In the community of remote sensing, nonlinear mixing models have recently received particular attention in hyperspectral image processing. In this paper, we present a novel nonlinear spectral unmixing method following the recent multilinear mixing model of [1], which includes an infinite number of terms related to interactions between different endmembers. The proposed unmixing method is unsupervised in the sense that the endmembers are estimated jointly with the abundances and other parameters of interest, i.e., the transition probability of undergoing further interactions. Non-negativity and sum-to one constraints are imposed on abundances while only nonnegativity is considered for endmembers. The resulting unmixing problem is formulated as a constrained nonlinear optimization problem, which is solved by a block coordinate descent strategy, consisting of updating the endmembers, abundances and transition probability iteratively. The proposed method is evaluated and compared with linear unmixing methods for synthetic and real hyperspectral datasets acquired by the AVIRIS sensor. The advantage of using non-linear unmixing as opposed to linear unmixing is clearly shown in these examples.
Cross-Attention in Coupled Unmixing Nets for Unsupervised Hyperspectral Super-Resolution
Remote Sensing Technology Institute, German Aerospace Center（德国航空航天中心遥感技术研究所）；Technical University of Munich（慕尼黑工业大学）；Univ. Grenoble Alpes, INRIA, CNRS, Grenoble INP, LJK（格勒诺布尔阿尔卑斯大学、法国国家信息与自动化研究所、法国国家科学研究中心、格勒诺布尔理工学院、LJK实验室）；Macau University of Science and Technology（澳门科技大学）
Multi-Band Image Fusion Based on Spectral Unmixing
University of Cambridge（剑桥大学）；Instituto de Telecomunicações（电信研究所）；Instituto Superior Técnico（高等技术学院）；Universidade de Lisboa（里斯本大学）；IRIT/INP-ENSEEIHT（图卢兹国立高等电子技术、电子、计算机、水力学与电信学院）；University of Toulouse（图卢兹大学）；Nanyang Technological University（南洋理工大学）
Transformer for Multitemporal Hyperspectral Image Unmixing
College of Computer Science, Nankai University（南开大学计算机学院）；Haihe Lab（海河实验室）；School of Computer Science and Technology, Tiangong University（天津工业大学计算机科学与技术学院）；School of Astronautics, Beihang University（北京航空航天大学宇航学院）
Coupled Convolutional Neural Network with Adaptive Response Function Learning for Unsupervised Hyperspectral Super-Resolution
Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences（中国科学院空天信息创新研究院数字地球科学重点实验室）；Flemish Institute for Technological Research (VITO)（弗拉芒技术研究院）；IMEC Research Group, Ghent University（根特大学IMEC研究组）；Remote Sensing Technology Institute (IMF), German Aerospace Center (DLR)（德国航空航天中心遥感技术研究所）
