Hyperspectral image migration classification based on depth joint distributed adaptive network

Abstract

A hyperspectral image migration classification based on a depth joint distribution adaptive network includes such steps as inputting hyperspectral images in source domain and target domain, normalizing features and unifying dimensions; combining features of hyperspectral images in source domain and target domain; The edge probability distribution adaptation network is constructed to adapt the edgeprobability distribution of hyperspectral images in source domain and target domain. According to the principle of one-to-many classification, the training samples of hyperspectral images in source domain and target domain are selected. A conditional probability distribution adaptation network is constructed to adapt the conditional probability distribution of the hyperspectral images in the source domain and the target domain. One-to-many classification of hyperspectral images in target domain is performed. A depth-based joint distribution adaptation network is proposed, which realizes feature adaptation of a source domain and a target domain hyperspectral image, and reduces that joint probability distribution difference between the source domain and the target domain. At the same time,one-to-many classification model is used to improve the intra-class and inter-class discrimination, and then the accuracy of hyperspectral image migration classification is improved.

Description

technical field [0001] The invention belongs to the technical field of remote sensing image processing, in particular to a hyperspectral image migration classification based on a deep joint distribution adaptation network. Background technique [0002] Hyperspectral images have high spectral resolution, wide band coverage, and rich spectral details of ground objects, which are conducive to fine ground object analysis. Hyperspectral image classification is an important part of hyperspectral image interpretation, and it is widely used in mineral exploration, vegetation survey, agricultural monitoring and other fields. Due to the large amount of hyperspectral image data and redundancy, the same target has spectral differences in different data, which affects the classification effect. [0003] Hyperspectral image classification is the process of analyzing and classifying the acquired target spectral signal and other features, and mainly adopts the method of supervised classifi...

AI Technical Summary

Problems solved by technology

The above method does not analyze the joint probability distribution of the hyperspectral image in the source domain and the target domain, and does not effectively use the joint probability distribution of the data to further improve the migration classification effect

Images