Visual saliency modeling for river detection in high-resolution SAR imagery

Gao, Fei and Ma, Fei and Wang, Jun and Sun, Jinping and Yang, Erfu and Zhou, Huiyu (2017) Visual saliency modeling for river detection in high-resolution SAR imagery. IEEE Access, 6. pp. 1000-1014. ISSN 2169-3536 (https://doi.org/10.1109/ACCESS.2017.2777444)

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Abstract

Accurate detection of rivers plays a significant role in water conservancy construction and ecological protection, where airborne Synthetic Aperture Radar (SAR) data has already become one of the main sources. However, extracting river information from radar data efficiently and accurately still remains an open problem. The existing methods for detecting rivers are typically based on rivers’ edges, which are easily mixed with those of artificial buildings or farmland. In addition, pixel based image processing approaches cannot meet the requirement of real time processing. Inspired by the feature integration and target recognition capabilities of biological vision systems, in this paper, we present a hierarchical method for automated detection of river networks in the high-resolution SAR data using biologically visual saliency modeling. For effective saliency detection, the original image is first over-segmented into a set of primitive superpixels. A visual feature (VF) set is designed to extract a regional feature histogram, which is then quantized based on the optimal parameters learned from the labeled SAR images. Afterwards, three saliency measurements based on the specificity of the rivers in the SAR images are proposed to generate a single layer saliency map, i.e., Local Region Contrast (LRC), Boundary Connectivity (BC) and Edge Density (ED). Finally, by exploiting belief propagation, we propose a multi-layer saliency fusion approach to derive a high-quality saliency map. Extensive experimental results on three airborne SAR image datasets with the ground truth demonstrate that the proposed saliency model consistently outperforms the existing saliency target detection models.

ORCID iDs

Gao, Fei, Ma, Fei, Wang, Jun, Sun, Jinping, Yang, Erfu ORCID logoORCID: https://orcid.org/0000-0003-1813-5950 and Zhou, Huiyu;
CORE (COnnecting REpositories)