Adaptive Morphological Filtering Method for Structural Fusion Restoration of Hyperspectral Images

Abstract

Recovering hyperspectral image (HSI) from mixed noise degradation is a challenging and promising theme in remote sensing, particularly when stripes and deadlines exist in several contiguous bands. This paper proposes a HSI's restoration method making use of adaptive morphological filtering (AMF) and fusing structure information of an auxiliary color image. An adaptive structuring element (ASE) indicating morphological features of each pixel is generated through information fusion, to simultaneously remove the mixed noise and preserve fine spatial structures. This key technology contains three main steps. First, edges are extracted from the auxiliary image exploiting its color information; then, an edge-constraint growing algorithm is used to generate the clustering kernel; finally, the ASE is obtained via goal-guided k-means clustering. The ASE has extensive application value, for it can be an enhancing module for most filters-based restoration methods, to mitigate the structural damage due to the fixed mask. Among these methods, Gaussian filter for preprocessing and majority voting for postprocessing are introduced in this paper as representatives. In addition, the auxiliary image can be both visible image of multisensor and false RGB component of the undamaged bands of the HSI, so it is relatively available. Experiments on simulated and real data sets show obvious effects on denoising and destriping both subjectively and objectively. The advantage of ASE on structure details preserving, compared to conventional approaches, is clearly demonstrated. The application value of the proposed restoration frame and ASE is further proved through the decision-level postprocessing experiments.