融合自适应窗口显著性检测和改进超像素分割的高光谱异常检测

钱晓亮; 曾银凤; 林生; 张博; 任航丽; 王慰

doi:10.11834/jrs.20222004

AI+遥感 | 浏览量 : 0 下载量: 157 CSCD: 0 更多指标

R-PDF
PDF
导出
分享
收藏
专辑

融合自适应窗口显著性检测和改进超像素分割的高光谱异常检测
Hyperspectral anomaly detection via combining adaptive window saliency detection and improved superpixel segmentation
2023年27卷第12期页码：2748-2761
纸质出版日期： 2023-12-07 ，
DOI： 10.11834/jrs.20222004

扫描看全文

钱晓亮，曾银凤，林生，张博，任航丽，王慰.2023.融合自适应窗口显著性检测和改进超像素分割的高光谱异常检测.遥感学报，27（12）： 2748-2761

Qian X L，Zeng Y F，Lin S，Zhang B，Ren H L and Wang W. 2023. Hyperspectral anomaly detection via combining adaptive window saliency detection and improved superpixel segmentation. National Remote Sensing Bulletin， 27（12）：2748-2761
钱晓亮，曾银凤，林生，张博，任航丽，王慰.2023.融合自适应窗口显著性检测和改进超像素分割的高光谱异常检测.遥感学报，27（12）： 2748-2761 DOI： 10.11834/jrs.20222004.

Qian X L，Zeng Y F，Lin S，Zhang B，Ren H L and Wang W. 2023. Hyperspectral anomaly detection via combining adaptive window saliency detection and improved superpixel segmentation. National Remote Sensing Bulletin， 27（12）：2748-2761 DOI： 10.11834/jrs.20222004.

摘要

高光谱异常检测旨在识别与周围像素具有显著光谱差异的像素，由于不需要先验光谱信息的特点，其在军事和民用领域发挥重要价值。实现高光谱异常检测的一个重要手段是局部对比度计算，现有方法通常采用双窗口法进行计算，然而，窗口尺寸通常依据经验值进行手工设定，泛化能力不足。为了解决上述问题，本文提出了一种融合自适应窗口显著性检测和改进超像素分割的高光谱异常检测方法。该方法先利用对抗自编码网络对高光谱图像进行降维，以降低模型计算复杂度。其次，引入正交投影散度来改进超像素分割中的光谱距离度量方式，提升分割精度。然后，提出一种自适应窗口显著性检测算法来初步定位异常目标，该算法依据超像素分割结果来自适应确定双窗口，提高显著性检测的精度和泛化能力。最后，采用域变换递归滤波和阈值化操作对初始检测结果进行后处理，降低虚警率。消融实验表明，本文所提基于正交投影散度的改进超像素分割算法较基于传统光谱距离度量方式的算法性能更优，所提自适应窗口显著性检测算法的性能优于传统手工设定窗口尺寸的算法；与7种流行算法的主、客观对比实验表明，本文方法在总检测精度和异常—背景像素分离度上均优于流行算法。综上所述，本文所提改进超像素分割算法能提升现有高光谱图像超像素分割的效果，以此为基础所设计的自适应窗口显著性检测算法不仅能克服现有双窗口算法泛化能力不足的问题，还能获得优于流行算法的异常检测效果。

Abstract

Hyperspectral anomaly detection is used to identify pixels with significant spectral contrast to their surrounding pixels. It plays a valuable role in military and civilian fields due to the characteristic that the priori spectral information is not required. The existing local contrast-based methods usually adopt dual rectangular window scheme for hyperspectral anomaly detection. However

they empirically set the size of dual window

which limits their generalization capability.

