Intelligent Optimization Learning for Semantic Segmentation of High Spatial Resolution Remote Sensing Images

  Objectives   With the hardware development of remote sensing sensors, the high spatial resolution remote sensing images are widely used. In the actual classification, typical classification algorithms can not achieve high accuracy and efficiency, while deep learning semantic segmentation algorithms can not achieve good generalization.

  Methods   In order to adapt to the large-scale high-resolution images, we design a semantic segmentation model of simulated annealing hyperparameter optimization and depthwise separable convolution based on U-Net. Firstly, the depthwise separable convolutional module is used to extract the features on the baseline. Then, the intelligent optimization learning model based on simulated annealing searches the global optimal solution of hyperparameters. Finally, experiments were carried out in ISPRS2D and Gaofen image dataset (GID).

  Results   Compared to other classification methods, the proposed method achieves the highest classification accuracy in buildings, low vegetation, trees, cars and overall accuracy, and the total accuracy of classification results reaches 86.5% in ISPRS2D. And in the classification results of GID, the accuracies of the proposed method in water, grassland, forest and general classification are all greatly improved. The mean intersection over union of the proposed method is also higher than that of other methods.

  Conclusions   The results demonstrated the higher efficiency, higher accuracy and robustness of the proposed method.