With the rapid development of sensor technology and deep learning technology,change detection based on deep learning technology has become a research hotspot in the field of remote sensing.However,existing deep learning-based change detection methods suffer from limited accuracy due to inadequate feature extraction and fusion.To address that,this paper proposes a change detection method based on 3D convolutional neural network.In the encoding stage,the inner fusion property of 3D convolution is used to extract and fuse bi-temporal image features simultaneously.In the decoding stage,in order to effectively utilize the full-scale information of image features,the full-scale skip connection strategy is introduced to combine the feature information from different scales in the time dimension and then generate the change map.Extensive comparative experiments demonstrate that the proposed method performs better than the other state-of-the-art change detection methods.
维普
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