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首页> 外文会议>2016 World Conference on Futuristic Trends in Research and Innovation for Social Welfare >Histogram based hill climbing optimization for the segmentation of region of interest in satellite images
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Histogram based hill climbing optimization for the segmentation of region of interest in satellite images

机译：基于直方图的爬山优化用于卫星图像中感兴趣区域的分割

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摘要

Images received from the satellite contains huge amount of information to process and analyze. So the segmentation is a crucial and important procedure in the analysis of images to gather necessary information from the satellite images. In the proposed approach, the satellite images are segmented using hill climbing local optimization technique and modified k-means clustering algorithm. In this approach, satellite images in RGB color space is converted into CIELAB color space. This color space is intended to approximate vision of human and perceptually uniform. Moreover, the intensity (L) component of this color space exactly matches the human perception of lightness. In the next step, the hill climbing process is applied on the color histogram of CIELAB color space image to obtain the initial cluster centers. In the final step, these cluster centers are given to the k-means clustering algorithm to produce the segmented image as the output. The effectiveness of the proposed approach has been demonstrated by number of experiments. The proposed method is more effective and efficient in the segmentation of satellite images to obtain meaningful clusters as compared to other conventional methods.
机译：从卫星接收的图像包含大量信息，需要处理和分析。因此，分割是图像分析中从卫星图像中收集必要信息的关键和重要过程。在该方法中，采用爬山局部优化技术和改进的k均值聚类算法对卫星图像进行分割。通过这种方法，将RGB颜色空间中的卫星图像转换为CIELAB颜色空间。该色彩空间旨在逼近人类的视觉并在感知上统一。此外，此色彩空间的强度（L）分量与人类对亮度的感知完全匹配。下一步，对CIELAB颜色空间图像的颜色直方图应用爬山过程，以获得初始聚类中心。在最后一步中，将这些聚类中心赋予k-均值聚类算法，以生成分割图像作为输出。大量实验证明了该方法的有效性。与其他常规方法相比，该方法在分割卫星图像以获得有意义的聚类方面更加有效。

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