A hyperspectral anomaly detection method via combining adaptive window saliency detection and improved superpixel segmentation is proposed in this study to address the abovementioned issue. An adversarial autoencoder is first introduced to reduce the dimension of the hyperspectral image for decreasing the computation complexity of the proposed method. Second

the dimension-reduced hyperspectral image is segmented by improved superpixel segmentation. The existing spectral distance measurements used in the superpixel segmentation are effective when the relationship between the spectral value and the intensity of each pixel is linear. However

this condition cannot be guaranteed in practical applications. The improved superpixel segmentation adopts the orthogonal projection divergence to measure the spectral distance for solving the aforementioned problem. Thereafter

an adaptive window-based saliency detection algorithm is proposed and used to obtain the initial detection results. Specifically

the size of the inner window is adaptively determined by the superpixels

which ensures that the pixels belonging to the same inner window are homogeneous. The outer window can be obtained by enlarging the inner window with fixed size. Finally

the domain transform recursive filter and thresholding operation are employed to optimize the initial detection results for reducing the false alarm rate.

The comparisons between the orthogonal projection divergence and three common spectral distance measurements (Euclidean distance

spectral angular mapping

and spectral information divergence) in terms of AUC show that the orthogonal projection divergence-based method achieves the highest score on all five datasets. The comparisons between the adaptive window and traditional manual setting dual window in terms of AUC show that the adaptive window-based method achieves the highest score on all five datasets. Comprehensive comparisons between the proposed method and seven state-of-the-art methods on five public datasets are implemented to validate the overall performance of the proposed method. Specifically

the subjective comparisons show that the anomalous pixels detected by the proposed method are more precise and have stronger contrast to background regions. The objective comparisons demonstrate that the proposed method obtains the highest overall detection accuracy and offers the best separability between the anomalous and background pixels.

Three conclusions can be derived from this study. First

the improved superpixel segmentation algorithm can enhance the segmentation results

and the proposed adaptive window scheme can increase the performance of saliency detection. Second

the proposed method has excellent detection accuracy

false alarm rate

and separability between the anomalous and background pixels. Finally

the overall performance of the proposed method is superior to that of state-of-the-art methods.

关键词

异常检测高光谱图像正交投影散度超像素分割自适应窗口显著性检测

Keywords

anomaly detectionhyperspectral imageorthogonal projection divergencesuperpixel segmentationadaptive windowsaliency detection

references

Achanta R, Shaji A, Smith K, Lucchi A, Fua P and Süsstrunk S. 2012. SLIC superpixels compared to state-of-the-art superpixel methods. IEEE Transactions on Pattern Analysis and Machine Intelligence, 34(11): 2274-2282 [DOI: 10.1109/TPAMI.2012.120http://dx.doi.org/10.1109/TPAMI.2012.120]

Bacher R, Meillier C, Chatelain F and Michel O. 2017. Robust control of varying weak hyperspectral target detection with sparse nonnegative representation. IEEE Transactions on Signal Processing, 65(13): 3538-3550 [DOI: 10.1109/TSP.2017.2688965http://dx.doi.org/10.1109/TSP.2017.2688965]

Bioucas-Dias J M, Plaza A, Camps-Valls G, Scheunders P, Nasrabadi N and Chanussot J. 2013. Hyperspectral remote sensing data analysis and future challenges. IEEE Geoscience and Remote Sensing Magazine, 1(2): 6-36 [DOI: 10.1109/MGRS.2013.2244672http://dx.doi.org/10.1109/MGRS.2013.2244672]

Bradley A P. 1997. The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern Recognition, 30(7): 1145-1159 [DOI: 10.1016/S0031-3203(96)00142-2http://dx.doi.org/10.1016/S0031-3203(96)00142-2]

Chen J H and Yang J. 2014. Robust subspace segmentation via low-rank representation. IEEE Transactions on Cybernetics, 44(8): 1432-1445 [DOI: 10.1109/TCYB.2013.2286106http://dx.doi.org/10.1109/TCYB.2013.2286106]

Chen Y, Nasrabadi N M and Tran T D. 2011. Simultaneous joint sparsity model for target detection in hyperspectral imagery. IEEE Geoscience and Remote Sensing Letters, 8(4): 676-680 [DOI: 10.1109/LGRS.2010.2099640http://dx.doi.org/10.1109/LGRS.2010.2099640]

