Xiong Xu

TL;DR: An adaptive subpixel mapping method based on a maximum a posteriori (MAP) model and a winner-take-all class determination strategy, namely, AMCDSM, is proposed for hyperspectral remote sensing imagery to improve the accuracy and regularize the ill-posed subpixels mapping problem.

TL;DR: A sub-pixel mapping framework based on a maximum a posteriori (MAP) model is proposed to utilize the complementary information of multiple shifted images to improve the accuracy of sub- pixel mapping for hyperspectral imagery.

TL;DR: An adaptive subpixel mapping framework based on a multiagent system for remote-sensing imagery is proposed and experimental results indicate that the proposed algorithm outperforms the other two subpixels mapping algorithms in reconstructing the different structures in mixed pixels.

TL;DR: The results indicate that the proposed AsWM was able to detect water pixels more efficiency than other unsupervised water extraction methods, and the water fractions estimated by the proposed ASWM method correspond closely to the reference fractions with the slopes of 0.97, 1.04, and 0.98.

TL;DR: This review introduces the principal laws underlying these methods, presents a survey of the existing subpixel methods calculated both in the spatial domain and in the frequency domain, and summarizes the major applications from three aspects, and discusses the challenges and possible directions of future research.