Data compression method for quantitative remote sensing with unmanned aerial vehicle

Abstract

The present disclosure provides a data compression method for quantitative remote sensing with an unmanned aerial vehicle. The method performs preprocessing on a multispectral image acquired by an unmanned aerial vehicle, successively performs a three-dimensional convolution and a two-dimensional convolution on the multispectral image by an encoder to obtain deep feature information, performs quantizing and entropy encoding on the deep feature information, optimally distributes a loss and a code rate of the image through end-to-end joint training to obtain an optimal compressed image, and reconstructs the optimal compressed image by using a decoder. Image reconstruction quality and a compression ratio are improved by performing a plurality of convolutions on a multispectral pattern; quantizing and entropy encoding are performed on the convoluted deep feature information, to remove redundancy in a feature image, so as to improve the image reconstruction quality and the compression ratio.

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