Du B and Zhang L P. 2011. Random-selection-based anomaly detector for hyperspectral imagery. IEEE Transactions on Geoscience and Remote Sensing, 49(5): 1578-1589 [DOI: 10.1109/TGRS.2010.2081677http://dx.doi.org/10.1109/TGRS.2010.2081677]

Du B, Zhao R, Zhang L P and Zhang L F. 2016. A spectral-spatial based local summation anomaly detection method for hyperspectral images. Signal Processing, 124: 115-131 [DOI: 10.1016/j.sigpro.2015.09.037http://dx.doi.org/10.1016/j.sigpro.2015.09.037]

Flach P, Hernández-Orallo J and Ferri C. 2011. A coherent interpretation of AUC as a measure of aggregated classification performance//Proceedings of the 28th International Conference on International Conference on Machine Learning. Bellevue: Omnipress: 657-664

Gastal E S L and Oliveira M M. 2011. Domain transform for edge-aware image and video processing. ACM Transactions on Graphics, 30(4): 69 [DOI: 10.1145/2010324.1964964http://dx.doi.org/10.1145/2010324.1964964]

Goferman S, Zelnik-Manor L and Tal A. 2012. Context-aware saliency detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 34(10): 1915-1926 [DOI: 10.1109/TPAMI.2011.272http://dx.doi.org/10.1109/TPAMI.2011.272]

Guo Q D, Zhang B, Ran Q, Gao L R, Li J and Plaza A. 2014. Weighted-RXD and linear filter-based RXD: improving background statistics estimation for anomaly detection in hyperspectral imagery. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7(6): 2351-2366 [DOI: 10.1109/JSTARS.2014.2302446http://dx.doi.org/10.1109/JSTARS.2014.2302446]

Itti L, Koch C and Niebur E. 1998. A model of saliency-based visual attention for rapid scene analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(11): 1254-1259 [DOI: 10.1109/34.730558http://dx.doi.org/10.1109/34.730558]

Jiang T, Li Y S, Xie W Y and Du Q. 2020. Discriminative reconstruction constrained generative adversarial network for hyperspectral anomaly detection. IEEE Transactions on Geoscience and Remote Sensing, 58(7): 4666-4679 [DOI: 10.1109/TGRS.2020.2965961http://dx.doi.org/10.1109/TGRS.2020.2965961]

Kang X D, Zhang X P, Li S T, Li K L, Li J and Benediktsson J A. 2017. Hyperspectral anomaly detection with attribute and edge-preserving filters. IEEE Transactions on Geoscience and Remote Sensing, 55(10): 5600-5611 [DOI: 10.1109/TGRS.2017.2710145http://dx.doi.org/10.1109/TGRS.2017.2710145]

Kwon H and Nasrabadi N M. 2005. Kernel RX-algorithm: a nonlinear anomaly detector for hyperspectral imagery. IEEE Transactions on Geoscience and Remote Sensing, 43(2): 388-397 [DOI: 10.1109/TGRS.2004.841487http://dx.doi.org/10.1109/TGRS.2004.841487]

Larochelle H, Erhan D, Courville A, Bergstra J and Bengio Y. 2007. An empirical evaluation of deep architectures on problems with many factors of variation//Proceedings of the 24th International Conference on Machine Learning. Corvalis: Association for Computing Machinery: 473-480 [DOI: 10.1145/1273496.1273556http://dx.doi.org/10.1145/1273496.1273556]

Li J Y, Zhang H Y, Zhang L P and Ma L. 2015. Hyperspectral anomaly detection by the use of background joint sparse representation. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 8(6): 2523-2533 [DOI: 10.1109/JSTARS.2015.2437073http://dx.doi.org/10.1109/JSTARS.2015.2437073]

