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首页> 外文会议>SPIE Conference on Automated Visual Inspection and Machine Vision >Region of Interest Detection Based on Visual Saliency Analysis and Iteratively Clustering for Remote Sensing Images
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Region of Interest Detection Based on Visual Saliency Analysis and Iteratively Clustering for Remote Sensing Images

机译：基于视觉显着性分析和遥感图像迭代聚类的感兴趣区域

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摘要

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.
机译：显着性分析对于检测遥感图像中的常见感兴趣区域（ROI）是必不可少的。然而，许多方法意味着单个图像中的显着性分析，不能准确地检测常见的ROI。本文提出了基于迭代聚类（JSIC）方法来检测普通ROI的关节显着性分析。首先，通过纹理特征自适应地确定SuperPixel补丁的大小。其次，利用彩色特征和强度特征来获得初始显着性图，利用OTSU获得初始ROI。最后，应用迭代聚类以获得具有较少背景推断的最终投资回报率。定量和定性实验结果表明，与其他最先进的方法相比，迭代聚类接头显着性分析方法不仅具有更好的性能，还可以在没有ROI的情况下消除图像。我们的贡献在三个方面如下：1）我们提出了一种新颖的方法，可自适应地计算超像素块的数量。 2）基于颜色特征和强度特征提出了一种新的关节显着性分析方法。 3）我们提出了一种基于迭代集群的新型显着性修改策略，这可以减少背景推断并消除没有ROI的图像。

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