Building extraction from remote sensing image based on improved segnet neural network and image pyramid

Abstract

Aiming at the problems such as the insufficient use of context information in high-resolution remote sensing image by convolutional neural network and the time-consuming training model, a building extraction method based on the combination of improved Segnet neural network and image pyramid is proposed. First, the Gaussian and Laplacian pyramid algorithms are used to obtain the multi-scale and multi-resolution detailed feature images of the original image respectively, and the network is trained together with the original image, so that the model can learn and extract more abundant image features. Secondly, in order to reduce the complexity of the network, reduce the encoder and decoder structure in the original Segnet neural network, save the calculation cost and effectively avoid the occurrence of over-fitting and other phenomena. The experiment uses Massachusetts buildings as the dataset. Compared with the original segnet neural network extraction method, the proposed method has significantly improved the extraction accuracy and extraction efficiency.