A novel sequential spectral change vector analysis for representing and detecting multiple changes in hyperspectral images

摘要

This paper focuses on a challenging task for representing and detecting multiple changes in multitemporal hyperspectral images. To this aim, a novel Sequential Spectral Change Vector Analysis (SCVA) method is proposed that extends the use of the popular CVA method [1]. The proposed SCVA approach is designed in a sequential and semiautomatic fashion, where a fully automatic 2-D change representation and an interactive change identification are included at each level of the processing, exploiting the multiple change information hierarchically. In particular, an adaptive reference vector scheme is developed to drive the change representation, and thus the sequential analysis, by following a top-down structure. Changes are represented and separated according to their spectral change significance. Experimental results obtained on multitemporal Hyperion images confirm the effectiveness of the proposed method.