光谱—频域属性模式融合的高光谱遥感图像变化检测

周承乐; 石茜; 李军; 张新长

doi:10.11834/jrs.20232600

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光谱—频域属性模式融合的高光谱遥感图像变化检测
Spectral-frequency domain attribute pattern fusion for hyperspectral image change detection
2024年28卷第1期页码：105-120
纸质出版日期： 2024-01-07 ，
DOI： 10.11834/jrs.20232600

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周承乐，石茜，李军，张新长.2024.光谱—频域属性模式融合的高光谱遥感图像变化检测.遥感学报，28（1）： 105-120

Zhou C L，Shi Q，Li J and Zhang X C. 2024. Spectral-frequency domain attribute pattern fusion for hyperspectral image change detection. National Remote Sensing Bulletin， 28（1）：105-120
周承乐，石茜，李军，张新长.2024.光谱—频域属性模式融合的高光谱遥感图像变化检测.遥感学报，28（1）： 105-120 DOI： 10.11834/jrs.20232600.

Zhou C L，Shi Q，Li J and Zhang X C. 2024. Spectral-frequency domain attribute pattern fusion for hyperspectral image change detection. National Remote Sensing Bulletin， 28（1）：105-120 DOI： 10.11834/jrs.20232600.

摘要

高光谱作为“图谱合一”的遥感技术，具有精细光谱和空间影像的地面覆盖观测与识别优势。然而，高光谱遥感数据的光谱信息表征以及空间信息的利用给双时相高光谱遥感图像变化检测任务带来了巨大的挑战。为此，本文探讨了一种光谱—频域属性模式融合的高光谱遥感图像变化检测方法SFDAPF（Spectral-Frequency Domain Attribute Pattern Fusion）。首先，设计一种基于梯度相关性的光谱绝对距离，使双时相高光谱遥感图像像元对的属性模式从光谱信息表征方面得到了逐级量化；其次，基于傅里叶变换理论提出一种变化像元属性模式显著性增强策略，从全局空间信息利用方面改善了变化与非变化属性像元对的可分性；再次，将全图属性模式显著性水平与梯度相关性的光谱绝对距离进行融合，得到变化检测的综合界定值；最后，依据虚警阈值确定双时相高光谱遥感图像变化检测的二值化结果。将本文提出的SFDAPF方法在开源的双时相高光谱遥感图像河流和农场数据集上进行了变化检测性能验证，结果表明SFDAPF方法能够优于传统的和最新的变化检测方法，变化检测的总体精度在河流和农场数据集上分别达到了0.96508和0.97287（最高精度为1.00000）。证实了本文SFDAPF方法的有效性。

Abstract

HyperSpectral Imagery (HSI) is a three-dimensional cube data that combines spatial imagery and spectral information

which introduces increased conveniences to the accurate interpretation of observation information of ground coverings. However

high-dimensional nonlinear data processing for the HSI Change Detection (HSI-CD) task encounters challenges. Therefore

an HSI-CD method based on Spectral-Frequency Domain Attribute Pattern Fusion (SFDAPF) is introduced to gradually quantify the spectral representation of pixel attribute patterns. Specifically

a Saliency Enhancement (SE) strategy for pixel attribute patterns based on Fourier transform theory is developed to improve the separability between pixel attribute patterns in the current work. The proposed SFDAPF method comprises four components as follows.

First

a gradient correlation-based spectral absolute distance (GCASD) is designed in this paper. Therefore

the attribute patterns of pixel pairs in bitemporal HSI can be gradually quantified from the aspect of spectral information representation. Then

an SE strategy of attribute patterns of pixel pairs is proposed in accordance with Fourier transform theory

which improves the separability of attribute patterns of changing and non-changing pixel pairs in terms of global spatial information utilization. Next

the saliency level and GCASD per pixel are fused to obtain the comprehensive discrimination value of change detection. Finally

the binarization results of the bitemporal HSI-CD are obtained in accordance with the false alarm threshold.

