Convolutional neural networks for building extraction from high-resolution remote sensing images
Convolutional neural networks have poor effects on building extraction in complex image scenes. To address this issue, this paper optimized and adjusted the lightweight convolutional neural network LinkNet. CA-ResNet-50, a deep residual network integrated with coordinate attention (CA) mechanism, was used as the encoder of LinkNet, which significantly enhanced the feature extraction performance of the network model and improved its building extraction capability in complex scenes. At the same time, the convolutional decomposition method was used to optimize the initial block of LinkNet, and a faster network training speed was obtained. Finally, the building extraction network with excellent performance CA-LinkNet was obtained. The test results show that the accuracy indexes IoU, Kappa, and F1 of CA-LinkNet on the WHU aerial building dataset have increased by 2.01％, 1.26％, and 1.11％, respectively, compared with the original LinkNet. In addition, the accuracy indexes of CA-LinkNet on the selected dataset are superior to those of the classical segmentation network, and CA-LinkNet can effectively extract buildings in complex image scenes, showing strong anti-interference ability.
万方数据
维普
