浏览全部资源
扫码关注微信
Published： 2018-9 ，
Accepted： 29 May 2018
扫 描 看 全 文
钱晓亮, 李佳, 程塨, 姚西文, 赵素娜, 陈宜滨, 姜利英. 2018. 特征提取策略对高分辨率遥感图像场景分类性能影响的评估. 遥感学报, 22(5): 758–776
Qian X L, Li J, Cheng G, Yao X W, Zhao S N, Chen Y B and Jiang L Y. 2018. Evaluation of the effect of feature extraction strategy on the performance of high-resolution remote sensing image scene classification. Journal of Remote Sensing, 22(5): 758–776
高分辨率遥感图像场景分类方法主要涉及两个环节：特征提取以及特征分类，分类器的设计已经相对成熟，当前工作的重点是特征提取策略的研究。为了进一步推动特征提取策略的研究，将特征提取策略对高分辨率遥感图像场景分类性能的影响进行了定性和定量评估。首先，回顾了高分辨率遥感图像场景分类的发展历程；然后，对现有高分辨率遥感图像场景分类方法的特征提取策略进行分类总结，并从理论上将各类特征提取策略对场景分类性能的影响进行定性评估；最后，在3个规模较大的数据集上对多种特征提取策略进行实验对比，将不同特征提取策略对场景分类性能的影响和各数据集的复杂度进行定量评估。
Remote sensing image scene classification aims to tag remote sensing images with semantic categories according to the content of the image and is important in disaster monitoring
environmental detection
and urban planning. Scene classification results can provide valuable information about object recognition and image retrieval and can effectively improve the performance of image interpretation. The general process of remote sensing image scene classification mainly consists of feature extraction and scene classification based on image features. Given that the design of classifiers is relatively mature
this work focuses on feature extraction strategy. The influence of various strategies on the performance of scene classification is short of unified evaluation
which limits its development. The effect of various feature extraction strategies on the performance of high-resolution remote sensing image scene classification is evaluated in this study. In the second section of this paper
existing feature extraction strategies are divided into two categories: (1) hand-designed and (2) data-driven feature extraction. Hand-designed features
such as Color Histograms (CH) and Scale Invariant Feature Transform (SIFT)
provide the primary description of images and are presented in the early period. Further abstract description of the images is introduced by coding of hand-designed features
such as Bag of Visual Words (BoVW) and has higher classification accuracy than hand-designed features. However
these feature extraction strategies generally suffer from poor generalization capability due to specific requirements for designing. Furthermore
hand-designed features require significant domain knowledge. By contrast
data-driven features can directly learn powerful features from a large number of sample images and are generally divided into shallow and deep learning features. Shallow learning feature extraction mainly involves Principal Component Analysis (PCA)
Independent Component Analysis (ICA)
and sparse coding algorithms. Typical deep learning feature extraction strategies include stacked autoencoder (SAE)
Deep Belief Network (DBN)
and Convolutional Neural Network (CNN). Compared with deep learning models
shallow learning models can be regarded as a neural network with a single hidden layer and thus cannot capture high-level semantic features. The superiority of deep learning features is obvious when dealing with complex scene classification. Furthermore
CNN-based features exhibit improved performance compared with SAE- and DBN-based features because the one-dimensional structure of SAE and DBN destroys the spatial information of images. In the third section of this paper
29 feature descriptors are quantitatively compared in UC Merced
AID
and NWPU RESISC-45 datasets and eight combinations of feature descriptors are quantitatively compared in the NWPU RESISC-45 dataset. The effect of different feature extraction strategies on the performance of scene classification and the complexity of each dataset are evaluated through quantitative comparison. The experimental results are as follows. (1) The classification accuracy and stability of hand-designed features is poor
however the efficiency of most features is satisfactory and can attain better performance by combining with other types of features. (2) Among all feature extraction strategies
the coding of hand-designed features possesses moderate levels of classification accuracy
efficiency
and stability. (3) The classification accuracy and stability of data-driven features are best
but most of them have low efficiency. (4) AlexNet
a deep learning model with few layers
exhibits the best comprehensive performance and is suitable for occasions that require high classification accuracy
efficiency
and stability. (5) Some scene classes belonging to land use type are easy to be confused because of similar landmark buildings or sites. Moreover
some scene classes belonging to land cover type are easy to be confused because of their similar geomorphologic features. (6) The recently proposed NWPU RESISC-45 dataset is more complex than the other datasets and is more challenging for scene classification algorithms. Finally
the summary and conclusion of this paper are presented
and the discussion of future development is provided. On the one hand
combining prior knowledge introduced by hand-designed features with the CNN model may be one of the future development directions. On the other hand
introducing Generative Adversarial Networks (GAN) into CNN training may be a research hotspot in the future. In addition
remote sensing parameters
such as NDVI and NDWI
and multi-spectral information can be integrated with current feature extraction strategies for practical applications.
