Hyperspectral Classification Through Unmixing Abundance Maps Addressing Spectral Variability

Abstract

Climate change and anthropogenic pressure are causing an indisputable decline in biodiversity; therefore, the need of environmental knowledge is important to develop the appropriate management plans. In this context, remote sensing and, specifically, hyperspectral imagery (HSI) can contribute to the generation of vegetation maps for ecosystem monitoring.To properly obtain such information and to address the mixed pixels inconvenience, the richness of the hyperspectral data allows the application of unmixing techniques. In this sense, a problem found by the traditional linear mixing model (LMM), a fully constrained least squared unmixing (FCLSU), is the lack of ability to account for spectral variability. This paper focuses on assessing the performance of different spectral unmixing models depending on the quality and quantity of endmembers. A complex mountainous ecosystem with high spectral changes was selected. Specifically, FCLSU and 3 approaches, which consider the spectral variability, were studied: scaled constrained least squares unmixing (SCLSU), Extended LMM (ELMM) and Robust ELMM (RELMM). The analysis includes two study cases: 1) robust endmembers and 2) nonrobust endmembers. Performances were computed using the reconstructed root-mean-square error (RMSE) and classification maps taking the abundances maps as inputs. It was demonstrated that advanced unmixing techniques are needed to address the spectral variability to get accurate abundances estimations. RELMM obtained excellent RMSE values and accurate classification maps with very little knowledge of the scene and minimum effort in the selection of

Combining Spectral Unmixing and 3D/2D Dense Networks with Early-Exiting Strategy for Hyperspectral Image Classification

“…Firstly, in our framework, the spectral signatures used for unmixing purposes are not obtained by endmember extraction but are obtained by averaging the spectral signatures of each labeled category in the training set. Although the average endmembers will cause a decrease in spectral purity, it can reduce the effects of noise and/or average the subtle spectral variability of each spectral category, resulting in a more representative final endmember as a whole [10,13].…”

“…Later, Dópido et al [11] quantitatively evaluated the unmixing-based feature extraction methods, and further proved that these features can effectively improve the accuracy of classification. This strategy was further explored in many works [12,13] and also proved that the unmixing before classification provided an effective solution for HSI classification. Secondly, several techniques are proposed to utilize the complementarity of the classification and spectral unmixing in a semi-supervised framework, where the abundance maps have been applied as a supplementary source for the multinomial logistic regression (MLR) classifier [14][15][16][17].…”

“…Generally, these algorithms can be divided into two groups. Firstly, spectral unmixing has been widely studied as a feature extraction strategy before classification [10][11][12][13]. For instance, in Reference [10], unmixing results is used to improve classification performance in an alternative strategy and spectral unmixing can be used to extract suitable features for future classify images.…”

A Detail-Preserving Cross-Scale Learning Strategy for CNN-Based Pansharpening

“…In addition, it is also worth mentioning the matrix factorization approaches, examples are References [38][39][40], which are more suited to the fusion of low resolution hyperspectral images with high resolution multispectral ones. In this case, in fact, the spectral variability becomes a serious concern to be handled carefully by means of unmixing oriented methodologies [41][42][43].…”

Spectral Unmixing: A Derivation of the Extended Linear Mixing Model From the Hapke Model

“…For experiments on the capability of the ELMM to explain variability with real data acquired in various contexts, we refer e.g. to [8], [14], [15].…”