Abstract
Spectral unmixing and sub-pixel mapping have been used to estimate the proportion and spatial distribution of the different land-cover classes in mixed pixels at a sub-pixel scale. In the past decades, several algorithms were proposed in both categories; however, these two techniques are generally regarded as independent procedures, with most sub-pixel mapping methods using abundance maps generated by spectral unmixing techniques. It should be noted that the utilized abundance map has a strong impact on the performance of the subsequent sub-pixel mapping process. Recently, we built a novel sub-pixel mapping model in combination with the linear spectral mixture model. Therefore, a joint sub-pixel mapping model was established that connects an original (coarser resolution) remotely sensed image with the final sub-pixel result directly. However, this approach focuses on incorporating the spectral information contained in the original image without addressing the spectral endmember variability resulting from variable illumination and environmental conditions. To address this important issue, in this paper we designed a new joint sparse sub-pixel mapping method under the assumption that various representative spectra for each endmember are known a priori and available in a library. In addition, the total variation (TV) regularization was also adopted to exploit the spatial information. The proposed approach was experimentally evaluated using both synthetic and real hyperspectral images, and the obtained results demonstrate that the method can achieve better results by considering the impact of endmember variability when compared with other sub-pixel mapping methods.
Highlights
Mixed pixels are frequent in remotely sensed images due to coarse spatial resolution
Many efforts have been directed towards the development of sub-pixel mapping techniques aimed at obtaining a finer classification map from a lower spatial resolution image [6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23], such as the pixel-swapping algorithm [6,7], the Hopfield neural network [8,9], the spatial attraction model [10,11,12], genetic algorithms [13,14], multi-agent systems [15], maximum a posteriori (MAP)
The OA increases from 85.54% to 92.39% for the joint sparse sub-pixel mapping (JSSM) compared with the Markov random field (MRF) which results from the consideration of endmember variability
Summary
Mixed pixels are frequent in remotely sensed images due to coarse spatial resolution. Procedure that the abundance map with higher accuracy always generates a better sub-pixel mapping result This deduction needs to be further verified and it is meaningful to develop sub-pixel mapping algorithms which act on the original remotely sensed images directly. A joint sparse sub-pixel mapping method (JSSM) was designed to combine the procedures of spectral unmixing and sub-pixel mapping together by introducing a so-called sub-pixel abundance map, which indicates the proportions of sub-pixels belonging to different land cover classes In this way, the original remotely sensed image and the final sub-pixel map can be connected without the need for intermediate abundance maps, and the propagation of errors in the model can be mitigated. The paper with some with remarks and hints atand plausible research lines
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