Li L, Li W, Du Q and Tao R. 2021. Low-rank and sparse decomposition with mixture of gaussian for hyperspectral anomaly detection. IEEE Transactions on Cybernetics, 51(9): 4363-4372 [DOI: 10.1109/TCYB.2020.2968750http://dx.doi.org/10.1109/TCYB.2020.2968750]

Li S T, Zhang K Z, Hao Q B, Duan P H and Kang X D. 2018. Hyperspectral anomaly detection with multiscale attribute and edge-preserving filters. IEEE Geoscience and Remote Sensing Letters, 15(10): 1605-1609 [DOI: 10.1109/LGRS.2018.2853705http://dx.doi.org/10.1109/LGRS.2018.2853705]

Li W and Du Q. 2015. Collaborative representation for hyperspectral anomaly detection. IEEE Transactions on Geoscience and Remote Sensing, 53(3): 1463-1474 [DOI: 10.1109/TGRS.2014.2343955http://dx.doi.org/10.1109/TGRS.2014.2343955]

Liu Z X, Ma L and Du Q. 2021. Class-wise distribution adaptation for unsupervised classification of hyperspectral remote sensing images. IEEE Transactions on Geoscience and Remote Sensing, 59(1): 508-521 [DOI: 10.1109/tgrs.2020.2997863http://dx.doi.org/10.1109/tgrs.2020.2997863]

Manolakis D and Shaw G. 2002. Detection algorithms for hyperspectral imaging applications. IEEE Signal Processing Magazine, 19(1): 29-43 [DOI: 10.1109/79.974724http://dx.doi.org/10.1109/79.974724]

Molero J M, Garzon E M, Garcia I and Plaza A. 2013. Analysis and optimizations of global and local versions of the RX algorithm for anomaly detection in hyperspectral data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 6(2): 801-814 [DOI: 10.1109/JSTARS.2013.2238609http://dx.doi.org/10.1109/JSTARS.2013.2238609]

Niu Y B and Wang B. 2016. Hyperspectral anomaly detection based on low-rank representation and learned dictionary. Remote Sensing, 8(4): 289 [DOI: 10.3390/rs8040289http://dx.doi.org/10.3390/rs8040289]

Otsu N. 1979. A threshold selection method from gray-level histograms. IEEE Transactions on Systems, Man, and Cybernetics, 9(1): 62-66 [DOI: 10.1109/TSMC.1979.4310076http://dx.doi.org/10.1109/TSMC.1979.4310076]

Paris S, Mary D and Ferrari A. 2013. Detection tests using sparse models, with application to hyperspectral data. IEEE Transactions on Signal Processing, 61(6): 1481-1494 [DOI: 10.1109/TSP.2013.2238533http://dx.doi.org/10.1109/TSP.2013.2238533]

Perona P and Malik J. 1990. Scale-space and edge detection using anisotropic diffusion. IEEE Transactions on Pattern Analysis and Machine Intelligence, 12(7): 629-639 [DOI: 10.1109/34.56205http://dx.doi.org/10.1109/34.56205]

Peyghambari S M and Zhang Y. 2021. Hyperspectral remote sensing in lithological mapping, mineral exploration, and environmental geology: an updated review. Journal of Applied Remote Sensing, 15(3): 031501 [DOI: 10.1117/1.JRS.15.031501http://dx.doi.org/10.1117/1.JRS.15.031501]

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

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 [DOI: 10.11834/jrs.20188015http://dx.doi.org/10.11834/jrs.20188015]

Qian X L, Lin S, Cheng G, Yao X W, Ren H L and Wang W. 2020. Object detection in remote sensing images based on improved bounding box regression and multi-level features fusion. Remote Sensing, 12(1): 143 [DOI: 10.3390/rs12010143http://dx.doi.org/10.3390/rs12010143]

Reed I S and Yu X. 1990. Adaptive multiple-band cfar detection of an optical pattern with unknown spectral distribution. IEEE Transactions on Acoustics, Speech, and Signal Processing, 38(10): 1760-1770 [DOI: 10.1109/29.60107http://dx.doi.org/10.1109/29.60107]

