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Multiresolution image denoising for detection of bridges in Satellite Images

机译:用于卫星图像中桥梁检测的多分辨率图像去噪

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Satellite Images contain very important information about geographical features such as roads, rivers, bridges, buildings, etc of earth. Such features are required for military as well as civilian applications. Automatic detection of these features are required for terrain mapping and Geographic Information System (GIS). However, satellite images are often corrupted by different types of noise and detection of details is a nontrivial problem. Hence suitable denoising algorithms have to be incorporated for the images before detection of features is done. Otherwise the process may end in erroneous results and loss of wanted information. Although different algorithms are existing for denoising images, due to the requirement of detection of long edges in the case of bridge extraction, the Curvelet Transform (CT)based method is found to be superior. This choice provides the benefit of multiresolution image processing such as compatibility of human visual system and line and edge detection. Once this pre processing is done, appropriate types of segmentation algorithms can be applied for detection process. Initially, Fuzzy based algorithm is used for segmenting water and background details and then bridge extraction is done using difference in pixel intensity between bridge edge and water.
机译:卫星图像包含有关地理特征(例如道路,河流,桥梁,地球等)的非常重要的信息。这些功能对于军事和民用应用都是必需的。地形图和地理信息系统(GIS)需要自动检测这些功能。但是,卫星图像通常会因不同类型的噪声而损坏,并且对细节的检测不是一个简单的问题。因此,在完成特征检测之前,必须为图像合并合适的降噪算法。否则,该过程可能会导致错误的结果并丢失所需的信息。尽管存在用于图像去噪的不同算法,但是由于在桥提取的情况下需要检测长边缘,因此发现基于Curvelet变换(CT)的方法更为优越。这种选择提供了多分辨率图像处理的优势,例如人类视觉系统与线条和边缘检测的兼容性。一旦完成该预处理,就可以将适当类型的分割算法应用于检测过程。最初,基于模糊的算法用于分割水和背景细节,然后使用桥边缘和水之间的像素强度差异来完成桥提取。

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