Hyperspectral Image Classification Method Based on 3D Lightweight Deep Network

Abstract

A hyperspectral image classification method based on a three-dimensional lightweight deep network proposed by the present invention achieves autonomous extraction of hyperspectral image depth features and high-precision classification under limited sample conditions by constructing a three-dimensional lightweight deep network. Compared with the existing hyperspectral image classification method based on deep learning, the present invention has a deeper network model, higher precision, and fewer parameters.

Description

technical field [0001] The invention relates to a hyperspectral image classification method based on a three-dimensional lightweight deep network, belonging to the field of remote sensing image processing. Background technique [0002] Hyperspectral images contain both spectral information and spatial information, and have important applications in military and civilian fields. However, the high-dimensional characteristics of hyperspectral images, high correlation between bands, and spectral mixing make hyperspectral image classification face great challenges. In recent years, with the emergence of new technologies in deep learning, deep learning-based hyperspectral image classification has achieved breakthroughs in both methods and performance. However, deep learning has many model parameters and requires a large number of training samples. In deep learning related technologies, generally speaking, effectively increasing the depth of the network is very important to impro...

AI Technical Summary

Problems solved by technology

However, limited by the number of hyperspectral image training samples, the deep learning model applied to hyperspectral image classification is relatively shallow, although a large number of experiments in computer vision have shown that effectively increasing the depth is very beneficial for improving classification performance

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