The proposed SFDAPF method is applied to two open-source bitemporal HSI datasets (i.e.

River and Farmland datasets). Experimental results show that the proposed SFDAPF method can outperform the traditional and state-of-the-art HSI-CD methods. For the River dataset

compared with the traditional methods

the SFDAPF method in this paper introduces the local context information of the pixel in the calculation stage of the GCASD and adopts the global SE strategy

which is effective in reducing false alarms. Compared with the state-of-the-art methods

the SFDAPF method in this paper achieves the highest accuracy for most of the performance evaluation indicators. For the Farmland dataset

the AA

Kappa

IoU

and OA indicators of the SFDAPF method in this paper have reached the highest accuracy

which is 0.01985

0.05653

0.01474

0.02798

and 0.02187 higher than the second highest accuracy. In addition

the OAu (0.97500) and OAc (0.96766) indicators of the SFDAPF method did not achieve the highest accuracy. However

they were only 0.00673 and 0.01237 lower than the highest accuracy

which can be called slightly lower than the highest accuracy. Therefore

the experiments verified the effectiveness of the proposed SFDAPF method in the HSI-CD task.

The proposed SFDAPF method generally considers the representation of spectral information and the utilization of neighborhood spatial information

thus promoting the overall accuracy of HSI-CD. However

the proposed SFDAPF method only considers the single-window eight-connected neighborhood in the spectral characterization stage and the magnitude features represented in the frequency domain. Therefore

future research work should further explore the contribution of dual-window spectral information representation and phase information of frequency domain representation to HSI-CD task.

关键词

高光谱图像变化检测图像融合特征提取显著性分析傅里叶变换

Keywords

hyperspectral imagechange detectionimage fusionfeature extractionsaliency analysisfourier transform

references

Baisantry M, Negi D S and Manocha O P. 2012. Change vector analysis using enhanced PCA and inverse triangular function-based thresholding. Defence Science Journal, 62(4): 236-242 [DOI: 10.14429/dsj.62.1072http://dx.doi.org/10.14429/dsj.62.1072]

Bovolo F, Bruzzone L and Marconcini M. 2008. A novel approach to unsupervised change detection based on a semisupervised SVM and a similarity measure. IEEE Transactions on Geoscience and Remote Sensing, 46(7): 2070-2082 [DOI: 10.1109/TGRS.2008.916643http://dx.doi.org/10.1109/TGRS.2008.916643]

Carvalho Jr O A, Guimarães R F, Gillespie A R, Silva N C and Gomes R A T. 2011. A new approach to change vector analysis using distance and similarity measures. Remote Sensing, 3(11): 2473-2493 [DOI: 10.3390/rs3112473http://dx.doi.org/10.3390/rs3112473]

Cong R M, Lei J J, Fu H Z, Cheng M M, Lin W S and Huang Q M. 2019. Review of visual saliency detection with comprehensive information. IEEE Transactions on circuits and Systems for Video Technology, 29(10): 2941-2959 [DOI: 10.1109/TCSVT.2018.2870832http://dx.doi.org/10.1109/TCSVT.2018.2870832]

Demir B, Bovolo F and Bruzzone L. 2012. Detection of land-cover transitions in multitemporal remote sensing images with active-learning-based compound classification. IEEE Transactions on Geoscience and Remote Sensing, 50(5): 1930-1941 [DOI: 10.1109/TGRS.2011.2168534http://dx.doi.org/10.1109/TGRS.2011.2168534]

Du B, Ru L X, Wu C and Zhang L P. 2019. Unsupervised deep slow feature analysis for change detection in multi-temporal remote sensing images. IEEE Transactions on Geoscience and Remote Sensing, 57(12): 9976-9992 [DOI: 10.1109/TGRS.2019.2930682http://dx.doi.org/10.1109/TGRS.2019.2930682]

Du P J, Liu S C, Gamba P, Tan K and Xia J S. 2012. Fusion of difference images for change detection over urban areas. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 5(4): 1076-1086 [DOI: 10.1109/JSTARS.2012.2200879http://dx.doi.org/10.1109/JSTARS.2012.2200879]

Gao F, Wang X, Dong J Y and Wang S K. 2018. Synthetic aperture radar image change detection based on frequency-domain analysis and random multigraphs. Journal of Applied Remote Sensing, 12(1): 016010 [DOI: 10.1117/1.JRS.12.016010].

