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Saliency analysis and region of interest detection via orientation information and contrast feature in remote sensing images

Saliency analysis is an important implement for remote sensing image processing. It can effectively solve the contradiction between accuracy and computation complexity when it is applied in the region of interest (ROI) detection and extraction for remote sensing images. In this paper, we propose an efficient ROI detection model for remote sensing images based on low-level contrast feature saliency analysis. For the proposed model, we first perform fast directional integer wavelet transform (FD-IWT) to obtain multi-scale approximate and detail coefficients. Then these multi-scale orientation, local contrast, and global contrast features are exploited to generate the saliency map. Qualitative and quantitative evaluation shows that the proposed model outperforms the other nine state-of-art ROI detection models for that the proposed model can obtain highlighted and integrated ROI with well-defined boundaries, as well as eliminate the shadow interference in remote sensing image.