueous minerals detected and verified on Martian surface from orbital spectrometers and landers/rovers can be classified as hydrous silicate minerals, sulfates, carbonates, chlorides, and perchlorates. Third, the major aqueous mineral quantitative retrieval methods, including absorption band depth conversion and spectral unmixing algorithms, are introduced. Compared with the linear unmixing model, the nonlinear spectral unmixing model can characterize mineral composition and retrieval mineral abundance with higher precision; therefore, it has been extensively used for the quantitative retrieval of aqueous mineral on Martian surface. The most commonly used nonlinear spectral unmixing models are the Hapke and Shkuratov models. The retrieval works provide fine quantitative data for inferring the formation and evolution history of aqueous mineral on Martian surface. However, numerous limitations exist, which are still important and difficult issues in aqueous mineral detection and retrieval via remote sensing. Furthermore, the retrieval advances at global and local scales, as well as their geological implications for Mars, are summarized. The detection of aqueous minerals confirms the existence of past water solution environment on Martian surface, and the phenomenon of diverse aqueous mineral categories that formed in various geomorphologies and geological contexts gradually changes over time reveals the evolution diversity of the chemical properties of water solution. Considering these two conditions, the paper finally proposes an analog study between Earth and Mars that should be carried out from the viewpoint of comparative planetology on the formation environment and process of aqueous minerals, which has important reference significance for the Mars exploration mission of China that is planned to be launched in 2020.关键词：Martian surface;aqueous mineral;remote sensing detection;in-situ detection;quantitative retrieval;comparative planetology- 290
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发布时间：2021-06-07 -
摘要：The rapid development of remote sensing technology provides an effective technical approach for humans to understand living environments and to utilize natural resources. Various remote sensing sensors exist, and the images formed by different image sensors have various characteristics, thereby resulting in multi-source remote sensing images, such as multi-spectral and panchromatic images. Fusing the multi-source remote sensing images of the same scene is necessary to efficiently and comprehensively deal with these image data. The multi-spectral image has high spectral resolution and rich spectral information; however, the spatial resolution of this image is low due to the limitations of physical devices. The panchromatic image has high spatial resolution and clear spatial detail; however, its spectral resolution is low. The fusion of multi-spectral and panchromatic images is the integration of the spatial detail information of the panchromatic image into the multi-spectral image to generate an image with high spatial resolution and spectral resolution, which benefit subsequent image processing. A method for the fusion of multi-spectral and panchromatic images using chaotic artificial bee colony optimization and improved Pulse Coupled Neural Network (PCNN) in a Non-Subsampled Shearlet Transform (NSST) domain is proposed. First, Intensity Hue Saturation (IHS) transform is performed on the multi-spectral image. The histogram of the panchromatic image is matched to the histogram of the intensity component of the multi-spectral image. The intensity component of the multispectral image and the new panchromatic image are then decomposed by NSST. Next, the low-frequency component is fused with the improved weighted fusion algorithm. Recently, artificial bee colony algorithm is one of the effective swarm intelligence optimization algorithms, which can adaptively determine the weighted coefficient. Chaotic bee colony optimization algorithm is designed by introducing the tent mapping chaotic sequence to avoid premature phenomena. The chaotic bee colony optimization algorithm has high convergence precision and rapid convergence speed in global optimization. By exploiting this property, the optimal improved weighted coefficient is determined by the chaotic artificial bee colony optimization algorithm. Mutual information is used as the fitness function. The improved PCNN method is adopted for the fusion of high-frequency components. Finally, the fused image is obtained by inverse NSST and inverse IHS transform. Many multi-spectral and panchromatic remote sensing images from LANDSAT TM, IKONOS, and SPOT 4 satellites are tested. Qualitative and quantitative evaluation results are obtained to verify the feasibility and effectiveness of the proposed method. The proposed method outperforms five other kinds of fusion methods: the IHS method, the method of Non-Subsampled Contourlet Transform (NSCT) combined with Non-negative Matrix Factorization (NMF),the method of NSCT combined with PCNN in the subjective visual effect, and the objective quantitative evaluation indexes, such as information entropy and spectral distortion. The proposed method can effectively preserve the spectral information of the multispectral image, while the details of panchromatic images are injected into the fused image as much as possible, effectively improving the spatial resolution of the fused image.关键词：image fusion;multispectral image and panchromatic image;non-subsampled shearlet transform;chaotic artificial bee colony optimization;improved pulse coupled neural network- 407
