Building Detection from Remote Sensing Images Based on Improved U-net

The building environment in urban areas is complex. Achieving high building detection accuracy from remote sensing images is challenging because of the difficulty associated with distinguishing between buildings and the environmental information. To solve this problem, an improved U-type convolutional neural (U-net) network with enhanced low-dimensional feature information is proposed for detecting buildings from the remote sensing images. Initially, a building is detected using the U-net network model typically employed for medical image segmentation. Further, the low-dimensional information is weakened at each step of the network propagation process. Before merging the feature map of a certain level in the feature pyramid with the feature map of the corresponding expansion path level, it is merged with the feature map of the previous level to optimize the detection accuracy of the building edges. According to the experimental results obtained using a datasct of remote sensing images covering a range of approximately 340 km(2), the proposed method achieves values of 83. 9%, 92. 8%, and 83. 6% for the intersection-over-union (IoU), pixel accuracy, and Kappa coefficient, respectively, demonstrating its superior performance when compared with the fuzzy C-means clustering algorithm, fully convolutional neural network, and classic U-net methods.