端元
高光谱成像
计算机科学
人工智能
深度学习
卷积神经网络
Python(编程语言)
子空间拓扑
数据集
丰度估计
单纯形
模式识别(心理学)
数学
丰度(生态学)
生物
操作系统
渔业
几何学
作者
Behnood Rasti,Bikram Koirala,Paul Scheunders,Pedram Ghamisi
出处
期刊:IEEE Transactions on Geoscience and Remote Sensing
[Institute of Electrical and Electronics Engineers]
日期:2022-01-01
卷期号:60: 1-15
被引量:58
标识
DOI:10.1109/tgrs.2021.3067802
摘要
In this article, we introduce a deep learning-based technique for the linear hyperspectral unmixing problem. The proposed method contains two main steps. First, the endmembers are extracted using a geometric endmember extraction method, i.e., a simplex volume maximization in the subspace of the data set. Then, the abundances are estimated using a deep image prior. The main motivation of this work is to boost the abundance estimation and make the unmixing problem robust to noise. The proposed deep image prior uses a convolutional neural network to estimate the fractional abundances, relying on the extracted endmembers and the observed hyperspectral data set. The proposed method is evaluated on simulated and three real remote sensing data for a range of SNR values (i.e., from 20 to 50 dB). The results show considerable improvements compared to state-of-the-art methods. The proposed method was implemented in Python (3.8) using PyTorch as the platform for the deep network and is available online: https://github.com/BehnoodRasti/UnDIP .
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