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发布时间：2021-06-07 -
摘要：High-precision inter-satellite baseline determination is essential for the distributed InSAR system. The reduced dynamic orbit determination method, which employs onboard GNSS measurements and the dynamical constraints, is most extensively used to generate a baseline solution between two formation-fly satellites in low-Earth orbits. However, the reference point of the dynamical model is the center-of-mass (CoM) of the satellite rather than the phase center of the SAR antenna. Owing to the effect of structural deformation and the inevitable formation-keeping maneuvers, the CoM of a satellite in orbit is usually different from the point calibrated on ground. Thus, the effect of CoM errors must be carefully considered in GNSS-based precision baseline determination; otherwise, CoM errors are likely to induce systematic errors in the spatial baseline solution of the InSAR formation. Simulations are carried out in this study to analyze and mitigate the effect of CoM errors on the InSAR baseline determination. First, a constant CoM error of 1 cm is added to the x-, y-, and z-directions of the satellite-fixed frame. Simulation results show that the effect of such 1 cm error in the x- and y-directions are extremely tiny and can be safely neglected. By contrast, the effect of CoM error in the z-direction is significant, and the results in a systematic variation in the baseline product. In view of this, we further propose two independent methods in GNSS-based InSAR baseline determination to mitigate the z-component CoM errors by adding the following: (1) constant empirical acceleration in the radial direction; (2) an offset parameter in the z-direction of the satellite-fixed frame. The first method is based on dynamical modeling and does not need any extra correction for CoM variations. In addition, the first method is suitable for the mitigation of the CoM errors caused by formation-keeping maneuvers. The second method is based on geometric modeling, and an online calibration for the CoM errors is necessary. Moreover, the second method is suitable for the CoM calibration at the beginning of the in-orbit validation phase. Both methods are proven effective according to the numerical experiments. The 3D RMS caused by the simulated 1 cm CoM errors in the satellite-fixed z-direction can be reduced from 8.8 mm to 0.13 mm and 0.09 mm, respectively. Over 98% of the CoM errors can be mitigated. Using our proposed methods, the systematic errors caused by the CoM offset in the baseline solution can be considerably mitigated. Compared with traditional in-orbit CoM calibration, the proposed method not only avoids the complex design for satellite attitude control but also improves the efficiency of the InSAR system. The current research is based on simulations. Further validation of real data will be carried out in future studies.关键词：Center-of-Mass errors;distributed InSAR;inter-satellite baseline determination;spaceborne GNSS;empirical acceleration- 255
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发布时间：2021-06-07 -
摘要：Multi-scale segmentation is the key step of analysis in remotely-sensed imagery. Scale parameter selection in the segmentation process is directly related to the quality and accuracy of object-oriented analysis. Only on the basis of experience for segmentation parameter choice that has less quantitative analysis ways, currently. These methods lack quantitative estimation before segmentation, with considerable workloads and low efficiency. From the perspective of scientific research, the scientificity and universality of these methods are poor. The objective of this paper is to use the quantitative method to determine the scale parameterin order to realize the automatic extracting target on the object-oriented analysis. This paper summarizes the concept of scale parameter in the object-oriented analysis. The commonly used segmentation scale parameters into spatial and attribute bandwidths were also analyzed. This paper used a spatial and spectral statistics-based scale parameter selection method for object-based information extraction from high spatial resolution remote sensing images. The relationship between Fractal Net Evolution Approach (FNEA) in multi-scale segmentation and spatial statistical characteristics was analyzed. Scale estimation based on spatial and spectral statistical characteristics was applied to FNEA in multi-scale segmentation. Meanwhile, the scale estimation approach proposed in this paper was verified by high spatial resolution image, namely