Abstract: Shift-invariant wavelet transform with properly selected wavelet base and decomposition level(s), is used to characterize rough-wavelet granules producing wavelet granulation of a feature space for a multispectral image such as a remote sensing image. Through the use of the granulated feature space contextual information in time and/or frequency domains are analyzed individually or in combination. Neighborhood rough sets (NRS) are employed in the selection of a subset of granulated features that further explore the local and/or contextual information from neighbor granules.

Abstract: Provided embodiments include a method, a system, a device, and an article of manufacture. A system for terminating a genetic algorithm (GA), where the GA uses an iterator and generates one best solution per iteration, includes a memory, an iterative processor, and a terminating processor. The memory is provided for storing a plurality of best solutions generated in a plurality of iterations of the GA. One of the best solutions generated in one of the iterations is stored in the memory if the one of the best solutions is better than a previous one of the best solutions generated in a previous one of the iterations. The iterative processor computes a variance of the plurality of the best solutions stored in the memory. The terminating processor terminates the iterator when the variance is less than or equal to a predetermined threshold.

Abstract: Shift-invariant wavelet transform with properly selected wavelet base and decomposition level(s), is used to characterize rough-wavelet granules producing wavelet granulation of a feature space for a multispectral image such as a remote sensing image. Through the use of the granulated feature space contextual information in time and/or frequency domains are analyzed individually or in combination. Neighborhood rough sets (NRS) are employed in the selection of a subset of granulated features that further explore the local and/or contextual information from neighbor granules.

Abstract: Provided embodiments include a method, a system, a device, and an article of manufacture. A system for terminating a genetic algorithm (GA), where the GA uses an iterator and generates one best solution per iteration, includes a memory, an iterative processor, and a terminating processor. The memory is provided for storing a plurality of best solutions generated in a plurality of iterations of the GA. One of the best solutions generated in one of the iterations is stored in the memory if the one of the best solutions is better than a previous one of the best solutions generated in a previous one of the iterations. The iterative processor computes a variance of the plurality of the best solutions stored in the memory. The terminating processor terminates the iterator when the variance is less than or equal to a predetermined threshold.