高分辨率场景分类特征提取策略手工特征数据驱动特征深度学习
high-resolutionscene classificationfeature extraction strategyhand-designed featuresdata driven featuresdeep learning
Aptoula E. 2014. Remote sensing image retrieval with global morphological texture descriptors. IEEE Transactions on Geoscience and Remote Sensing, 52(5): 3023–3034
Avramović A and Risojević V. 2016. Block-based semantic classification of high-resolution multispectral aerial images. Signal, Image and Video Processing, 10(1): 75–84
Bengio Y, Lamblin P, Popovici D and Larochelle H. 2007. Greedy layer-wise training of deep networks//Proceedings of the 20th Annual Conference on Neural Information Processing Systems. Vancouver, British Columbia, Canada: MIT Press: 153–160
Bhagavathy S and Manjunath B S. 2006. Modeling and detection of geospatial objects using texture motifs. IEEE Transactions on Geoscience and Remote Sensing, 44(12): 3706–3715
Blaschke T. 2003. Object-based contextual image classification built on image segmentation//Proceedings of 2003 IEEE Workshop on Advances in Techniques for Analysis of Remotely Sensed Data. Greenbelt, MD, USA, USA: IEEE: 113–119 [DOI: 10.1109/WARSD.2003.1295182]
Blaschke T. 2010. Object based image analysis for remote sensing. ISPRS Journal of Photogrammetry and Remote Sensing, 65(1): 2–16
Blei D M, Ng A Y and Jordan M I. 2003. Latent dirichlet allocation. Journal of Machine Learning Research, 3: 993–1022
Bosch A, Zisserman A and Muñoz X. 2006. Scene classification via pLSA//Proceedings of the 9th European Conference on Computer Vision. Graz, Austria: Springer: 517–530 [DOI: 10.1007/11744085_40]
Chaib S, Gu Y F and Yao H X. 2016a. An informative feature selection method based on sparse pca for vhr scene classification. IEEE Geoscience and Remote Sensing Letters, 13(2): 147–151
Chaib S, Gu Y F, Yao H X and Zhao S C. 2016b. A VHR scene classification method integrating sparse PCA and saliency computing//Proceedings of 2016 IEEE International Geoscience and Remote Sensing Symposium. Beijing, China: IEEE: 2742–2745 [DOI: 10.1109/IGARSS.2016.7729708]
Chen G Y, Li X and Liu L. 2016. A study on the recognition and classification method of high resolution remote sensing image based on deep belief network//Gong M G, Pan L Q, Song T and Zhang G X, eds. Bio-Inspired Computing - Theories and Applications. Singapore: Springer: 362–370 [DOI: 10.1007/978-981-10-3611-8_29]
Chen L J, Yang W, Xu K and Xu T. 2011. Evaluation of local features for scene classification using VHR satellite images//Proceedings of 2011 Joint Urban Remote Sensing Event. Munich, Germany: IEEE: 385–388 [DOI: 10.1109/JURSE.2011.5764800]
Chen Y S, Zhao X and Jia X P. 2015. Spectral–spatial classification of hyperspectral data based on deep belief network. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8(6): 2381–2392
Cheng G, Han J W and Lu X Q. 2017a. Remote sensing image scene classification: benchmark and state of the art. Proceedings of the IEEE, 105(10): 1865–1883
Cheng G, Li Z P, Yao X W, Guo L and Wei Z L. 2017b. Remote sensing image scene classification using bag of convolutional features. IEEE Geoscience and Remote Sensing Letters, 14(10): 1735–1739
Cheriyadat A M. 2014. Unsupervised feature learning for aerial scene classification. IEEE Transactions on Geoscience and Remote Sensing, 52(1): 439–451
Comon P. 1994. Independent component analysis, a new concept?. Signal Processing, 36(3): 287–314
Cousin A, Forni O, Maurice S, Lasue J, Gasnault O and Wiens R. 2011. Independent component analysis classification for chemcam remote sensing data//Proceedings of the 42nd Lunar and Planetary Science Conference. The Woodlands, Texas: [s.n.]
