An Automatic Unsupervised Method Based on Context-Sensitive Spectral Angle Mapper for Change Detection of Remote Sensing Images

This paper proposes an automatic unsupervised method for change detection at pixel level of Landsat-5 TM images based on spectral angle mapper (SAM). In most existing studies, conventional use of SAM does not take into account contextual information of a pixel. The proposed method incorporates spatio-contextual information both at feature and decision level for improved change detection accuracy. First, a similarity image is created using context-sensitive spectral angle mapper, and then it is segmented into two segments changed and unchanged using k-means algorithm to create a change map. The quantitative as well as qualitative comparison of the experiment results shows that the proposed method gives better results than the other existing method.

[1]  Peijun Du,et al.  Information fusion techniques for change detection from multi-temporal remote sensing images , 2013, Inf. Fusion.

[2]  Sylvain Douté,et al.  WAVANGLET: An Efficient Supervised Classifier for Hyperspectral Images , 2007, IEEE Transactions on Geoscience and Remote Sensing.

[3]  Rui Seara,et al.  Image segmentation by histogram thresholding using fuzzy sets , 2002, IEEE Trans. Image Process..

[4]  Badrinath Roysam,et al.  Image change detection algorithms: a systematic survey , 2005, IEEE Transactions on Image Processing.

[5]  Jinsong Deng,et al.  PCA‐based land‐use change detection and analysis using multitemporal and multisensor satellite data , 2008 .

[6]  Yu-Jin Zhang,et al.  Ridler and Calvard's, Kittler and Illingworth's and Otsu's methods for image thresholding , 2012, Pattern Recognit. Lett..

[7]  Pramod K. Varshney,et al.  An image change detection algorithm based on Markov random field models , 2002, IEEE Trans. Geosci. Remote. Sens..

[8]  Bülent Sankur,et al.  Survey over image thresholding techniques and quantitative performance evaluation , 2004, J. Electronic Imaging.

[9]  Ke-Sheng Cheng,et al.  Hypothesis-test-based landcover change detection using multi-temporal satellite images – A comparative study , 2008 .

[10]  F. Meer.  The effectiveness of spectral similarity measures for the analysis of hyperspectral imagery , 2006 .

[11]  Turgay Çelik,et al.  Unsupervised Change Detection in Satellite Images Using Principal Component Analysis and $k$-Means Clustering , 2009, IEEE Geoscience and Remote Sensing Letters.

[12]  D. Lu,et al.  Change detection techniques , 2004 .

[13]  Vijay S. Bhagat,et al.  Use of Remote Sensing Techniques for Robust Digital Change Detection of Land: A Review , 2012 .

[14]  Peijun Du,et al.  Fusion of Difference Images for Change Detection Over Urban Areas , 2011, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[15]  Dongmei Chen,et al.  Change detection from remotely sensed images: From pixel-based to object-based approaches , 2013 .

[16]  A. Skidmore,et al.  Comparing accuracy assessments to infer superiority of image classification methods , 2006 .

[17]  Jungho Im.  Neighborhood Correlation Image Analysis for Change Detection Using Different Spatial Resolution Imagery , 2006 .

[18]  Pol Coppin,et al.  Review ArticleDigital change detection methods in ecosystem monitoring: a review , 2004 .

[19]  Paul L. Rosin,et al.  Evaluation of global image thresholding for change detection , 2003, Pattern Recognit. Lett..

[20]  W. Cohen,et al.  Comparison of Tasseled Cap-based Landsat data structures for use in forest disturbance detection , 2005 .

[21]  D. Rubin,et al.  Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .

[22]  Patrick Hostert,et al.  Correcting brightness gradients in hyperspectral data from urban areas , 2006 .

[23]  John R. Jensen,et al.  A change detection model based on neighborhood correlation image analysis and decision tree classification , 2005 .

[24]  Ashbindu Singh,et al.  Review Article Digital change detection techniques using remotely-sensed data , 1989 .

[25]  J. Chan,et al.  Detecting the nature of change in an urban environment : A comparison of machine learning algorithms , 2001 .

[26]  Randolph L. Moses,et al.  Detecting Changes in Hyperspectral Imagery Using a Model-Based Approach , 2011, IEEE Transactions on Geoscience and Remote Sensing.

[27]  Gabriele Moser,et al.  Change detection with synthetic aperture radar images by Wilcoxon statistic likelihood ratio test , 2012, 2012 19th IEEE International Conference on Image Processing.