考虑光谱信息和超像素分割的高光谱解混网络

谢金凤; 陈涛

doi:10.11834/jrs.20232587

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专辑

考虑光谱信息和超像素分割的高光谱解混网络
Hyperspectral unmixing network considering spectral information and superpixel segmentation
2024年28卷第1期页码：142-153
纸质出版日期： 2024-01-07 ，
DOI： 10.11834/jrs.20232587
稿件说明：

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谢金凤，陈涛.2024.考虑光谱信息和超像素分割的高光谱解混网络.遥感学报，28（1）： 142-153

Xie J F and Chen T. 2024. Hyperspectral unmixing network considering spectral information and superpixel segmentation. National Remote Sensing Bulletin， 28（1）：142-153
谢金凤，陈涛.2024.考虑光谱信息和超像素分割的高光谱解混网络.遥感学报，28（1）： 142-153 DOI： 10.11834/jrs.20232587.

Xie J F and Chen T. 2024. Hyperspectral unmixing network considering spectral information and superpixel segmentation. National Remote Sensing Bulletin， 28（1）：142-153 DOI： 10.11834/jrs.20232587.

摘要

在高光谱解混的过程中考虑影像的空间信息，能够有效提高解混精度。而超像素分割能够划分空间同质区域，为此本文提出一种考虑光谱信息和超像素分割的解混网络（SSUNet）。首先需对原始影像进行超像素分割处理，获得具有空间特征的超像素分割数据，然后采用SSUNet对原始高光谱数据和超像素分割数据进行训练和解混。在线性和非线性混合模型生成的模拟数据集和两个真实数据集上的实验表明，与SUnSAL、SUnSAL-TV、SCLRSU、MTAEU、EGU-Net-pw和1DCNN的解混结果相比，所提网络具有更高的解混精度和较好的鲁棒性。

Abstract

The pixel is the basic unit of remote sensing images. If a single pixel contains multiple types of covering ground objects

then it is called a mixed pixel. Hyperspectral unmixing aims to decompose the mixed pixels into several basic component units (endmembers) and obtain the proportion (abundance) of each endmember

which can improve the accuracy of remote sensing image classification and subpixel level target detection. Thus

research on this method promotes the development of hyperspectral remote sensing technology. Studies show that considering the spatial information in the process of hyperspectral unmixing can effectively improve the unmixing accuracy. However

most of the nonlinear unmixing networks based on deep learning only use the spectral information of images.

A hyperspectral unmixing network considering spectral information and superpixel segmentation (SSUNet) is proposed on the basis of the supervised unmixing idea and one-dimensional convolutional neural network to maximize the spectral and spatial information of images. First

the original hyperspectral data should be processed using superpixel segmentation to obtain the superpixel segmentation data with spatial characteristics. Then

SSUNet is used to train and unmix the original hyperspectral and superpixel segmentation data. The loss function adds regularization constraint term based on the root mean square error to promote the sparsity of the unmixing abundance and generate closer unmixing results to the real value. The activation function of the network output layer is softmax

which yields output values of each output node within the range of [0

1] and constrains their sum to 1

thus satisfying the two constraints of unmixing: the abundance nonnegative constraint and abundance sum-to-one constraint.

Experiments on simulated datasets generated by the linear and nonlinear mixed models and the two real datasets show that the proposed network has higher unmixing accuracy and better robustness than the unmixing results of SUnSAL

SUnSAL-TV

SCLRSU

MTAEU

EGU-Net-pw

and 1DCNN. Three Gaussian noises with different SNR levels (20

and 40 dB) are added to the simulated dataset. The proposed network can achieve the best unmixing results at all SNR levels

and the network also achieves high unmixing accuracy with the increase in SNR. In addition

the influence of the change of w value on the unmixing result of the simulated datasets under different SNR is verified. The experimental results show that when the value range of w is [3

13]

the RMSE value does not change substantially

and the best value of w is 5. Experiments on real datasets show that SSUNet can still achieve the best unmixing results in complex real scenes.

The SSUNet network uses the dual-branch structure to mine the features of the original image data and the superpixel segmentation data with spatial features. This network also utilizes the fusion layer to fuse the features and improve the unmixing accuracy of the model. Experiments on simulated and real hyperspectral datasets show that the proposed network has high accuracy.

关键词

高光谱图像高光谱解混光谱和空间信息超像素分割深度学习卷积神经网络

Keywords

hyperspectral imageshyperspectral unmixingspectral and spatial informationsuperpixel segmentationdeep learningconvolutional neural network

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