2024/02/05 by Saeid Mehrdad, Mehrdad, Saeid, Seyed AmirHossein Janani +1
Engineering · #Advanced Image Fusion Techniques #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #FOS: Electrical engineering #Image and Video Processing (eess.IV) #Infrared Target Detection Methodologies #Machine Learning (cs.LG) #Neural and Evolutionary Computing (cs.NE) #Remote-Sensing Image Classification #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2402.03398
openalex publication_date 2024/02/05 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Nonlinear hyperspectral unmixing has recently received considerable attention, as linear mixture models do not lead to an acceptable resolution in some problems. In fact, most nonlinear unmixing methods are designed by assuming specific assumptions on the nonlinearity model which subsequently limits the unmixing performance. In this paper, we propose an unsupervised nonlinear unmixing approach based on deep learning by incorporating a general nonlinear model with no special assumptions. This model consists of two branches. In the first branch, endmembers are learned by reconstructing the rows of hyperspectral images using some hidden layers, and in the second branch, abundance values are learned based on the columns of respective images. Then, using multi-task learning, we introduce an auxiliary task to enforce the two branches to work together. This technique can be considered as a regularizer mitigating overfitting, which improves the performance of the total network. Extensive experiments on synthetic and real data verify the effectiveness of the proposed method compared to some state-of-the-art hyperspectral unmixing methods.