For change detection in multi-source heterogeneous images, this paper proposes a change detection method using a refined hierarchical clustering approach. For multi-source heterogeneous multi-temporal images, Stacked Denoising Autoencoders (SDAE) is used to extract deep features from multi-source heterogeneous images. On this basis, the comparability of heterogeneous data in deep feature space is guaranteed by iterative transformation of features. Finally, the correlation between deep features of heterogeneous data is described by introducing a variety of distance measures, and the hierarchical clustering method is improved. The classification of change types is gradually realized through multiple clustering, while improving the accuracy of change detection.
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.
In this paper, we first reviewed the development of remote sensing satellites in China, subsequently establishing that artificial intelligence (AI) is the key to achieve global geographic environment change monitoring in the era of big data. Next, to improve existing specialized and isolated AI solutions, we proposed a change detection method based on the geometric registration of multi-modal images and the fusion of multi-modal radiation information. Our objective was to promote the development of artificial intelligence technology and multi-modal remote sensing image fusion technology in the field of intelligent change detection of global geographic environment.
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