Method for selecting high-spectrum remote-sensing image wave band

Abstract

The invention discloses a method for selecting high-spectrum remote-sensing image wave band based on hybrid binary particle swarm optimization differential evolution (HBPSODE). The method comprises the steps of preprocessing an original high-spectrum remote-sensing image; initializing double swarm individual and algorithm parameters; iterating the double swarms in parallel by the HBPSODE; transferring the optimal solution information through the swarm; calculating the classification precision through an SVM classifier to be used as an adaptability value; updating evolution until reaching the specified evolution times or reaching the maximum precision.

Description

technical field [0001] The present invention relates to a hyperspectral remote sensing image band selection method, specifically a hyperspectral remote sensing image band selection method based on a packaged HBPSODE-SVM algorithm based on mixed binary particle swarm differential evolution and aiming at obtaining the optimal band combination , belonging to the technical field of hyperspectral remote sensing image processing. Background technique [0002] Remote Sensing (Remote Sensing) is a technology that uses the principle of electromagnetic waves to obtain distant signals and image them, and can feel and perceive distant things remotely. It is a new science. With the improvement of computer technology and optical technology, remote sensing technology has also been developed rapidly. In recent years, a variety of remote sensing satellites have been successfully launched, which has promoted the remote sensing data acquisition technology towards three high (high spatial reso...

AI Technical Summary

Problems solved by technology

However, both algorithms have some defects. In the initial stage of population iteration, the individuals maintain a high diversity. With the increase of the number of iterations, the individuals in the PSO algorithm gradually approach the optimal particle in the population, and the DE algorithm is performing selection. The greedy strategy is adopted in the operation, that is, only when the mutant individual has a better fitness function value than the current individual can it participate in the next iterative evolution

Although these evolutionary mechanisms can speed up the convergence speed of the algorithm, they also make the differences between individuals in the population gradually narrow, and the diversity of the population also decreases. no change

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