IKONOS and SPOT 5 data. Construction and farmland areas were selected for spatial and spectral statistical characteristics, respectively, to further estimate the optimal scale parameters in segmentation. A series of supervised classification was performed to verify the reasonability of the predicted optimal scale parameter. The classification and accuracy assessment results show that the estimated scale by spatial statistical characteristics is basically close to the optimal one in the FNEA-based multi-scale segmentation. The proposed scale estimation approach can ensure the accuracy of the following object-oriented image classification. The method can be used to estimate the appropriate scale parameters before segmentation. In addition, it is an essentially data-driven method that requires nearly no prior knowledge; thus, it can enhance the efficiency and automatic degree of object-based image analysis.关键词：object oriented image analysis;image segmentation;scale estimation;scale selection;spatial statistics;fractal net evolution approach- 306
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发布时间：2021-06-07 -
摘要：High resolution is an important direction for the development of Synthetic Aperture Radar (SAR). Conventional SAR imaging algorithms based on matched filtering is limited by the signal bandwidth and the synthetic aperture length. Compressive Sensing (CS) theory has recently attracted the attention of domestic and foreign scholars. Electromagnetic scattering signals of man-made objects, such as vehicles and buildings, have obvious sparse characteristics, and the total electromagnetic scattering of the target can be approximated by the synthesis of the center scattering of the local scattering. Studies have shown that CS has considerable potential in improving the quality of radar imaging and high precision imaging of man-made targets. However, the application of CS theory in radar imaging is novel; therefore, it has some problems that require further study. Studies have shown that the regularization and shrinkage parameter have an obvious impact on the performance of radar sparse imaging. Traditional parameter selection methods have serious disadvantages in computation cost and accuracy or are limited to a few specific models. Therefore, proposing a parameter selection method is of great importance. A fully automated algorithm is proposed in this paper to improve the performance of radar sparse imaging in the case of parameter optimization and to overcome the preceding disadvantages mentioned. First, combined with the Maximum A Posteriori (MAP) estimation and Bayesian inference, we obtain the relationship between regularization and shrinkage parameter. The value of regularization parameter is determined by the choices of shrinkage parameter, noise, and signal variance, thereby reducing the dimension of the parameter selection problem. Second, we propose a radar sparse imaging algorithm which can solve the joint optimization problem to simultaneously achieve model parameters estimation and SAR imaging. In each iterative of the approach, all required parameters are updated with training data without the necessary prior information, and the image is reconstructed by regularization technique with the updated observation model constructed by new parameters. Extensive comparisons are carried out between the proposed method and several other competing methods based on simulations and real-data processes. The experimental results demonstrate that the proposed method can achieve accurate parameter estimation and imaging performance with low computational complexity. Compared with the traditional imaging method, the imaging results of this method are sparse, thereby reducing the sidelobes and maintaining cleanliness without noise. This paper presents a fully automated parameter selection method based on maximum a posteriori estimation and Bayesian inference without extra prior information. All required parameters can be obtained through known data. We deduce the relationship between model parameters and signal and noise variance; thus, a series of training processes has been avoided, and the computational cost has been considerably reduced. Simulations and realdata experiment demonstrate that the proposed method can achieve accurate parameter selection with lower computational cost than other parameter selection methods, such as Bayesian information criteria and L-curve.关键词：sparse imaging;synthetic aperture radar;parameter selection;Bayesian inference- 335
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发布时间：2021-06-07 -
