The Design and Use of Steerable Filters
IEEE Transactions on Pattern Analysis and Machine Intelligence
A review of recent texture segmentation and feature extraction techniques
CVGIP: Image Understanding
Handbook of pattern recognition & computer vision
Filtering for Texture Classification: A Comparative Study
IEEE Transactions on Pattern Analysis and Machine Intelligence
Texture Classification by Wavelet Packet Signatures
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiscale estimation of vector field anisotropy application to texture characterization
Signal Processing - From signal processing theory to implementation
Wavelet-based texture retrieval using generalized Gaussian density and Kullback-Leibler distance
IEEE Transactions on Image Processing
Wavelet-based level set evolution for classification of textured images
IEEE Transactions on Image Processing
Indexing of Satellite Images With Different Resolutions by Wavelet Features
IEEE Transactions on Image Processing
Texture classification and segmentation using wavelet frames
IEEE Transactions on Image Processing
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This paper address the problem of change detection in very high resolution remote sensing images. To that end, we define a measure of the observed change based on the distribution of the coefficients issued from a wavelet transform, taking care to be rotation invariant. The dissimilarities are obtained through the Kullback-Liebler distance and a change features vector is defined from all the distances between the bands of the wavelet decomposition. This measurement is able to classify the nature of the change between two images. We present two applications: the first one uses a decision tree to classify several changes (homogeneous or oriented texture, abrupt or subtle change) whereas the second one detects some particular changes from a pair of images (an aerial and a satellite image). These experiments bring out the efficiency of the proposed technique to discriminate correctly the different textures and to interpret each change.