The technology of automatic selecting landmark plays a significant role in aircraft navigation and ground informationassurance. Compared to the normal object detection, it is quite difficult to describe and quantify the characteristics of alandmark due to its various status and no stable structure. This paper attempts to innovatively combine CNN with thetechnology of selecting landmark. The algorithm used in this paper uses a structurally stable adaptation region as a learningsample to train the CNN classification model. In the selection phase, remote sensing images were cut into pieces of patches,landmark of which was then recognized through the CNN classification model. Non-maxima suppression was used to filterout the low rate landmark and a correlation peak-based uniqueness analysis (the ratio of primary and secondary peaks andthe highest sharpness of peak) was used to ensure landmark with no similarity pattern in the remote sensing image. Theresults indicate the effectiveness of proposed method for Selecting Remote Sensing Image Adaptation Structure.
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