摘要：Hyperspectral remote sensing image classification is one of the enormous challenges in the field of applied remote sensing. Traditionally, supervised methods, such as Support Vector Machine (SVM), dominate this area. Especially, Conditional Random Field (CRF) excels in solving this kind of problem in most cases, due to its prominent ability in formulating the spatial relationship. However, CRF suffers from the availability of large amount of labeled samples, which is labor- and time-consuming to obtain in practice. The accuracy tends to decrease dramatically once labeled samples are not adequate or informative enough. To solve the above problem, a semi-supervised CRF model is proposed in this paper. In the semi-supervised CRF model, the association potential is defined as the spatio-spectral Laplacian Support Vector Machine (ssLapSVM), to exploit the information contained in the unlabeled samples. And the multi-class probability for each sample is obtained by the ssLapSVM with the one-versus-one scheme. In addition, the interaction potential is newly designed by introducing a weight into the Potts model. Note that, in the classification of hyperspectral remote sensing with limited labeled samples, unlabeled neighbors of one labeled samples may often exist. Thus, the labels of these unlabeled neighbors are assigned based on maximum probability acquired by the ssLapSVM, and use the maximum probability as a weight. In the training phrase, the optimal parameters in the association potential, i.e. ssLapSVM, is firstly trained, and then the whole semi-supervised CRF model is trained to get the optimal parameters in the interaction potential. In the inference phrase, mean-field is adopted to find the optimal label configuration over the testing set. The performance of the proposed semi-supervised CRF model is evaluated on two well-known benchmarks, i.e. Indian Pines scene and Pavia University scene. The objective comparison experiments are carried out among some state-of-the-art methods in terms of kappa statistic. On both Indian Pines (IP) scene and Pavia University (PU) scene, the proposed method can exhibits completely better performance, improve by 4.94%@IP and 3.28%@PU, respectively. In addition, the kappa of the proposed method rises with the increase of the number of labeled training samples. And in most cases, the proposed method shows better performance than other contrast methods under the case of the same training labeled samples. With the increase of trade-off coefficient, kappa statistic rise first and tend to steady, and then degrades dramatically. The proposed method also shows better performance when the larger scope of samples participating in the construction of the interaction potential. In this paper, we have developed a semi-supervised CRF to address the problem of hyperspectral image classification. Our method can effectively improve the kappa statistic under limited labeled-training set by newly designed association and interaction potential. Experiments conducted on two well-known hyperspectral datasets demonstrate the effectiveness of the proposed method. And when compared to related semi-supervised algorithms, the proposed method shows its superiority.关键词：hyperspectral;remote sensing;classification;semi-supervised;conditional random field- 390
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发布时间：2021-06-07 -
摘要：Shadow of the remote sensing image is widespread in China southwest hilly area, which has affected the effects of automatic recognition of image computer and quantitative analysis. Topographic correction methods, which are widely used to adjust for differences in solar incidence angles, can partly alleviate the impacts of shadows. But the model based on DEM data has limitations and errors in application, resulting to the scattered and discontinuous images of the terrain correction. In order to overcome the shortcomings of the topographic correction models based on DEM and improve the quality of remote sensing image of southwest hills, the article introduced a new shadow correction methods based on the similar spectral information after continuum removed. The advantage of the method is that it no longer depend on DEM and semi-empirical estimation value, which can maximize the computer automatic identification and the independent access for parameters. Throughout these shadow correction methods, they are based on weak information in the shadow area, and the shadow correction is achieved by establishing a relationship between shadow and non-shadow. As to shadow pixel and non-shadow pixel with the same surface land cover types, their spectral curve is similar and only brightness (continuum information) is different. In order to establish relationship of spectral statistic feature between shadow pixel and non-shadow pixel, the spectral information is recovered by the similar pixel of shadow pixel and by the principle of using surface features spectrum envelope line to remove the continuum line. The article introduced a new shadow correction methods based on the similar spectral information after continuum removed, and experiment it on Landsat 8 OLI image by shadow extraction, continuum removing, searching for similar pixel, shadow information restoration. And the calibration accuracy was tested by visual evaluation, statistical analysis, comparative verification and automatic classification. By comparing the images of C-method correction and CR-method correction, the visual feature of the two images tend to be flat, and the image details of shadow area tend to be obvious. After CR-method shadow correction, the pixel brightness value