超像素与主动学习相结合的遥感影像变化检测方法
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摘要
针对高分辨率遥感影像变化检测结果较破碎,易产生椒盐噪声、监督训练过程中人工标注成本较高、训练样本冗余以及大量未标注样本信息未有效利用等问题,提出一种超像素与主动学习相结合的高分辨率遥感影像变化检测方法。利用超像素分割算法得到超像素对象,提取其光谱和纹理特征;引入并借助主动学习样本选择策略充分利用未标注样本信息,挖掘不确定性最大、最易错分的样本交由用户人工标注;为保证所选样本的多样性,加入基于余弦角距离的样本相似性度量,以减少样本间信息冗余,在减轻人工标注负担的同时获得良好的分类性能。通过对2组不同场景的遥感影像的实验,表明本文提出的2种方法能够在标注少量训练样本的情况下获得较好的变化检测结果,且加入样本相似性度量的变化检测方法在有效减少人工标注成本和训练样本冗余的同时,能够更快地达到收敛、提升检测质量。
In terms of change detection with high resolution remote sensing images, there are still some unresolved problems such as scattered plots with ragged boundaries in output, being prone to occurrence of"salt-and-pepper"noise, expensive cost of manual annotation in the process of supervised training, redundancy of training samples,underutilization of information in unlabeled samples and so on. In order to address these problems, this paper proposes a new high resolution remote sensing image change detection method by combining the superpixel segmentation technology and Active Learning(AL) approaches. The proposed method consists of the following steps.Firstly the difference image is derived from two temporal remote sensing images. Subsequently the lattice-like homogenous superpixel are obtained by applying the Simple Linear Iterative Clustering(SLIC) algorithm.Simultaneously, we compare the SLIC algorithm with entropy-rate-based and modified-watershed-based superpixel generating algorithms respectively by means of homogeneity of superpixel and their coherence with image object boundaries. Then we compute the means and standard deviations of three bands of superpixel objects as spectral features and extract the entropy, energy and angular second moment by employing Gray-Level Cooccurrence Matrix(GLCM) as texture features. After that, initial training samples are randomly selected and labeled by introducing and following the Margin Sampling(MS) active learning sample selection strategy which is a kind of SVM based AL algorithm taking advantage of SVM geometrical properties and suitable for bipartition problems.A cosine distance based sample similarity measurement called Angle Based Diversity(ABD) is introduced to relief redundancy and ensure diversity of the selected samples. Lastly change detection is carried out according to the extracted information from trained samples. The proposed algorithms(SLIC-MS, SLIC-MS+ABD) are utilized to process World View Ⅱ multispectral remote sensing data of urban and suburb scenes and the detection result from proposed sampling is compared with that from random sampling to explain detection accuracy of our methods. To illustrate the efficiency of methods proposed in this article, we investigate the iterative times of three techniques for reaching the same detection accuracy. Experimental results confirm that both SLIC-MS and SLIC-MS+ABD can reduce manual labeling cost and achieve better change detection quality than random sampling methods. They also indicate that the two proposed methods can find out samples with high uncertainty, which can be labeled by user themselves, from the unlabeled sample pool by making full use of and mining unlabeled sample information.Compared with the other two methods, SLIC-MS+ABD is more accurate with respect to identical data sets(the same two mentioned remote sensing images) and the same labeled sample number because the diversity of new selected samples has been considered in the learning process. In addition, SLIC-MS+ABD can obviously reduce iterative times to converge for achieving the same detection accuracy than other two approaches. On the basis of the experiment, it can be concluded that our proposed methods greatly relief the amount of user marking and acquire good change detection performance on high resolution remote sensing data sets as well. Experimental results also indicate that the methods implemented in this article saliently exhibit their advantages of manual cost reduction in sample labeling, avoidance of training sample redundancy to reach the same change detection quality for the same data set.
