The Moon is the heavenly body closest to Earth. In order to conduct an in-depth study on the Moon, select the landing site, and/or plan for roving exploration, researchers need to understand how long the Moon has existed and how it was formed. An internationally common method for age dating of the Moon in areas without lunar soil samples is to determine the absolute age of the Moon based on the number and sizes of impact craters. For the identification and extraction of impact craters required for age dating, we combined histogram of oriented gradients (HOG) features and support-vector machine (SVM) classifiers to set up a sample pool (including positive and negative samples) for lunar impact craters, thereby achieving automatic identification and extraction of impact craters of different sizes in the landing area of Chang'e-5.
A new method based on a Network in Network (NIN) structure is proposed to detect target changes from multi-temporal optical remote sensing images. Firstly, the changed areas are captured by a change detection method based on multifeature fusion, and the changed patches are obtained by morphological processing. Then, a convolutional neural network with an NIN structure is constructed to train the target recognition model using a small number of samples and to distinguish the original images corresponding to the tchanged patches. Finally, a recognition strategy combining preliminary screening and thorough screening is designed, and multiple thresholds are assigned according to the patch size to avoid the possible false detection brought by a single threshold. Based on experiments with multi-temporal airport images, the overall accuracy of aircraft target change detection using the method in this study was 91.89%, with a false alarm rate of 10.71%, indicating that this method can accurately and reliably detect target change.
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