Abstract
Remote sensing image change detection method plays a great role in land cover research, disaster assessment, medical diagnosis, video surveillance, and other fields, so it has attracted wide attention. Based on a small sample dataset from SZTAKI AirChange Benchmark Set, in order to solve the problem that the deep learning network needs a large number of samples, this work first uses nongenerative sample augmentation method and generative sample augmentation method based on deep convolutional generative adversarial networks, and then, constructs a remote sensing image change detection model based on an improved DeepLabv3+ network. This model can realize end-to-end training and prediction of remote sensing image change detection with subpixel convolution. Finally, Landsat 8, Google Earth, and Onera satellite change detection datasets are used to verify the generalization performance of this network. The experimental results show that the improved network accuracy is 95.1% and the generalization performance is acceptable.
Highlights
R EMOTE sensing image change detection is a process of extracting natural or artificial change areas from two or more images in the same scene at different times through a series of methods
1) Results for Landsat 8 Data: In order to testify the proposed DeepLabv3+ network generalization performance, another public change detection data utilized in network testing are derived from the Landsat 8 satellite images and provided on the United States National Geological Survey (USGS) website
The change detection problem of high-resolution remote sensing images is converted into a biclassification problem
Summary
R EMOTE sensing image change detection is a process of extracting natural or artificial change areas from two or more images in the same scene at different times through a series of methods. It has important applications in land use or cover, disaster assessment, medical diagnosis, video monitoring, and other fields. It is helpful to adjust the planting plan of crops to increase yields [2]
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