Sub-Pixel Mapping Model Based on Total Variation Regularization and Learned Spatial Dictionary

Abstract

In this research study, we deal with remote sensing data analysis over high dimensional space formed by hyperspectral images. This task is generally complex due to the large spectral, spatial richness, and mixed pixels. Thus, several spectral un-mixing methods have been proposed to discriminate mixing spectra by estimating the classes and their presence rates. However, information related to mixed pixel composition is very interesting for some applications, but it is insufficient for many others. Thus, it is necessary to have much more data about the spatial localization of the classes detected during the spectral un-mixing process. To solve the above-mentioned problem and specify the spatial location of the different land cover classes in the mixed pixel, sub-pixel mapping techniques were introduced. This manuscript presents a novel sub-pixel mapping process relying on K-SVD (K-singular value decomposition) learning and total variation as a spatial regularization parameter (SMKSVD-TV: Sub-pixel Mapping based on K-SVD dictionary learning and Total Variation). The proposed approach adopts total variation as a spatial regularization parameter, to make edges smooth, and a pre-constructed spatial dictionary with the K-SVD dictionary training algorithm to have more spatial configurations at the sub-pixel level. It was tested and validated with three real hyperspectral data. The experimental results reveal that the attributes obtained by utilizing a learned spatial dictionary with isotropic total variation allowed improving the classes sub-pixel spatial localization, while taking into account pre-learned spatial patterns. It is also clear that the K-SVD dictionary learning algorithm can be applied to construct a spatial dictionary, particularly for each data set.

Forest Cover Change Monitoring Using Sub-Pixel Mapping with Edge-Matching Correction

“…At present, most of the research on forest cover monitoring is still at the image level and pixel-level, and due to the limitation of its own sensor, there are a large number of mixed pixels in remote sensing images, which makes the refinement of forest cover monitoring greatly limited [54,55]. Sub-pixel mapping technology is mainly applied to the accurate monitoring of land classes in remote sensing images, such as land cover [56][57][58][59], water boundaries [60,61], impermeable surfaces [62,63], etc. It can quantitatively solve the problem of mixed pixels and improve the spatial resolution of monitoring results.…”

Development of a Novel Burned-Area Subpixel Mapping (BASM) Workflow for Fire Scar Detection at Subpixel Level

“…Then, we downloaded the pre-and post-forest fire Sentinel 2 data sets and processed them. Second, burned area fraction was derived from the four selected Multispectral Instrument (MSI)/Sentinel 2 bands listed in Table 2 using the FCLS method [44,59,60]. Taking burned area fraction as input for the Modified Pixel Swapping Algorithm (MPSA) in Visual Studio 2012 platform edited by C# language, we obtained the burned area at subpixel level as output.…”

Editorial for the Special Issue “New Advances on Sub-Pixel Processing: Unmixing and Mapping Methods”

“…In this Special Issue, several stages of sub-pixel image processing are approached incorporating advanced techniques such as neural networks, deep learning, and probabilistic non-Gaussian mixture models. This Special Issue consists of nine research papers [1][2][3][4][5][6][7][8][9]. All the methods proposed in the papers were validated using real hyperspectral data and benchmarked with state-of-the-art methods, thus comprehensively demonstrating the theoretical and practical contributions of the papers.…”

“…All the methods proposed in the papers were validated using real hyperspectral data and benchmarked with state-of-the-art methods, thus comprehensively demonstrating the theoretical and practical contributions of the papers. The first paper contains a sub-pixel mapping model based on spatial regularization using the total variation and a dictionary of pre-learned spatial patterns for improving the class sub-pixel spatial localization [1]. Thus, the sub-pixel mapping problem is transformed into a regularization problem integrating the isotropic total variation minimization as a prior model applied to the abundance maps.…”