An appropriate detection network is required to extract building information in remote sensing images and to relieve the issue of poor detection effects resulting from the deficiency of detailed features. Firstly, we embed a transposed convolution sampling module fusing multiple normalization activation layers in the decoder based on the SegFormer network. This step alleviates the issue of missing feature semantics by adding holes and fillings, cascading multiple normalizations and activation layers to hold back over-fitting regularization expression and guarantee steady feature parameter classification. Secondly, the atrous spatial pyramid pooling decoding module is fused to explore multi-scale contextual information and to overcome issues such as the loss of detailed information on local buildings and the lack of long-distance information. Ablation experiments and comparison experiments are performed on the remote sensing image AISD, MBD, and WHU dataset. The robustness and validity of the improved mechanism are demonstrated by control groups of ablation experiments. In comparative experiments with the HRnet, PSPNet, U-Net, DeepLabv3+ networks, and the original detection algorithm, the mIoU of the AISD, the MBD, and the WHU dataset is enhanced by 17.68%, 30.44%, and 15.26%, respectively. The results of the experiments show that the method of this paper is superior to comparative methods such as U-Net. Furthermore, it is better for integrity detection of building edges and reduces the number of missing and false detections.

To establish a detection network appropriate for buildings in remote sensing images and lessen the issues including poor detection effects, missing detection and false detection due to the deficiency of detailed features, this paper conducted the design on the basis of Segformer network to solve the problem, coupled the transposed convolutional networks at the decoder stage, and addressed the issue of missing feature semantics via adding holes and fillings. Multiple normalization layers and activation layers were cascaded after the convolution layer to avert overfitting regularization expression and guarantee the classification of stable feature parameters, so as to further advance inter-class differentiated extraction. Ablation experiments and comparison experiments were conducted on AISD, MBD and WHU remote sensing image datasets: The robustness and effectiveness of the improved mechanism were demonstrated by control groups of ablation experiments; in comparison experiments with Hrnet, PSPNet, UNet, Deeplabv3+ and the original detection algorithm, the mIoU of AISD, MBD and WHU was improved by up to 12.83%, 28.82% and 14.26%, respectively. The experimental results indicated that the improved method was better than the comparative methods such as UNet, and had better effects on integrity detection of building edge as well as the reduction of missing detection and false detection.

Water body extraction is a significant direction of application in information monitoring of water resources. Thus, on the basis of high-resolution UAV remote sensing images and self-build inland water body datasets, this paper designs a water body extraction method fused atrous spatial pyramid pooling. First, SegFormer detection network is improved, and the semantic segmentation network is adopted to mine water body semantic information of high-resolution UAV remote sensing images; second, the atrous spatial pyramid pooling decoding module is fused to explore multi-scale contextual information, solve the problems such as feature information loss of water body and deficiency of long-distance information, and come true the coverage of more geometric information whilst responding to semantic features; finally, the semantic segmentation dataset of the inland water body is self-established on the basis of the UAV remote sensing images in Dengzhou, Henan, and the experiment is conducted. Then the validity of the proposed water body extraction method is validated via the comparative ablation experiment with Hrnet, PSPNet, UNet, Deeplabv3+ and the original detection algorithm. According to experimental results, the improved method is superior to the comparative detection methods such as UNet, and has a better effect on the integrity detection of water body edges and the reduction of missing detection and false detection of small water bodies and tributaries.

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