There are affordable houses that violation of construction like construction disorder and less construction in China, which will cause wasting and misappropriating the government investment. Thus, it needs to be verified. In view of the large number and wide distribution of residential housing, as well as traditional artificial verification methods are too time-consuming and laborious. In this paper, based on the characteristics of high-width and wide-width of GF-1 satellite, a fast verification method of affordable house from GF-1 panchromatic image by geographic constraint is proposed. First, use the Morphological Building Index (MBI) method to extract the building features of the entire study region, and crop out image blocks by using the affordable house’s vector data that measured by GPS for geographic constraints. Secondly, for the local features of each image block, combined with the Canny operator and the adaptive Mean-shift image segmentation algorithm to extract the buildings within the image block. Finally, based on the overlap rate between the building extraction result and the vector data, it is judged whether the location of the affordable house exists. Experiments show that the building extraction module of this paper can effectively extract the buildings in the image blocks on the GF-1 panchromatic image and is better than the MBI method, which can effectively realize the verification of the affordable houses.
Low-altitude unmanned aerial vehicles (UAV) are widely used to acquire aerial photographs, some of which are oblique and have a large angle of view. Precise, automatic registration of such images is a challenge for conventional image processing methods. We present an affine scale-invariant feature transform (ASIFT)-based method that can register UAV oblique images at a subpixel level. First, we used the ASIFT algorithm to collect initial feature points. Positions of the feature points on corresponding local images were then corrected using the weighted least square matching (WLSM) method. Mismatching points were discarded and a local transform model was estimated using the adaptive normalized cross correlation algorithm, which also provides initial parameters for WLSM. Experiments show that sufficient feature points are collected to successfully register, to the subpixel level, UAV and other images with large angle-of-view variations and strong affine distortions. The proposed method improves the matching accuracy of previous UAV image registration methods.
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