Hemati M, Hasanlou M, Mahdianpari M and Mohammadimanesh F. 2021. A systematic review of landsat data for change detection applications: 50 years of monitoring the earth. Remote Sensing, 13(15): 2869 [DOI: 10.3390/rs13152869http://dx.doi.org/10.3390/rs13152869]

Hou X D and Zhang L Q. 2007. Saliency detection: a spectral residual approach//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition. Minneapolis: IEEE: 1-8 [DOI: 10.1109/CVPR.2007.383267http://dx.doi.org/10.1109/CVPR.2007.383267]

Hou Z F, Li W and Du Q. 2021. A patch tensor-based change detection method for hyperspectral images//Proceedings of 2021 IEEE International Geoscience and Remote Sensing Symposium. Belgium: IEEE: 4328-4331 [DOI: 10.1109/IGARSS47720.2021.9554630http://dx.doi.org/10.1109/IGARSS47720.2021.9554630]

Hou Z F, Li W, Li L, Tao R and Du Q. 2022. Hyperspectral change detection based on multiple morphological profiles. IEEE Transactions on Geoscience and Remote Sensing, 60: 5507312 [DOI: 10.1109/TGRS.2021.3090802http://dx.doi.org/10.1109/TGRS.2021.3090802]

Hu M Q, Wu C, Du B and Zhang L P. 2023. Binary change guided hyperspectral multiclass change detection. IEEE Transactions on Image Processing, 32: 791-806 [DOI: 10.1109/TIP.2022.3233187http://dx.doi.org/10.1109/TIP.2022.3233187]

Jaemsiri J, Titijaroonroj T and Rungrattanaubol J. 2019. Modified scale-space analysis in frequency domain based on adaptive multiscale Gaussian filter for saliency detection//Proceedings of 2019 16th International Joint Conference on Computer Science and Software Engineering. Chonburi: IEEE: 218-223 [DOI: 10.1109/JCSSE.2019.8864211http://dx.doi.org/10.1109/JCSSE.2019.8864211]

Koch C and Poggio T. 1999. Predicting the visual world: silence is golden. Nature Neuroscience, 2(1): 9-10 [DOI: 10.1038/4511http://dx.doi.org/10.1038/4511]

Kwan C. 2019. Methods and challenges using multispectral and hyperspectral images for practical change detection applications. Information, 10(11): 353 [DOI: 10.3390/info10110353http://dx.doi.org/10.3390/info10110353]

Li J, Levine M D, An X J, Xu X and He H G. 2013. Visual saliency based on scale-space analysis in the frequency domain. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(4): 996-1010 [DOI: 10.1109/TPAMI.2012.147http://dx.doi.org/10.1109/TPAMI.2012.147]

Li M K, Li M, Zhang P, Wu Y, Song W Y and An L. 2019. SAR image change detection using PCANet guided by saliency detection. IEEE Geoscience and Remote Sensing Letters, 16(3): 402-406 [DOI: 10.1109/LGRS.2018.2876616http://dx.doi.org/10.1109/LGRS.2018.2876616]

Liu S C, Marinelli D, Bruzzone L and Bovolo F. 2019. A review of change detection in multitemporal hyperspectral images: current techniques, applications, and challenges. IEEE Geoscience and Remote Sensing Magazine, 7(2): 140-158 [DOI: 10.1109/MGRS.2019.2898520http://dx.doi.org/10.1109/MGRS.2019.2898520]