Dai D X and Yang W. 2011. Satellite image classification via two-layer sparse coding with biased image representation. IEEE Geoscience and Remote Sensing Letters, 8(1): 173–176
Dell’Acqua F, Gamba P, Ferrari A, Palmason J A, Benediktsson J A and Arnason K. 2004. Exploiting spectral and spatial information in hyperspectral urban data with high resolution. IEEE Geoscience and Remote Sensing Letters, 1(4): 322–326
dos Santos J A, Penatti O A B and da Silva Torres R. 2010. Evaluating the potential of texture and color descriptors for remote sensing image retrieval and classification//International Conference on Computer Vision Theory and Applications. Angers, France: [s.n.]: 203–208
Du B, Xiong W, Wu J, Zhang L F, Zhang L P and Tao D C. 2017. Stacked convolutional denoising auto-encoders for feature representation. IEEE Transactions on Cybernetics, 47(4): 1017–1027
Du Q, Kopriva I and Szu H H. 2006. Independent-component analysis for hyperspectral remote sensing imagery classification. Optical Engineering, 45(1): 017008
Fan R E, Chang K W, Hsieh C J, Wang X R and Lin C J. 2008. Liblinear: a library for large linear classification. Journal of Machine Learning Research, 9: 1871–1874
Gao Y, Mas J F, Maathuis B H P, Zhang X M and Dijk P M V. 2006. Comparison of pixel-based and object-oriented image classification approaches—a case study in a coal fire area, wuda, inner mongolia, China. International Journal of Remote Sensing, 27(18): 4039–4055
Gong Z Q, Zhong P, Yu Y and Hu W D. 2018. Diversity-promoting deep structural metric learning for remote sensing scene classification. IEEE Transactions on Geoscience and Remote Sensing, 56(1): 371–390
Guo X, Huang X, Zhang L F, Zhang L P, Plaza A and Benediktsson J A. 2016. Support tensor machines for classification of hyperspectral remote sensing imagery. IEEE Transactions on Geoscience and Remote Sensing, 54(6): 3248–3264
Han J W, Zhou P C, Zhang D W, Cheng G, Guo L, Liu Z B, Bu S H and Wu J. 2014. Efficient, simultaneous detection of multi-class geospatial targets based on visual saliency modeling and discriminative learning of sparse coding. ISPRS Journal of Photogrammetry and Remote Sensing, 89: 37–48
Han W, Feng R Y, Wang L Z and Cheng Y F. 2017. A semi-supervised generative framework with deep learning features for high-resolution remote sensing image scene classification. ISPRS Journal of Photogrammetry and Remote Sensing [DOI: 10.1016/ j.isprsjprs.2017.11.004]
Haralick R M, Shanmugam K and Dinstein I H. 1973. Textural features for image classification. IEEE Transactions on Systems, Man, and Cybernetics, SMC-3(6): 610–621
He K M, Zhang X Y, Ren S Q and Sun J. 2014. Spatial pyramid pooling in deep convolutional networks for visual recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 37(9): 1904–1916
He K M, Zhang X Y, Ren S Q and Sun J. 2016. Deep residual learning for image recognition//Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas, NV, USA: IEEE: 770–778 [DOI: 10.1109/CVPR.2016.90]
Hinton G E, Osindero S and Teh Y W. 2006. A fast learning algorithm for deep belief nets. Neural Computation, 18(7): 1527–1554
Hinton G E and Salakhutdinov R R. 2006. Reducing the dimensionality of data with neural networks. Science, 313(5786): 504–507