In terms of change detection with high resolution remote sensing images, there are still some unresolved problems such as scattered plots with ragged boundaries in output, being prone to occurrence of"salt-and-pepper"noise, expensive cost of manual annotation in the process of supervised training, redundancy of training samples,underutilization of information in unlabeled samples and so on. In order to address these problems, this paper proposes a new high resolution remote sensing image change detection method by combining the superpixel segmentation technology and Active Learning(AL) approaches. The proposed method consists of the following steps.Firstly the difference image is derived from two temporal remote sensing images. Subsequently the lattice-like homogenous superpixel are obtained by applying the Simple Linear Iterative Clustering(SLIC) algorithm.Simultaneously, we compare the SLIC algorithm with entropy-rate-based and modified-watershed-based superpixel generating algorithms respectively by means of homogeneity of superpixel and their coherence with image object boundaries. Then we compute the means and standard deviations of three bands of superpixel objects as spectral features and extract the entropy, energy and angular second moment by employing Gray-Level Cooccurrence Matrix(GLCM) as texture features. After that, initial training samples are randomly selected and labeled by introducing and following the Margin Sampling(MS) active learning sample selection strategy which is a kind of SVM based AL algorithm taking advantage of SVM geometrical properties and suitable for bipartition problems.A cosine distance based sample similarity measurement called Angle Based Diversity(ABD) is introduced to relief redundancy and ensure diversity of the selected samples. Lastly change detection is carried out according to the extracted information from trained samples. The proposed algorithms(SLIC-MS, SLIC-MS+ABD) are utilized to process World View Ⅱ multispectral remote sensing data of urban and suburb scenes and the detection result from proposed sampling is compared with that from random sampling to explain detection accuracy of our methods. To illustrate the efficiency of methods proposed in this article, we investigate the iterative times of three techniques for reaching the same detection accuracy. Experimental results confirm that both SLIC-MS and SLIC-MS+ABD can reduce manual labeling cost and achieve better change detection quality than random sampling methods. They also indicate that the two proposed methods can find out samples with high uncertainty, which can be labeled by user themselves, from the unlabeled sample pool by making full use of and mining unlabeled sample information.Compared with the other two methods, SLIC-MS+ABD is more accurate with respect to identical data sets(the same two mentioned remote sensing images) and the same labeled sample number because the diversity of new selected samples has been considered in the learning process. In addition, SLIC-MS+ABD can obviously reduce iterative times to converge for achieving the same detection accuracy than other two approaches. On the basis of the experiment, it can be concluded that our proposed methods greatly relief the amount of user marking and acquire good change detection performance on high resolution remote sensing data sets as well. Experimental results also indicate that the methods implemented in this article saliently exhibit their advantages of manual cost reduction in sample labeling, avoidance of training sample redundancy to reach the same change detection quality for the same data set.
引文
[1]Du P J,Liu S C.Change detection from multi-temporal remote sensing images by integrating multiple features[J].Journal of Remote Sensing,2012,16(4):663-677
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[5]佃袁勇,方圣辉,姚崇怀.多尺度分割的高分辨率遥感影像变化检测[J].遥感学报,2016,20(1):129-137.[Dian YY,Fang S H,Yao C H.Change detection for high-resolution images using multilevel segment method[J].Journal of Remote Sensing,2016,20(1):129-137.]
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[7]宋熙煜,周利莉,李中国,等.图像分割中的超像素方法研究综述[J].中国图象图形学报,2015,20(5):599-608.[Song X Y,Zhou L L,Li Z G,et al.Review on super-pixel methods in image segmentation[J].Journal of Image and Graphics,2015,20(5):0599-0608.]
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[16]Zhang X,Cahill N D.SLIC Superpixels for efficien graph-based dimensionality reduction of hyperspectra imagery[C]//SPIE Defense+Security.International Society for Optics and Photonics,2015.
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[18]Toro C,Martín C,Pedreroá,et al.Superpixel-based roughness measure for multispectral satellite image segmentation[J].Remote Sensing,2015,7(11):14620-14645.
[19]Csillik O.Fast Segmentation and classification of very high resolution remote sensing data using SLIC superpixels[J].Remote Sensing,2017,9(3):243.
[17]吴健,盛胜利,赵朋朋,等.最小差异采样的主动学习图像分类方法[J].通信学报,2014(1):107-114.[Wu J,Sheng S L,Zhao P P,et al.Minimal difference sampling for active learning image classification[J].Journal on Communications,2014(1):107-114.]
