This paper develops a novel multitemporal spectral unmixing (MSU) approach for addressing the challenging multiple-change detection problem in bi-temporal hyperspectral (HS) images. Differently from state-of-the-art techniques that mainly perform at a pixel level, the proposed MSU approach investigates the spectral-temporal variations at a subpixel level. A multitemporal spectral mixture model is defined to analyze the spectral composition within a pixel. Distinct multitemporal endmembers (MT-EMs) are extracted and employed for distinguishing change and no-change MT-EMs in the unmixing model. The CD problem is solved by analyzing the abundances of the unique change and no-change multitemporal endmembers and their contribution to each pixel. Experimental results obtained on multitemporal Hyperion HS images confirmed the effectiveness of the proposed method.
Multitemporal spectral unmixing for change detection in hyperspectral images
Bovolo, Francesca;
2015-01-01
Abstract
This paper develops a novel multitemporal spectral unmixing (MSU) approach for addressing the challenging multiple-change detection problem in bi-temporal hyperspectral (HS) images. Differently from state-of-the-art techniques that mainly perform at a pixel level, the proposed MSU approach investigates the spectral-temporal variations at a subpixel level. A multitemporal spectral mixture model is defined to analyze the spectral composition within a pixel. Distinct multitemporal endmembers (MT-EMs) are extracted and employed for distinguishing change and no-change MT-EMs in the unmixing model. The CD problem is solved by analyzing the abundances of the unique change and no-change multitemporal endmembers and their contribution to each pixel. Experimental results obtained on multitemporal Hyperion HS images confirmed the effectiveness of the proposed method.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