Hou B, Luo X H, Wang S, Jiao L C and Zhang X R. 2015. Polarimetric SAR images classification using deep belief networks with learning features//Proceedings of 2015 IEEE International Geoscience and Remote Sensing Symposium. Milan, Italy: IEEE: 2366–2369 [DOI: 10.1109/IGARSS.2015.7326284]
Hu F, Xia G S, Hu J W and Zhang L P. 2015a. Transferring deep convolutional neural networks for the scene classification of high-resolution remote sensing imagery. Remote Sensing, 7(11): 14680–14707
Hu J W, Jiang T B, Tong X Y, Xia G S and Zhang L P. 2015b. A benchmark for scene classification of high spatial resolution remote sensing imagery//Proceedings of 2015 IEEE International Geoscience and Remote Sensing Symposium. Milan, Italy: IEEE: 5003–5006 [DOI: 10.1109/IGARSS.2015.7326956]
Hu J W, Xia G S, Hu F, Sun H and Zhang L P. 2015c. A comparative study of sampling analysis in scene classification of high-resolution remote sensing imagery//Proceedings of 2015 IEEE International Geoscience and Remote Sensing Symposium. Milan, Italy: IEEE: 14988–15013 [DOI: 10.1109/IGARSS.2015.7326290]
Jégou H, Perronnin F, Douze M, Sánchez J, Pérez P and Schmid C. 2012. Aggregating local image descriptors into compact codes. IEEE Transactions on Pattern Analysis and Machine Intelligence, 34(9): 1704–1716
Jain A K, Ratha N K and Lakshmanan S. 1997. Object detection using gabor filters. Pattern Recognition, 30(2): 295–309
Jia Y Q, Shelhamer E, Donahue J, Karayev S, Long J, Girshick R, Guadarrama S and Darrell T. 2014. Caffe: convolutional architecture for fast feature embedding//Proceedings of the 22nd ACM International Conference on Multimedia. Orlando, Florida, USA: ACM: 675–678 [DOI: 10.1145/2647868.2654889]
Jolliffe I T. 2005. Pincipal component analysis. Journal of Marketing Research, 87(100): 513
Karoui M S, Deville Y, Hosseini S, Ouamri A and Ducrot D. 2009. Improvement of remote sensing multispectral image classification by using independent component analysis//Proceedings of the 1st Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing. Grenoble, France: IEEE: 1–4 [DOI: 10.1109/WHISPERS.2009.5289033]
Kasapoglu N G and Ersoy O K. 2007. Border vector detection and adaptation for classification of multispectral and hyperspectral remote sensing images. IEEE Transactions on Geoscience and Remote Sensing, 45(12): 3880–3893
Krizhevsky A, Sutskever I and Hinton G E. 2012. Imagenet classification with deep convolutional neural networks// Proceedings of the 25th International Conference on Neural Information Processing Systems. Lake Tahoe, Nevada: ACM: 1097–1105
Lazebnik S, Schmid C and Ponce J. 2006. Beyond bags of features: spatial pyramid matching for recognizing natural scene categories//Proceedings of 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. New York, NY, USA: IEEE: 2169–2178 [DOI: 10.1109/CVPR.2006.68]
Li F F and Perona P. 2005. A Bayesian hierarchical model for learning natural scene categories//Proceedings of 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. San Diego, CA, USA: IEEE: 524–531 [DOI: 10.1109/CVPR.2005.16]
Li H T, Gu H Y, Han Y S and Yang J H. 2010. Object-oriented classification of high-resolution remote sensing imagery based on an improved colour structure code and a support vector machine. International Journal of Remote Sensing, 31(6): 1453–1470
Liu Q S, Hang R L, Song H H and Li Z. 2018. Learning multiscale deep features for high-resolution satellite image scene classification. IEEE Transactions on Geoscience and Remote Sensing, 56(1): 117–126
