Abstract
Using high-resolution satellite image to detect change has been a hotspot in the field of remote sensing for a long time series. The change detection method combining feature extraction and machine learning could extract the change information effectively, but the manual sample selection is a huge workload for a wide range remote sensing images, and it is also difficult to ensure the accuracy of the pre-detection sample using a single difference image. Therefore, in this paper, a new method for change detection has been put forward based on multi-feature fusion of D-S evidence theory. In this approach, the texture difference image has calculated by structural similarity, because the difference image based on structural similarity plays a great role in change detection, which was verified in experiments. The difference images based on texture features and traditional spectral features are fused by D-S evidence theory, and texture features and spectral features have been fully utilized. Setting rules to select samples with high confidence based on pixels, and SLIC super-pixel segmentation has applied in order to improve further the credibility of the sample. Finally, the samples selected by SLIC segmentation optimization are sent to the classifier training to obtain the final result. The experimental results show that texture features play a very important role in the change detection of high-resolution remote sensing images, and D-S evidence theory could effectively fuse spectral texture features to improve the accuracy of change detection. The proposed method has high accuracy and good performance in change detection.
Highlights
With the intensification of human construction activities and the continuous change of natural ecological environment, it is of great significance to obtain real-time and accurate land surface change information for protecting ecological environment, managing natural resources, and researching on social development [1]–[3]
The performance of image fusion based on texture difference is significantly improved, the overall accuracy (OA) has increased to 95.86%, and the kappa coefficient has increased by nearly 30%, showing that the role of spatial texture features is far greater than spectral features in the change detection of high-resolution optical images
structural similarity (SSIM) model is used to calculate the difference images of texture features and the fusion of spectral and texture features is achieved by the D-S evidence theory
Summary
With the intensification of human construction activities and the continuous change of natural ecological environment, it is of great significance to obtain real-time and accurate land surface change information for protecting ecological environment, managing natural resources, and researching on social development [1]–[3]. Another method of supervised change detection is to select the change-unchanged samples on the multitemporal images, and to train the binary classification model to predict and classify the whole image in order to get the change detection map [39], [41] This method is a hot spot in the field of remote sensing change detection, which has high accuracy. This method requires prior information to be used for sample selection [41], [56], [57], which will greatly increase the workload for large-scale remote sensing change detection in the actual change detection work. In order to solve the problem that a large number of training samples need to be marked manually in the supervised change detection, the D-S evidence the theory has used to fuse a variety of different images, and super-pixel segmentation has combined to realize the selection of changed and unchanged samples to reduce manual intervention. In view of the limited spectral band and insufficient spectral information of high spatial resolution remote sensing images, texture features has extracted as the supplement of spectral information, and structural similarity has used to generate the texture difference images. Support vector machine (SVM), random forest (RF) and deep neural networks (DNN) are used to train the model, and the change information is extracted
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