Hyperspectral unmixing algorithm based on denoised three-dimensional convolutional self-encoding network

Abstract

The invention discloses a hyperspectral unmixing algorithm based on a de-noising three-dimensional convolutional self-encoding network, and solves the problems that the traditional end member extraction algorithm cannot quickly and synchronously obtain end member spectrum and abundance information, is easily interfered and is poor in robustness. The method comprises the following steps: 1, designing and establishing a three-dimensional convolutional self-encoding network based on a hyperspectral volume data structure; 2, training a network by using a denoising self-encoding algorithm and a generated data set, and extracting robust space-spectrum joint features; and 3, designing and realizing a non-negative sparse auto-encoder, adding non-negative and sparse constraints to abundance, and mapping data to a low-dimensional signal subspace so as to synchronously obtain high-precision end member and abundance information. The basic idea of the method is that the deep self-encoding network is adopted to extract the spatial-spectral joint features, the end member and abundance information of the image is synchronously obtained in an unsupervised mode, the unmixing precision is high, the method is suitable for hyperspectral end member extraction and abundance inversion application such as urban remote sensing, precision agriculture and exploration and investigation, and the social andeconomic value is high.

Description

technical field [0001] The invention relates to an unmixing method for endmember extraction and category abundance calculation of hyperspectral data, in particular to an unsupervised unmixing method for hyperspectral pixels based on a denoising three-dimensional convolutional self-encoding network. Background technique [0002] Hyperspectral remote sensing imaging combines two-dimensional spatial imaging technology with spectral imaging, and can finely measure the radiation information of ground features in hundreds of continuous spectral bands in the visible and near-infrared bands, thereby obtaining a three-dimensional spectral data cube with integrated maps . Hyperspectral images play an important role in various fields of military and civilian use. However, due to the limitation of imaging technology, the spatial resolution of spectral imager is relatively high, which results in the generation of mixed pixels. This brings difficulties to the fine classification of obje...

AI Technical Summary

Problems solved by technology

[0005] The hyperspectral unmixing network based on self-encoding only considers the spectral feature information of pixels, and does not take advantage of the spatial distribution characteristics of endmembers in the image

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