BASHVS: A Multispectral and SAR Image Fusion Method Based on Bidirectional Aggregation of Saliency in Human Visual System

Xunqiang Gong, Yichuang Luo, Wenbo Chen, Yonglei Chang, Yuting Wan, Ailong Ma, Yanfei Zhong

Abstract: The limitations of remote sensing sensor technology make it difficult to simultaneously capture earth observation information presented in different forms within a single remote sensing image. Acquiring more plentiful target information through fusion technology has therefore remained a research hotspot. The fusion of multispectral (MS) and synthetic aperture radar (SAR) imagery integrates spectral and backscatter information, thereby improving land cover (LC) classification effects. However, current pixel-level fusion methods often fail to adequately account for the model differences between SAR and MS images, leading to spectral–spatial inconsistencies and severe degradation from speckle noise. To address this problem, a fusion method is proposed based on bidirectional aggregation of saliency in the human visual system (BASHVS). First, the SAR and MS images are decomposed into base and detail layers using a synchronized-anisotropic diffusion algorithm. Subsequently, the detail layer is fused using a new sum of modified anisotropic Laplacian (NSMAL) algorithm. Finally, for base layer fusion, pixel saliency (PS) and structural saliency are extracted bidirectionally. The BASHVS is compared with 16 existing fusion methods using ten evaluation metrics. The results demonstrate that BASHVS achieves the best comprehensive performance and significantly improves the visual quality of the fused images. LC classification using BASHVS fused images shows an average increase of 1.050% in overall accuracy (OA) and 0.014 in Kappa coefficient compared to the original MS images, confirming its advantage for LC classification. The source code of BASHVS is shared at https://github.com/CHUANGL8346/BASHVS.

External IDs:dblp:journals/tgrs/GongLCCWMZ25