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Region of interest detection based on salient feature clustering for remote sensing images

机译：基于显着特征聚类的遥感图像感兴趣区域检测

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摘要

The region of interests (ROI) detection plays an important role in the remote sensing data processing and analysis. In this paper, a new region of interest detection method based on salient feature clustering for remote sensing images is proposed. Four steps are included in the proposed method. First, the information salient feature maps are constructed by computing the spectrum information and histograms of multispectral images. Second, a clustering strategy based on k-means is presented to generate the common salient feature maps in the CIE Lab color space. Third, the final saliency maps are generated by fusing the information salient feature maps with the common salient feature maps. Finally, we can get the ROIs by segmenting the final saliency map. Experimental results show that compared with five existing models, our model gets more accurate saliency maps without the basis of prior knowledge.
机译：感兴趣区域（ROI）检测在遥感数据处理和分析中起着重要作用。提出了一种基于显着特征聚类的遥感图像感兴趣区域检测新方法。所提出的方法包括四个步骤。首先，通过计算多光谱图像的光谱信息和直方图来构造信息显着特征图。其次，提出了一种基于k均值的聚类策略，以在CIE Lab颜色空间中生成常见的显着特征图。第三，通过将信息显着特征图与公共显着特征图融合来生成最终显着图。最后，我们可以通过分割最终的显着性图来获得ROI。实验结果表明，与五个现有模型相比，我们的模型无需先验知识即可获得更准确的显着性图。

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