Change detection in hyperspectral and radar/multispectral images using an unmixing-based multivariate manifold estimation

Change detection (CD) in remote sensing images is an important problem requiring the analysis of high-dimensional data such as radar, multispectral, and hyperspectral images. Solutions for radar and multispectral data are available in the literature. However, the use of hyperspectral images is still an area of improvement, not only because the existing methods were designed to handle a reduced number of spectral bands but also because they do not fully exploit the rich spectral information available for CD. Additionally to the use of hyperspectral data, the interest in multimodal CD is that different sensors, such as optical and synthetic aperture radar (SAR), can contribute with essential features improving the generation of high-quality change maps. This paper addresses the multimodal CD problem using hyperspectral and optical/SAR images or pairs of hyperspectral images using a multivariate manifold estimation model based on spectral unmixing. The estimated abundance maps associated with hyperspectral images are used as inputs to the CD strategy, instead of the original hyperspectral data enabling significant dimensionality reduction. Several simulations demonstrate the effectiveness of the proposed approach, achieving, for instance, overall accuracy values above 91%, AUC values up to 0.95, and consistently higher recall compared to competing methods. These results highlight the strong performance of the proposed method while preserving its flexibility to handle any combination of radar, multispectral, and hyperspectral images. • Spectral information can play a fundamental role for change detection. • Proposes a multivariate manifold estimation model based on spectral unmixing. • A model that adapts and accommodates various multimodal sensor combinations.