Luo F L, Du B, Zhang L P, Zhang L F and Tao D C. 2019. Feature learning using spatial-spectral hypergraph discriminant analysis for hyperspectral image. IEEE Transactions on Cybernetics, 49(7): 2406-2419 [DOI: 10.1109/TCYB.2018.2810806http://dx.doi.org/10.1109/TCYB.2018.2810806]

Marchesi S and Bruzzone L. 2009. ICA and kernel ICA for change detection in multispectral remote sensing images//Proceedings of 2009 IEEE International Geoscience and Remote Sensing Symposium. Cape Town: IEEE: II-980-II-983 [DOI: 10.1109/IGARSS.2009.5418265http://dx.doi.org/10.1109/IGARSS.2009.5418265]

Nielsen A A. 2007. The regularized iteratively reweighted MAD method for change detection in multi-and hyperspectral data. IEEE Transactions on Image Processing, 16(2): 463-478 [DOI: 10.1109/TIP.2006.888195http://dx.doi.org/10.1109/TIP.2006.888195]

Ortiz-Rivera V, Vélez-Reyes M and Roysam B. 2006. Change detection in hyperspectral imagery using temporal principal components//Proceedings Volume 6233, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XII. Orlando: SPIE: 368-377 [DOI: 10.1117/12.667961http://dx.doi.org/10.1117/12.667961]

Ou X F, Liu L Z, Tu B, Zhang G Y and Xu Z. 2022. A CNN framework with slow-fast band selection and feature fusion grouping for hyperspectral image change detection. IEEE Transactions on Geoscience and Remote Sensing, 60: 5524716 [DOI: 10.1109/TGRS.2022.3156041http://dx.doi.org/10.1109/TGRS.2022.3156041]

Prasad S and Bruce L M. 2008. Limitations of principal components analysis for hyperspectral target recognition. IEEE Geoscience and Remote Sensing Letters, 5(4): 625-629 [DOI: 10.1109/LGRS.2008.2001282http://dx.doi.org/10.1109/LGRS.2008.2001282]

Shang X D, Song M P, Wang Y L, Yu C Y, Yu H Y, Li F and Chang C I. 2021. Target-constrained interference-minimized band selection for hyperspectral target detection. IEEE Transactions on Geoscience and Remote Sensing, 59(7): 6044-6064 [DOI: 10.1109/TGRS.2020.3010826http://dx.doi.org/10.1109/TGRS.2020.3010826]

Song A, Choi J, Han Y and Kim Y. 2018. Change detection in hyperspectral images using recurrent 3D fully convolutional networks. Remote Sensing, 10(11): 1827 [DOI: 10.3390/rs10111827http://dx.doi.org/10.3390/rs10111827]

Srivastava A, Lee A B, Simoncelli E P and Zhu S C. 2003. On advances in statistical modeling of natural images. Journal of Mathematical Imaging and Vision, 18(1): 17-33 [DOI: 10.1023/A:1021889010444http://dx.doi.org/10.1023/A:1021889010444]

Su H J. 2022. Dimensionality reduction for hyperspectral remote sensing: advances, challenges, and prospects. National Remote Sensing Bulletin, 26(8): 1504-1529.

苏红军. 2022. 高光谱遥感影像降维: 进展、挑战与展望. 遥感学报, 26(8): 1504-1529 [DOI: 10.11834/jrs.20210354http://dx.doi.org/10.11834/jrs.20210354]

Su H J, Wu Z Y, Zhang H H and Du Q. 2022. Hyperspectral anomaly detection: a survey. IEEE Geoscience and Remote Sensing Magazine, 10(1): 64-90 [DOI: 10.1109/MGRS.2021.3105440http://dx.doi.org/10.1109/MGRS.2021.3105440]