[20]田彦平,陶超,邹峥嵘,等.主动学习与图的半监督相结合的高光谱影像分类[J].测绘学报,2015,44(8):919-926.[Tian Y P,Tao C,Zou Z R,et al.Semi-supervised graphbased hyperspectral image classification with active learning[J].Acta Geodaetica et Cartographica Sinica,2015,44(8):919-926.]
[21]陈荣,曹永锋,孙洪.基于主动学习和半监督学习的多类图像分类[J].自动化学报,2011,37(8):954-962.[Chen R,Cao Y F,Sun H.Multi-class image classification with active learning and semi-supervised learning[J].Acta Automatica Sinica,2011,37(8):954-962.]
[22]Tuia D,Ratle F,Pacifici F,et al.Active learning methods for remote sensing image classification[J].IEEE Transactions on Geoscience&Remote Sensing,2009,47(7):2218-2232.
[23]Pasolli E,Melgani F,Tuia D,et al.SVM active learning approach for image classification using spatial information[J].IEEE Transactions on Geoscience&Remote Sensing,2013,52(4):2217-2233.
[24]Tuia D,Volpi M,Copa L,et al.A Survey of active learning algorithms for supervised remote sensing image classification[J].IEEE Journal of Selected Topics in Signal Processing,2011,5(3):606-617.
[25]Persello C,Bruzzone L.Active and semisupervised learning for the classification of remote sensing images[J].IEEE Transactions on Geoscience&Remote Sensing,2014,52(11):6937-6956.
[26]Brinker K.Incorporating diversity in active learning with support vector machines[C]//Machine Learning,Proceedings of the Twentieth International Conference.DBLP,2003:59-66.
[27]汤玉奇.面向对象的高分辨率影像城市多特征变化检测研究[D].武汉:武汉大学,2013.[Tang Y Q.Object-oriented change detection with multi-feature in urban high-resolution remote sensing imagery[D].Wuhan:Wuhan University,2013.]
[28]Hu Z,Zou Q,Li Q.Watershed superpixel[C]//IEEE International Conference on Image Processing.IEEE,2015.
[2]Singh A.Digital change detection techniques using remotely sensed data[J].International Journal of Remote Sensing,1988,10(6):989-1003.
[3]黄亮,左小清,於雪琴.遥感影像变化检测方法探讨[J].测绘科学,2014,38(4):203-206.[Huang L,Zuo X Q,Yu XQ.Review on change detection methods of remote sensing images[J].Science of Surveying and Mapping,2014,38(4):203-206.]
[4]Liu S C,Du P J,Chen S.A novel change detection method of multi-resolution remotely sensed images based on the decision level fusion[J].Journal of Remote Sensing,2011(4):846-862.
[5]佃袁勇,方圣辉,姚崇怀.多尺度分割的高分辨率遥感影像变化检测[J].遥感学报,2016,20(1):129-137.[Dian YY,Fang S H,Yao C H.Change detection for high-resolution images using multilevel segment method[J].Journal of Remote Sensing,2016,20(1):129-137.]
[6]Ren X,Malik J.Learning a classification model for segmentation[C]//IEEE International Conference on Computer Vision,2003.Proceedings.IEEE,2003:10-17.
[7]宋熙煜,周利莉,李中国,等.图像分割中的超像素方法研究综述[J].中国图象图形学报,2015,20(5):599-608.[Song X Y,Zhou L L,Li Z G,et al.Review on super-pixel methods in image segmentation[J].Journal of Image and Graphics,2015,20(5):0599-0608.]
[8]Shi J,Malik J.Normalized Cuts and Image Segmentation[J].IEEE Transactions on Pattern Analysis&Machine Intelligence,2000,22(8):888-905.
[10]Moore A P,Prince S J D,Warrell J,et al.Superpixel lattices[C]//Computer Vision and Pattern Recognition IEEE Conference on.IEEE,2008:1-8.
[11]Liu M Y,Tuzel O,Ramalingam S,et al.Entropy rate superpixel segmentation[C]//Computer Vision and Pattern Recognition.IEEE,2011:2097-2104.
