A Theory for Multiresolution Signal Decomposition: The Wavelet Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Scale-Space and Edge Detection Using Anisotropic Diffusion
IEEE Transactions on Pattern Analysis and Machine Intelligence
Ten lectures on wavelets
Smoothing and edge detection by time-varying coupled nonlinear diffusion equations
Computer Vision and Image Understanding
Computer Vision and Image Understanding
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image recovery using the anisotropic diffusion equation
IEEE Transactions on Image Processing
Efficient and reliable schemes for nonlinear diffusion filtering
IEEE Transactions on Image Processing
Spatio-temporal reasoning for the classification of satellite image time series
Pattern Recognition Letters
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Quantification of forestland cover extents, changes and causes thereof are currently of regional and global research priority. Remote sensing data (RSD) play a significant role in this exercise. However, supervised classification-based forest mapping from RSD are limited by lack of ground-truth- and spectral-only-based methods. In this paper, first results of a methodology to detect change/no change based on unsupervised multiresolution image transformation are presented. The technique combines directional wavelet transformation texture and multispectral imagery in an anisotropic diffusion aggregation or segmentation algorithm. The segmentation algorithm was implemented in unsupervised self-organizing feature map neural network. Using Landsat TM (1986) and ETM+ (2001), logical-operations-based change detection results for part of Mau forest in Kenya are presented. An overall accuracy for change detection of 88.4%, corresponding to kappa of 0.8265, was obtained. The methodology is able to predict the change information a-posteriori as opposed to the conventional methods that require land cover classes a priori for change detection. Most importantly, the approach can be used to predict the existence, location and extent of disturbances within natural environmental systems.