Multi-scale hybrid saliency analysis for region of interest detection in very high resolution remote sensing images.

Researchers have recently been performing region of interest detection in such applications as object recognition, object segmentation, and adaptive coding. In this paper, a novel region of interest detection model that is based on visually salient regions is introduced by utilizing the frequency and space domain features in very high resolution remote sensing images. First, the frequency domain features that are based on a multi-scale spectrum residual algorithm are extracted to yield intensity features. Next, we extract the color and orientation features by generating space dynamic pyramids. Then, spectral features are obtained by analyzing spectral information content. In addition, a multi-scale feature fusion method is proposed to generate a saliency map. Finally, the detected visual saliency regions are described using adaptive threshold segmentation. Compared with existing models, our model eliminates the background information effectively and highlights the salient detected results with well-defined boundaries and shapes. Moreover, an experimental evaluation indicates promising results from our model with respect to the accuracy of detection results. Novel multi-scale frequency analysis is proposed for intensity feature analysis.Multi-scale analysis is introduced to extract color and orientation features.Spectral information content is proposed for spectral feature analysis.Propose a novel weight multi-scale feature fusion methodSynthesize frequency and space domain features in proposed algorithm frame.