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The objective of this paper is to utilize the extracted features obtained by the wavelet transform (WT) rather than the original multispectral features of remote-sensing images for land-cover classification. WT provides the spatial and spectral characteristics of a pixel along with its neighbors, and hence, this can be utilized for an improved classification. Four classifiers, namely, the fuzzy product aggregation reasoning rule (FPARR), fuzzy explicit, multilayered perceptron, and neuro-fuzzy (NF), are used for this purpose. The performance is tested on multispectral real and synthetic images. The performance of original and wavelet-feature (WF)-based methods is compared. The WF-based methods have consistently yielded better results. Biorthogonal3.3 (Bior3.3) wavelet is found to be superior to other wavelets. FPARR along with the Bior3.3 wavelet outperformed all other methods. Results are evaluated using quantitative indexes like [beta] and Xie-Beni. Index Terms--Fuzzy, land-cover classification, neural and neuro-fuzzy (NF) classification, remote sensing, wavelet transform (WT).

In this paper, we propose a context-sensitive technique for unsupervised change detection in multitemporal remote sensing images. This technique is based on a modified Hopfield neural network architecture designed to model spatial correlation between neighboring pixels of the difference image produced by comparing images acquired on the same area at different times. Each spatial position in the considered scene is represented by a neuron in the Hopfield network that is connected only to its neighboring units. These connections model the spatial correlation between neighboring pixels and are associated with a context-sensitive energy function that represents the overall status of the network. Change detection maps are obtained by iteratively updating the output status of the neurons until a minimum of the energy function is reached and the network assumes a stable state. A simple heuristic thresholding procedure is presented and adopted for initializing the network. The proposed change detection technique is unsupervised and distribution free. Experimental results carried out on two multispectral and multitemporal remote sensing images confirm the effectiveness of the proposed technique. Index Terms--Change detection, context-sensitive image analysis, Hopfield neural network, multitemporal images, remote sensing, thresholding.

The water molecules associated with coal play a significant role in spontaneous ignition and combustion of coal. This paper deals with studies to evaluate the impact of moisture content on crossing point temperature (CPT) and ignition point temperature (IPT) of some low-rank coal and lignite after various stages of drying. Experimental data suggest (i) existence of critical moisture content value determining the propensity of a particular coal toward self-heating and (ii) content of moisture and nature of oxygen-bearing functional groups to be the most influencing factors in self-heating. The auto-oxidation cum self-heating characteristics of coal become more conducive with increase in the ratio of quantity of hydroxyl to carboxyl functional groups. The FTIR studies further corroborate that the initial stages of auto-oxidation cum self-heating occur presumably at the surface of the coal. [ABSTRACT FROM AUTHOR]

An investigation on the robustness (or ruggedness) of neural network (NN) based information processing systems with respect to component failure (damaging of nodes/links) is done. The damaging/component failure process has been modeled as a Poisson process. To choose the instants or moments of damaging, statistical sampling technique is used. The nodes/links to be damaged are determined randomly. As an illustration, the model is implemented and tested on different object extraction algorithms employing Hopfield's associative memory model, Gibbs random fields, and a self-organizing multi-layer neural network. The performance (hence robustness of the underlying network model) of these algorithms is evaluated in terms of percentage of pixels correctly classified under different noisy environments and different degrees and sequences of damaging. The deterioration in the output is seen to be very small even when a large number of nodes/links are damaged.