Liu Y L, Zhang Y M, Zhang X Y and Liu C L. 2016. Adaptive spatial pooling for image classification. Pattern Recognition, 55(C): 58–67
Lowe D G. 2004. Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, 60(2): 91–110
Luo B, Jiang S J and Zhang L P. 2013. Indexing of remote sensing images with different resolutions by multiple features. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 6(4): 1899–1912
Luus F P S, Salmon B P, van den Bergh F and Maharaj B T J. 2015. Multiview deep learning for land-use classification. IEEE Geoscience and Remote Sensing Letters, 12(12): 2448–2452
Mekhalfi M L, Melgani F, Bazi Y and Alajlan N. 2015. Land-use classification with compressive sensing multifeature fusion. IEEE Geoscience and Remote Sensing Letters, 12(10): 2155–2159
Negrel R, Picard D and Gosselin P H. 2014. Evaluation of second-order visual features for land-use classification//Proceedings of the 12th International Workshop on Content-Based Multimedia Indexing. Klagenfurt, Austria: IEEE: 1–5 [DOI: 10.1109/CBMI.2014.6849835]
Newsam S, Wang L, Bhagavathy S and Manjunath B S. 2004. Using texture to analyze and manage large collections of remote sensed image and video data. Applied Optics, 43(2): 210–217
Ojala T, Pietikäinen M and Harwood I. 1996. A comparative study of texture measures with classification based on featured distributions. Pattern Recognition, 29(1): 51–59
Ojala T, Pietikäinen M and Mäenpää T. 2002. Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(7): 971–987
Oliva A and Torralba A. 2001. Modeling the shape of the scene: a holistic representation of the spatial envelope. International Journal of Computer Vision, 42(3): 145–175
Olshausen B A and Field D J. 1997. Sparse coding with an overcomplete basis set: a strategy employed by V1? Vision Research, 37(23): 3311–3325 [DOI: 10.1016/S0042-6989(97)00169-7]
Penatti O A B, Nogueira K and dos Santos J A. 2015. Do deep features generalize from everyday objects to remote sensing and aerial scenes domains?//Proceedings of 2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops. Boston, MA, USA: IEEE: 44–51 [DOI: 10.1109/CVPRW.2015.7301382]
Perronnin F, Sánchez J and Mensink T. 2010. Improving the fisher kernel for large-scale image classification//Proceedings of the 11th European Conference on Computer Vision. Crete, Greece: Springer: 143–156 [DOI: 10.1007/978-3-642-15561-1_11]
祁昆仑. 2017. 基于视觉特征的高分辨率光学遥感影像多任务分类研究. 测绘学报, 46(6): 802
Qi K L. 2017. Multi-task classification of high resolution optic remote sensing images based on visual features. Acta Geodaeticaet Cartographica Sinica, 46(6): 802 (
Risojević V, Momić S and Babić Z. 2011. Gabor descriptors for aerial image classification//Proceedings of the 10th International Conference on Adaptive and Natural Computing Algorithms. Ljubljana, Slovenia: Springer: 51–60 [DOI: 10.1007/978-3-642-20267-4_6]
Sermanet P, Eigen D, Zhang X, Mathieu M, Fergus R and LeCun Y. 2014. Overfeat: integrated recognition, localization and detection using convolutional networks. arXiv preprint arXiv:1312.6229
Sheng G F, Yang W, Xu T and Sun H. 2012. High-resolution satellite scene classification using a sparse coding based multiple feature combination. International Journal of Remote Sensing, 33(8): 2395–2412
