出版方 ： Institute of Electrical and Electronics Engineers Inc.

作　　者： ;

机构地区： 深圳大学

出　　处： 《IEEE地球科学与遥感快报》

摘　　要： 规模基于空间的方法最近为 multispectral 排列被学习了；由于图象对之间的重要紧张差别，然而，通常没有足够的 keypoint 通讯，发现，并且排列的坚韧性趋于被损害。在这封信，我们试图从下列二个方面改进表演： 1 )放大空间避免 Gaussian 变模糊的边界，我们采用非线性的规模空间与正确地被匹配的潜力探索更多的 keypoints ，并且 2 )一个柔韧的特征描述符被建议，并且产生特征矩阵用以前建议的旋转不变的距离被匹配获得更正确的 keypoint 通讯。建议方法以正确地匹配的图象的 keypoints，排列精确性，和率的正确地匹配的数字与最先进的方法相比改进匹配的性能，这配对的遥远的图象显示的 multispectral 的试验性的结果。如果描述符小心地被设计，本地特征是足够特殊的为，这也在这个字母被揭示甚至当主要取向不是现在时，生产好匹配。 The scale space-based method has been recently studied for multispectral alignment; however, due to the significant intensity difference between the image pairs, there are usually not enough keypoint correspondences found, and the robustness of the alignment tends to be compromised. In this letter, we attempt to improve the performance from the following two aspects: 1) to avoid the boundary blurring of Gaussian scale space, we adopt nonlinear scale space to explore more keypoints with potential of being correctly matched, and 2) a robust feature descriptor is proposed, and the resulting feature matrix is matched using the previously proposed rotation-invariant distance to obtain more correct keypoint correspondences. Experimental results for multispectral remote images indicate that the proposed method improves the matching performance compared to state-of-the-art methods in terms of correctly matched number of keypoints, aligning accuracy, and rate of correctly matched image pairs. It is also revealed in this letter that, if the descriptor is carefully designed, the local features are distinctive enough for produce good matching even when the main orientation is not present. © 2004-2012 IEEE.

关 键 词： 精确性 展示抽取 图象登记 图象分辨率 遥感 坚韧性 向量 图象排列 非线性的规模不变的 旋转不变的距离 根除 放大不变的特征变换 筛

分 类 号： [P2]