ABSTRACT:SIFT as the representative of the same feature point extraction and matching algorithm has been widely applied in the field of multisource remote sensing image matching. However, it eliminates noise and detects features at different scale levels by building or approximating the Gaussian scale space based on linear. Gaussian blurring does not respect the natural boundaries of objects and smoothes to the same degree details and noise, reducing localization accuracy. To solve this problem, we proposed an improved KAZE algorithm which can build stable nonlinear scale space. Firstly, the extreme points are detected through building stable nonlinear scale space. Secondly, The match result by optimizing the feature points and strictly limiting matching threshold is used to calculate geometric transformation model parameters between two image. Finally, we can use this geometric transformation model to restrict the search space for feature points matching. Experimental results show that the improved KAZE algorithm is significantly better than the before KAZE. Moreover, for detail and texture blurred images, KAZE and its improved algorithm have unique advantages compared to the SIFT. I I I INTRODUCTION NTRODUCTION NTRODUCTION NTRODUCTIONWith the rapid development of remote sensing technology, different sensor resolutions and phase of the multi-source remote sensing images have become an important data source for basic surveying and mapping, agricultural census, meteorological observations, land and resources dynamic monitoring. Due to the influence of external factors such as weather, sunlight, shelter and different imaging time, angle, distance led to the questions of image resolution, pan, rotate, zoom, it has brought great difficulty to multi-source remote sensing images matching work . ASIFT (Morel, 2009), has been widely applied in the field of multi-source remote sensing images matching. However, it eliminates noise and detects features at different scale levels by building or approximating the Gaussian scale space based on linear. Gaussian blurring does not respect the natural boundaries of objects and smoothes to the same degree both details and noise, reducing localization accuracy and distinctiveness, which causes edge matching poor stability, brings more error matching points and also increases the difficulty of the error matching points elimination. To solve this problem, we proposed an improved KAZE algorithm which can builds stable nonlinear scale space using efficient Additive Operator Splitting (AOS) techniques (Ruan Zong-cai, 2006) and variable conductance diffusion. REMOTE REMOTE REMOTE REMOTE SENSING SENSING SENSING SENSING IMAGE IMAGE IMAGE IMAGES S S S MATCHING MATCHING MATCHING MATCHING ALGORITHM ALGORITHM ALGORITHM ALGORITHM BASED BASED BASED BASED ON ON ON ON IMPROVED IMPROVED IMPROVED IMPROVED KAZE KAZE KAZE KAZEIn the first phase of the improved KAZE algorithm, nonlinear scale space is built using efficient Additive Operator Splitting (AOS) techniques and v...
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