[12]Vincent L,Soille P.Soille,P.Watersheds in digital spaces:an efficient algorithm based on immersion simulations[J].IEEE Transactions on Pattern Analysis&Machine Intelligence,1991,13(6):583-598.
[13]Levinshtein A,Stere A,Kutulakos K N,et al.Turbo Pixels Fast superpixels using geometric flows[J].IEEE Transactions on Pattern Analysis&Machine Intelligence,200931(12):2290-2297.
[14]Achanta R,Shaji A,Smith K,et al.SLIC superpixels compared to State-of-the-Art superpixel methods[J].IEEETransactions on Pattern Analysis&Machine Intelligence2012,34(11):2274-2282.
[15]Hsu C Y,Ding J J.Efficient image segmentation algorithm using SLIC superpixels and boundary-focused region merging[C]//Communications and Signal Processing.IEEE,2014:1-5.
[16]Zhang X,Cahill N D.SLIC Superpixels for efficien graph-based dimensionality reduction of hyperspectra imagery[C]//SPIE Defense+Security.International Society for Optics and Photonics,2015.
[17]Barreto T L M,Rosa R A S,Wimmer C,et al.Classification of detected changes from multitemporal High-Res Xband SAR Images:Intensity and texture descriptors from super pixels[J].IEEE Journal of Selected Topics in Applied Earth Observations&Remote Sensing,2016,9(12):5436-5448.
[18]Toro C,Martín C,Pedreroá,et al.Superpixel-based roughness measure for multispectral satellite image segmentation[J].Remote Sensing,2015,7(11):14620-14645.
[19]Csillik O.Fast Segmentation and classification of very high resolution remote sensing data using SLIC superpixels[J].Remote Sensing,2017,9(3):243.
[17]吴健,盛胜利,赵朋朋,等.最小差异采样的主动学习图像分类方法[J].通信学报,2014(1):107-114.[Wu J,Sheng S L,Zhao P P,et al.Minimal difference sampling for active learning image classification[J].Journal on Communications,2014(1):107-114.]
[20]田彦平,陶超,邹峥嵘,等.主动学习与图的半监督相结合的高光谱影像分类[J].测绘学报,2015,44(8):919-926.[Tian Y P,Tao C,Zou Z R,et al.Semi-supervised graphbased hyperspectral image classification with active learning[J].Acta Geodaetica et Cartographica Sinica,2015,44(8):919-926.]
[21]陈荣,曹永锋,孙洪.基于主动学习和半监督学习的多类图像分类[J].自动化学报,2011,37(8):954-962.[Chen R,Cao Y F,Sun H.Multi-class image classification with active learning and semi-supervised learning[J].Acta Automatica Sinica,2011,37(8):954-962.]
[22]Tuia D,Ratle F,Pacifici F,et al.Active learning methods for remote sensing image classification[J].IEEE Transactions on Geoscience&Remote Sensing,2009,47(7):2218-2232.
[23]Pasolli E,Melgani F,Tuia D,et al.SVM active learning approach for image classification using spatial information[J].IEEE Transactions on Geoscience&Remote Sensing,2013,52(4):2217-2233.
[24]Tuia D,Volpi M,Copa L,et al.A Survey of active learning algorithms for supervised remote sensing image classification[J].IEEE Journal of Selected Topics in Signal Processing,2011,5(3):606-617.
[25]Persello C,Bruzzone L.Active and semisupervised learning for the classification of remote sensing images[J].IEEE Transactions on Geoscience&Remote Sensing,2014,52(11):6937-6956.
[26]Brinker K.Incorporating diversity in active learning with support vector machines[C]//Machine Learning,Proceedings of the Twentieth International Conference.DBLP,2003:59-66.
[27]汤玉奇.面向对象的高分辨率影像城市多特征变化检测研究[D].武汉:武汉大学,2013.[Tang Y Q.Object-oriented change detection with multi-feature in urban high-resolution remote sensing imagery[D].Wuhan:Wuhan University,2013.]
[28]Hu Z,Zou Q,Li Q.Watershed superpixel[C]//IEEE International Conference on Image Processing.IEEE,2015.