Shyu C R, Klaric M, Scott G J, Barb A S, Davis C H and Palaniappan K. 2007. Geoiris: geospatial information retrieval and indexing system—content mining, semantics modeling, and complex queries. IEEE Transactions on Geoscience and Remote Sensing, 45(4): 839–852
Simonyan K and Zisserman A. 2014. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556
Sridharan H and Cheriyadat A. 2015. Bag of lines (bol) for improved aerial scene representation. IEEE Geoscience and Remote Sensing Letters, 12(3): 676–680
Swain M J and Ballard D H. 1991. Color indexing. International Journal of Computer Vision, 7(1): 11–32
Szegedy C, Liu W, Jia Y Q, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V and Rabinovich A. 2015. Going deeper with convolutions//Proceedings of 2015 IEEE Conference on Computer Vision and Pattern Recognition. Boston, MA, USA: IEEE: 1–9 [DOI: 10.1109/CVPR.2015.7298594]
Tuia D, Ratle F, Pacifici F, Kanevski M F and Emery W J. 2009. Active learning methods for remote sensing image classification. IEEE Transactions on Geoscience and Remote Sensing, 47(7): 2218–2232
Tuia D, Volpi M, Copa L, Kanevski M and Munoz-Mari J. 2011. A survey of active learning algorithms for supervised remote sensing image classification. IEEE Journal of Selected Topics in Signal Processing, 5(3): 606–617
Vincent P, Larochelle H, Lajoie I, Bengio Y and Manzagol P A. 2010. Stacked denoising autoencoders: learning useful representations in a deep network with a local denoising criterion. Journal of Machine Learning Research, 11: 3371–3408
Wan L J, Tang K, Li M Z, Zhong Y F and Qin A K. 2015. Collaborative active and semisupervised learning for hyperspectral remote sensing image classification. IEEE Transactions on Geoscience and Remote Sensing, 53(5): 2384–2396
Wang J, Luo C, Huang H Q, Zhao H Z and Wang S Q. 2017a. Transferring pre-trained deep cnns for remote scene classification with general features learned from linear pca network. Remote Sensing, 9(3): 225
Wang J J, Yang J C, Yu K, Lv F J, Huang T and Gong Y H. 2010. Locality-constrained linear coding for image classification//Proceedings of 2010 IEEE Conference on Computer Vision and Pattern Recognition. San Francisco, CA, USA: IEEE: 3360–3367 [DOI: 10.1109/CVPR.2010.5540018]
Wang Y B, Zhang L Q, Deng H, Lu J W, Huang H Y, Zhang L, Liu J, Tang H and Xing X Y. 2017b. Learning a discriminative distance metric with label consistency for scene classification. IEEE Transactions on Geoscience and Remote Sensing, 55(8): 4427–4440
Xia G S, Hu J W, Hu F, Shi B G, Bai X, Zhong Y F, Zhang L P and Lu X Q. 2017. Aid: a benchmark data set for performance evaluation of aerial scene classification. IEEE Transactions on Geoscience and Remote Sensing, 55(7): 3965–3981
许夙晖, 慕晓冬, 赵鹏, 马骥. 2016. 利用多尺度特征与深度网络对遥感影像进行场景分类. 测绘学报, 45(7): 834–840
Xu S H, Mu X D, Zhao P and Ma J. 2016. Scene classification of remote sensing image based on multi-scale feature and deep neural network. Acta Geodaeticaet Cartographica Sinica, 45(7): 834–840 (
Xue Z H, Du P J, Li J and Su H J. 2017. Sparse graph regularization for hyperspectral remote sensing image classification. IEEE Transactions on Geoscience and Remote Sensing, 55(4): 2351–2366
Yang J C, Yu K, Gong Y H and Huang T. 2009. Linear spatial pyramid matching using sparse coding for image classification//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. Miami, FL, USA: IEEE: 1794–1801 [DOI: 10.1109/CVPR.2009.5206757]
