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Feature Discrimination in Large Scale Satellite Image Browsing and Retrieval

机译:大规模卫星图像浏览与检索中的特征识别

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Feature discrimination is a critical component in large scale satellite image browsing and retrieval, as well as in other areas of remote sensing. As many satellite images are multi-spectral, the spectral information should be exploited for efficient browsing and retrieval. In this aspect, color is a suitable candidate to represent the spectral contents. This paper presents an efficient algorithm on feature discrimination for browsing and retrieving of large-scale satellite imagery using color features. Our main focus is on color modeling of interest region based on appropriate color space. The description of color features using spherical influence fields allows unrealistic or biased color prototypes to be removed from the color model. And the proposed color model is capable of discriminating the interest region from the image background. Experimental results demonstrated that interest region was successfully discriminated from the complex scene using the proposed color model in the L~*a~*b color space.
机译:特征识别是大规模卫星图像浏览和检索以及其他遥感领域的重要组成部分。由于许多卫星图像都是多光谱的,因此应利用光谱信息进行有效的浏览和检索。在这个方面,颜色是代表光谱内容的合适候选物。本文提出了一种有效的特征判别算法,用于利用色彩特征浏览和检索大规模卫星图像。我们的主要重点是基于适当的色彩空间对感兴趣区域进行色彩建模。使用球形影响场对颜色特征进行描述,可以从颜色模型中删除不真实或有偏差的颜色原型。所提出的颜色模型能够从图像背景中区分出兴趣区域。实验结果表明,在L〜* a〜* b颜色空间中,使用提出的颜色模型可以成功地将兴趣区域与复杂场景区分开。

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