Active Contour Building Segmentation Model based on Convolution Neural Network

In high-resolution remote sensing images, artificial features on the surface account for a large proportion. In artificial features, buildings, as special artificial features, buildings have different shapes. They are easily affected by light, so it takes a long time to extract using traditional image segmentation methods. It can't effectively design feature engineering to depict the high-dimensional features of the target building. We propose an active contour model based on a convolution neural network, which integrates the prior knowledge and constraints of active contour model, such as continuity of boundary, smooth edge, and geometric characteristics of buildings, into the learning process of convolution neural network to realize the close unity of ACM and CNN. According to our work, a fundamental end-to-end trainable image segmentation framework which is composed of convolution neural network (CNN) and ACM with learnable parameters is implemented, the problem of semantic segmentation of buildings in aerial images was dealt with, the model was evaluated on the publicly available dataset called Vaihingen, and some parameters were explained. In building semantics, the active contour model based on a convolution neural network has good performance.