Schaum A. 2004. Joint subspace detection of hyperspectral targets//Proceedings of the 2004 IEEE Aerospace Conference. Big Sky: IEEE: 1818-1824 [DOI: 10.1109/AERO.2004.1367963http://dx.doi.org/10.1109/AERO.2004.1367963]

Stein D W J, Beaven S G, Hoff L E, Winter E M, Schaum A P and Stocker A D. 2002. Anomaly detection from hyperspectral imagery. IEEE Signal Processing Magazine, 19(1): 58-69 [DOI: 10.1109/79.974730http://dx.doi.org/10.1109/79.974730]

Su H J, Du P J and Du Q. 2012. Semi-supervised dimensionality reduction using orthogonal projection divergence-based clustering for hyperspectral imagery. Optical Engineering, 51(11): 111715 [DOI: 10.1117/1.OE.51.11.111715http://dx.doi.org/10.1117/1.OE.51.11.111715]

Tan K, Li E Z, Du Q and Du P J. 2014. Hyperspectral image classification using band selection and morphological profiles. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7(1): 40-48 [DOI: 10.1109/JSTARS.2013.2265697http://dx.doi.org/10.1109/JSTARS.2013.2265697]

Tomasi C and Manduchi R. 1998. Bilateral filtering for gray and color images//Proceedings of the Sixth International Conference on Computer Vision. Bombay: IEEE: 839-846 [DOI: 10.1109/ICCV.1998.710815http://dx.doi.org/10.1109/ICCV.1998.710815]

Tu B, Zhang X F, Kang X D, Zhang G Y and Li S T. 2019. Density peak-based noisy label detection for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing, 57(3): 1573-1584 [DOI: 10.1109/TGRS.2018.2867444http://dx.doi.org/10.1109/TGRS.2018.2867444]

Vanegas F, Bratanov D, Powell K, Weiss J and Gonzalez F. 2018. A novel methodology for improving plant pest surveillance in vineyards and crops using UAV-based hyperspectral and spatial data. Sensors, 18(1): 260 [DOI: 10.3390/s18010260http://dx.doi.org/10.3390/s18010260]

Williams J P, Bihl T J and Bauer K W. 2013. Towards the mitigation of correlation effects in anomaly detection for hyperspectral imagery. The Journal of Defense Modeling and Simulation: Applications, Methodology, Technology, 10(3): 263-273 [DOI: 10.1177/1548512912473679http://dx.doi.org/10.1177/1548512912473679]

Xie W Y, Lei J, Liu B Z, Li Y S and Jia X P. 2019. Spectral constraint adversarial autoencoders approach to feature representation in hyperspectral anomaly detection. Neural Networks, 119: 222-234 [DOI: 10.1016/j.neunet.2019.08.012http://dx.doi.org/10.1016/j.neunet.2019.08.012]

Xu Y, Wu Z B, Li J, Plaza A and Wei Z H. 2016. Anomaly detection in hyperspectral images based on low-rank and sparse representation. IEEE Transactions on Geoscience and Remote Sensing, 54(4): 1990-2000 [DOI: 10.1109/TGRS.2015.2493201http://dx.doi.org/10.1109/TGRS.2015.2493201]

Zhang Y X, Du B, Zhang L P and Wang S G. 2016. A low-rank and sparse matrix decomposition-based mahalanobis distance method for hyperspectral anomaly detection. IEEE Transactions on Geoscience and Remote Sensing, 54(3): 1376-1389 [DOI: 10.1109/TGRS.2015.2479299http://dx.doi.org/10.1109/TGRS.2015.2479299]

文章被引用时，请邮件提醒。

提交

基于空谱多路自编码器的高光谱图像异常检测

基于特征层融合的高光谱图像异常检测算法研究

光谱—频域属性模式融合的高光谱遥感图像变化检测

模态间匹配学习的高光谱和激光雷达联合分类