Yang Y and Newsam S. 2008. Comparing sift descriptors and gabor texture features for classification of remote sensed imagery//Proceedings of the 15th IEEE International Conference on Image Processing. San Diego, CA, USA: IEEE: 1852–1855 [DOI: 10.1109/ICIP.2008.4712139]
Yang Y and Newsam S. 2010. Bag-of-visual-words and spatial extensions for land-use classification//Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems. New York, NY, USA: ACM: 270–279 [DOI: 10.1145/1869790.1869829]
Yao X W, Han J W, Cheng G, Qian X M and Guo L. 2016. Semantic annotation of high-resolution satellite images via weakly supervised learning. IEEE Transactions on Geoscience and Remote Sensing, 54(6): 3660–3671
Zhang F, Du B and Zhang L P. 2016. Scene classification via a gradient boosting random convolutional network framework. IEEE Transactions on Geoscience and Remote Sensing, 54(3): 1793–1802
Zhang L F, Zhang L P, Tao D C and Huang X. 2012. On combining multiple features for hyperspectral remote sensing image classification. IEEE Transactions on Geoscience and Remote Sensing, 50(3): 879–893
Zhao B, Zhong Y F, Xia G S and Zhang L P. 2016a. Dirichlet-derived multiple topic scene classification model for high spatial resolution remote sensing imagery. IEEE Transactions on Geoscience and Remote Sensing, 54(4): 2108–2123
Zhao B, Zhong Y F and Zhang L P. 2016b. A spectral–structural bag-of-features scene classifier for very high spatial resolution remote sensing imagery. ISPRS Journal of Photogrammetry and Remote Sensing, 116: 73–85
Zhao B, Zhong Y F, Zhang L P and Huang B. 2016c. The fisher kernel coding framework for high spatial resolution scene classification. Remote Sensing, 8(2): 157
Zhao L J, Tang P and Huo L Z. 2014. A 2-D wavelet decomposition-based bag-of-visual-words model for land-use scene classification. International Journal of Remote Sensing, 35(6): 2296–2310
Zhao L L, Tang P and Huo L Z. 2016d. Feature significance-based multibag-of-visual-words model for remote sensing image scene classification. Journal of Applied Remote Sensing, 10(3): 035004
Zhao W Z and Du S H. 2016. Scene classification using multi-scale deeply described visual words. International Journal of Remote Sensing, 37(17): 4119–4131
Zheng X W, Sun X, Fu K and Wang H Q. 2013. Automatic annotation of satellite images via multifeature joint sparse coding with spatial relation constraint. IEEE Geoscience and Remote Sensing Letters, 10(4): 652–656
Zhong P, Gong Z P, Li S T and Schönlieb C B. 2017. Learning to diversify deep belief networks for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing, 55(6): 3516–3530
Zhong P, Gong Z Q and Schönlieb C. 2016. A diversified deep belief network for hyperspectral image classification. ISPRS-International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XLI-B7: 443–449 [DOI: 10.5194/isprs-archives-XLI-B7-443-2016]
Zhong Y F. 2016. Large patch convolutional neural networks for the scene classification of high spatial resolution imagery. Journal of Applied Remote Sensing, 10(2): 025006
Zhu Q Q, Zhong Y F, Zhang L P and Li D R. 2017. Scene classification based on the fully sparse semantic topic model. IEEE Transactions on Geoscience and Remote Sensing, 55(10): 5525–5538
Zou Q, Ni L H, Zhang T and Wang Q. 2015. Deep learning based feature selection for remote sensing scene classification. IEEE Geoscience and Remote Sensing Letters, 12(11): 2321–2325
相关文章
相关作者
相关机构
京公网安备11010802024621