Research on Lossless Compression of Hyperspectral Images Based on Improved Deep Learning Algorithm

Abstract

In order to improve the detection and recognition ability of multiscale hyperspectral images with weak edges, a lossless compression algorithm of multiscale hyperspectral images with weak edges based on improved deep learning is proposed. Using deep-level feature information extraction method to extract fine-grained features of cancer cells from multiscale hyperspectral images with weak edges, constructing 3D surface structure reconstruction model of multiscale hyperspectral images with weak edges, combining with variational Bayesian filter detection method to decompose multiscale color structure features of multiscale hyperspectral images with weak edges, and constructing gradually refined output structure model of multiscale hyperspectral images with weak edges, Fuzzy image coarse-scale neural network learning method is used to realize block matching and edge detection of multi-scale hyperspectral images with weak edges. Ultra-fine-grained features of multi-scale hyperspectral images with weak edges are extracted. Deep learning algorithm is used to adaptively optimize the lossless compression process of multi-scale hyperspectral images with weak edges, and the optimal design of lossless compression algorithm for multi-scale hyperspectral images with weak edges is realized. The simulation results show that the adaptive convergence of lossless compression of multi-scale weak edge hyperspectral images with this method is good, the feature resolution is strong, and the hyperspectral feature detection and recognition ability is good.