Visual Saliency Detection in High-Resolution Remote Sensing Images Using Object-Oriented Random Walk Model

As high-resolution remote sensing images begin to integrate new characteristics, such as a great volume of data, a wide variety of ground objects, and high structural complexity, traditional methods previously used for feature extraction in low-resolution remote sensing images are inefficient and inadequate for the accurate feature description of various objects. Thus, object feature extraction from a high-resolution remote sensing image remains a challenging task. To address this issue, we introduced the visual attention mechanism into high-resolution remote sensing image analysis in this study by proposing a novel object-oriented random walk model for visual saliency (ORWVS) detection from high-resolution remote sensing images. In the proposed model, an object-oriented random walk strategy is designed to simulate the transfer path of visual focus on the images and to extract the local salient regions in an efficient and accurate manner, laying a foundation for accurate feature descriptors. The ORWVS is compared with eight visual attention models, and the experiments prove its superiority.