端元
高光谱成像
自编码
人工智能
模式识别(心理学)
计算机科学
像素
卷积神经网络
背景(考古学)
特征提取
图像(数学)
计算机视觉
特征(语言学)
深度学习
地理
哲学
语言学
考古
作者
Yasiru Ranasinghe,S. Herath,H.M.H.K. Weerasooriya,M. P. B. Ekanayake,Roshan Godaliyadda,Parakrama Ekanayake,Vijitha Herath
标识
DOI:10.1109/iciis51140.2020.9342727
摘要
In the remote sensing context spectral unmixing is a technique to decompose a mixed pixel into two fundamental representatives: endmembers and abundances. In this paper, a novel architecture is proposed to perform blind unmixing on hyperspectral images. The proposed architecture consists of convolutional layers followed by an autoencoder. The encoder transforms the feature space produced through convolutional layers to a latent space representation. Then, from these latent characteristics the decoder reconstructs the roll-out image of the monochrome image which is at the input of the architecture; and each single-band image is fed sequentially. Experimental results on real hyperspectral data concludes that the proposed algorithm outperforms existing unmixing methods at abundance estimation and generates competitive results for endmember extraction with RMSE and SAD as the metrics, respectively.
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