Image segmentation is an important step of the object-oriented information extraction method, which is directly related to the accuracy of information extraction with high resolution remote sensing images. This paper mainly researched on the optimal segmentation scales about nine types of ground features in different layers with RMAS indicators, such as cultivated land, woodland, grassland, and so on, which were used to extract land use information. At the same time, the global optimal segmentation scale constructed with RMNE indicators was used to extract land use information, which was based on the five methods, such as Bayes, Nearest neighbor, Decision tree, Random Forest, and SVM under a single scale layer. The classification results of the two methods were compared and analyzed. The research results show that the multiscale optimal segmentation method adopted in this paper could effectively solve the problems of object confusion and ground object fragmentation in the classification results under a single scale layer, and the classification accuracy is better.
VHR imagery change detection is one of research hotspots and difficulties in the field of remote sensing. However, the traditional remote sensing image change detection method is a waste of time and energy and low efficiency. In recent years, deep learning approaches in remote sensing image change detection verified feasible and save time to improve efficiency. A UNet change detection method based on aggregation residuals and attention mechanism is proposed, using prior knowledge of deep learning. The UNet model is used as the basic model, and the aggregation residual module is introduced in the up-down sampling stage, which can fully extract the feature information of the image. The weight of each component in the feature graph can be adjusted by adding attention module in the jump connection layer. In the process of experiment based on the model parameters are reasonable and effective set of data sets to Longnan remote sensing image change detection, and the experimental results showing that compared with the traditional deep learning semantic segmentation method, this article methods F1 value of 0.873, the generated change detection figure closer to label figure, higher accuracy, shorter.
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