of shadow area gets a more complete compensation, and the image brightness converges with the shadow area, and it makes the shadow area visual characteristics more consistent with the near shadow area. At the same time, the standard deviation of the image tend to drop after CR-method correction, making the pixel brightness value of the slopes closer. CR-method is better than C-method for terrain shadow elimination and slope and luminance values of homogenization, which relative root mean square error(rRMSE) of sample point in per land use cover type is within 2.919% compared with unshaded pixels, and the minimum is only 0.516%. the automatic classification accuracy of CR-method correction, calculating the number of right and wrong pixels, has increased from 43.59% to 61.57%. The experiment of CR-method shadow correction in complex hilly terrain region has achieved good effect, and has improved the quality of remote sensing image with mountain shadow.关键词：shadow correction;continuum removal;Information enhancement;Landsat 8 OLI image;China Southwest Hilly region- 246
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发布时间：2021-06-07 -
摘要：Synthetic Aperture Radar (SAR) images have region homogeneity with gray and texture. Considering that SRM (Statistical Region Merging) algorithms of image segmentation are efficient, stable and robust against noise, we propose a novel change detection method based on cascade segmentation with Dynamic Sorting Statistical Region Merging (DSSRM) algorithm. Firstly a DSSRM algorithm based on dynamic sorting is proposed to overcome conventional SRM's over-segmentation problem caused by single feature and static sorting. This algorithm takes the Manhattan distance of multi-feature of regions to be merged as the sorting criterion, and updates the adjacency matrix after each merging. Secondly based on the rule of minimizing mutual information we design a multi-channel complementary appearance model to improve the capability of constraint for region merging. Finally we present a cascade change detection framework with multiple levels. The first level projects difference image to super-pixel space via SRM, the second level utilizes DSSRM to dynamically merge regions; and the third level leverages a simplified SRM to realize region merging again to obtain final change detection map. Experimental results of the proposed method and proposed methods based on PCA and MRF are presented. By analysis and quantitative comparisons, the false alarm number and total of error number by DSSRMare decreased thereby the performance of KAPPA can get higher than methods based on PCA and MRF. DSSRM method is based on dynamic sorting algorithm with Manhattan distance of multi-feature of regions, it makes similar regions to be merged firstly. Experiments on construction of multi-channel illustrates that the more is the difference between channels the better is the performance of change detection. Our method improved the performance of SRM algorithm to avoid the over-segmentation phenomenon. Comparison experiments show that this method can obtain better performance of change detection than conventional SRM and state of art algorithms.关键词：SAR image;change detection;MDSSRM;cascade segmentation- 267
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发布时间：2021-06-07 -
摘要：Lake is an important water resource and a sensitive indicator of climate and environment change. Satellite altimetry has been used as an alternative tool to measure lake levels since the 1990s. With the development of satellite altimetry technology, different altimetry thatcan be used for lake level monitoring has been launched. This paper aims to verify Cryosat-2/SIRAL data capabilities of monitoring lake level, improve the extraction accuracy of lake level changes, and monitor the water level change of Qinghai Lake. The boundary of the lake was first extracted using the image of MODIS13Q1 close to the date altimeter visited to ensure the observation points in the lake. This study used six kinds of algorithms to retrack Cryosat-2/SIRAL LRM level 1 data in order to extract the Qinghai Lake water levels from 2010 to 2015, including the primary peak Offset Center of Gravity (OCOG), primary peak threshold, primary peak 5-β parameter, traditional OCOG, traditional threshold, and traditional 5-β parameter methods. Furthermore, the Cryosat-2/SIRAL GDRs of LRM mode provides three different retrackers: UCL, refined CFI, and refined OCOG. The accuracy of all these different algorithms in extracting water level was then compared with the measured water level of the hydrological station using the indexes, such as the difference, correlation coefficient, and root mean square error (RMSE). The 2002 to 2015 water level time series of Qinghai Lake was obtained and combined with the Envisat/RA-2 GDR data by adding the differences between the lake levels extracted from Envisat/RA-2 and Cryosat-2/SIRAL. The seasonal and inter-annual variation features of Qinghai Lake water level were then analyzed. The results showed that the primary peak 5-β parameter retracker for Qinghai Lake performed the