Tu B, Zhou C L, Peng J, Zhang G Y and Peng Y S. 2021. Feature extraction via joint adaptive structure density for hyperspectral imagery classification. IEEE Transactions on Instrumentation and Measurement, 70: 5006916 [DOI: 10.1109/TIM.2020.3038557http://dx.doi.org/10.1109/TIM.2020.3038557]

Wang L G, Wang L F, Wang Q M and Bruzzone L. 2022. RSCNet: a residual self-calibrated network for hyperspectral image change detection. IEEE Transactions on Geoscience and Remote Sensing, 60: 5529917 [DOI: 10.1109/TGRS.2022.3177478http://dx.doi.org/10.1109/TGRS.2022.3177478]

Wang Q, Yuan Z H, Du Q and Li X L. 2019. GETNET: a general end-to-end 2-D CNN framework for hyperspectral image change detection. IEEE Transactions on Geoscience and Remote Sensing, 57(1): 3-13 [DOI: 10.1109/TGRS.2018.2849692http://dx.doi.org/10.1109/TGRS.2018.2849692]

Yousif O and Ban Y F. 2014. Improving SAR-based urban change detection by combining MAP-MRF classifier and nonlocal means similarity weights. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7(10): 4288-4300 [DOI: 10.1109/JSTARS.2014.2347171http://dx.doi.org/10.1109/JSTARS.2014.2347171]

Zhan T M, Song B, Sun L, Jia X P, Wan M H, Yang G W and Wu Z B. 2021. TDSSC: a three-directions spectral-spatial convolution neural network for hyperspectral image change detection. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14: 377-388 [DOI: 10.1109/JSTARS.2020.3037070http://dx.doi.org/10.1109/JSTARS.2020.3037070]

Zhang B, Yang W, Gao L R and Chen D M. 2012. Real-time target detection in hyperspectral images based on spatial-spectral information extraction. EURASIP Journal on Advances in Signal Processing, 2012: 142 [DOI: 10.1186/1687-6180-2012-142http://dx.doi.org/10.1186/1687-6180-2012-142]

Zhang J F, Xie L L and Tao X X. 2003. Change detection of earthquake-damaged buildings on remote sensing image and its application in seismic disaster assessment//Proceedings of 2003 IEEE International Geoscience and Remote Sensing Symposium. Toulouse: IEEE: 2436-2438 [DOI: 10.1109/IGARSS.2003.1294467http://dx.doi.org/10.1109/IGARSS.2003.1294467]

Zhao C H, Cheng H and Feng S. 2022. A spectral–spatial change detection method based on simplified 3-D convolutional autoencoder for multitemporal hyperspectral images. IEEE Geoscience and Remote Sensing Letters, 19: 5507705 [DOI: 10.1109/LGRS.2021.3096526http://dx.doi.org/10.1109/LGRS.2021.3096526]

Zheng K, Gao L R, Liao W Z, Hong D F, Zhang B, Cui X M and Chanussot J. 2021. Coupled convolutional neural network with adaptive response function learning for unsupervised hyperspectral super resolution. IEEE Transactions on Geoscience and Remote Sensing, 59(3): 2487-2502 [DOI: 10.1109/TGRS.2020.3006534http://dx.doi.org/10.1109/TGRS.2020.3006534]

Zhou C L, Tu B, Li N Y, He W and Plaza A. 2021. Structure-aware multikernel learning for hyperspectral image classification. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14: 9837-9854 [DOI: 10.1109/JSTARS.2021.3111740http://dx.doi.org/10.1109/JSTARS.2021.3111740]

Zhou C L, Tu B, Ren Q and Chen S Y. 2022. Spatial peak-aware collaborative representation for hyperspectral imagery classification. IEEE Geoscience and Remote Sensing Letters, 19: 5506805 [DOI: 10.1109/LGRS.2021.3083416http://dx.doi.org/10.1109/LGRS.2021.3083416]

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