Saliency analysis is essential to detect common regions of interest (ROI) in remote sensing images. However, many methods imply saliency analysis in single images and cannot detect common ROI accurately. In this paper, we propose the joint saliency analysis based on iterative clustering (JSIC) method to detect common ROIs. Firstly, the size of superpixel patch is adaptively determined by texture feature. Secondly, color feature and intensity feature are utilized to get initial saliency maps and Otsu is utilized to obtain initial ROIs. Finally, iterative clustering is applied to obtain final ROI with less background inference. Quantitative and qualitative experiments results show that the iterative clustering joint saliency analysis method not only has better performance when compared to the other state-of-the-art methods, but also can eliminate image without ROI. Our contributions lie in three aspects as follows: 1) We propose a novel method to calculate the number of superpixel blocks adaptively. 2) A new joint saliency analysis method is proposed based on color feature and intensity feature. 3) We propose a novel saliency modification strategy based on the iterative cluster, which could reduce the background inference and eliminate images without ROIs.
Region of Interest (ROI) extraction is an important component in remote sensing images processing, which is useful for further practical applications such as image compression, image fusion, image segmentation and image registration. Traditional ROI extraction methods are usually prior knowledge-based and depend on a global searching solution which are time consuming and computational complex. Saliency detection which is widely used for ROI extraction from natural scene images in these years can effectively solve the problem of high computation complexity in ROI extraction for remote sensing images as well as retain accuracy. In this paper, a new computational model is proposed to improve the accuracy of ROI extraction in remote sensing images. Considering the characteristics of remote sensing images, we first use lifting wavelet transform based on adaptive direction evaluation (ADE) to obtain multi-scale orientation contrast feature map (MF). Secondly, the features of color are exploited using the information content analysis to provide a color information map (CIM). Thirdly, feature fusion is used to integrate multi-scale orientation contrast features and color information for generating a saliency map. Finally, an adaptive threshold segmentation algorithm is employed to obtain the ROI. Compared with existing models, our method can not only effectively extract detail of the ROIs, but also effectively remove mistaken detection of the inner parts of the ROIs.
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