best with the least RMSE 0.093 m and a maximum correlation coefficient (0.956) among these retrackers. Generally, the water level extraction accuracy of the retrackers based on the primary peak is better than the retrackers based on the entire waveform. While for these waveforms which are influenced by land echo information, the primary peak OCOG algorithm and primary peak threshold algorithm presented were better than others. Comparing the three kinds of Cryosat-2/SIRAL GDR products for LRM patterns, the data based on the refined OCOG algorithm was more suitable for extraction of lake level. In addition, the water level of Qinghai Lake generally rose from 2002 to 2015 with the overall increasing trend of 0.112 m/a, with marked seasonal changes in a year. The water level began to rise in May and December each year, with respectively high peaks in September and January. Based on the preceding experiments and analysis, the Cryosat-2/SIRAL LRM data can be used to extract lake levels with high precision at approximately 1 dm. Retracking for altimetry level 1b data could improve the water level extraction accuracy. The best adaptive retracking algorithm for different types of lakes is often different because they show different echo waveforms. The analysis in the paper is rough; hence, the next step is selecting different types of lakes to obtain a detailed comparative analysis.关键词：satellite altimetry;Cryosat-2;Envisat/RA-2;retracking;lake level;Qinghai Lake- 399
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发布时间：2021-06-07 - 摘要：Multi-baseline InSAR techniques have demonstrated their great potential in topographic mapping and ground surface deformation monitoring. In order to minimize the decorrelation noise between stacked SAR images in multi-baseline InSAR processes, the phase reconstruction technique has been developed recently and has become one of the hotspot techniques in radar interferometry. Due to budget limitations and unstable SAR image acquisition frequency, a lot of multi-baseline InSAR applications have to be carried out based on small image datasets. Researchers have made every endeavor to address this problem, some targeted multi-baseline InSAR processing strategies have been therefore developed. Unfortunately, there are few literatures discussing the application of phase reconstruction to small image datasets at this stage. This paper aims to evaluate the effectiveness of the phase reconstruction technique on a small SAR image datasets. A targeted multi-baseline InSAR processing scheme was designed and applied to real data. The main idea of phase reconstruction technique is to reform phase observations along a SAR stack by taking advantage of a maximum likelihood estimator which is defined on the coherence matrix estimated from each target. The proposed multi-baseline InSAR processing scheme is divided into two modules. The first one is named as " pre-processing module”, which generates the zero-baseline SAR image stack required by the phase reconstruction technique via a series of operations including image coregistration, topographic phase component removal, and so on. The second one firstly constructs coherence matrices based on multilooked pixels, thereby conducting phase reconstruction operations. Subsequently, it isolates ground surface deformation signals based on reconstructed phase observations by taking advantage of the small baseline subset technique. Noted that an atmospheric disturbances estimation and removal step was involved in this module in order to assure the reliability of the output measurements. The proposed scheme is subsequently applied to five PALSAR images acquired over Taiyuan, ShanXi Province, China. During the experimental process, the performance of the phase reconstruction technique in the case of small image subsets was analyzed in different aspects (e.g. the signal-to-noise ratio of the FFT based orbital fringe estimation process, the number of residues contained in phase unwrapping networks). The corresponding annual deformation rate field was presented, as well as the distribution of additional points obtained after the application of the phase reconstruction technique. The results has demonstrated that the phase reconstruction technique can effectively improve interferometric coherence even in the case of small image datasets, which is beneficial to the proliferation of the density of multi-baseline InSAR results. It must be noted that the size of the coherence matrix is relatively small in the case of small image datasets. Thus the precision of the phase reconstruction results is likely to be influenced by the low coherent elements of coherence matrix. In order to make the phase reconstruction technique work with small image datasets better, future works should try to eliminate the negative impacts of low-quality elements on the phase reconstruction procedure.关键词：phase reconstruction;multi-baseline InSAR;small datasets;interferometric coherence;synthetic aperture radar
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发布时间：2021-06-07
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