A Model of Extracting Building from High Resolution Remote Sensing Image Based on Bayesian Convolutional Neural Networks

Abstract

When extract building from high resolution remote sensing image with meter/sub-meter accuracy, the shade of trees and interference of roads are the main factors of reducing the extraction accuracy. Proposed a Bayesian Convolutional Neural Networks(BCNET) model base on standard fully convolutional networks(FCN) to solve these problems. First take building with no shade or artificial removal of shade as Sample-A, woodland as Sample-B, road as Sample-C. Set up 3 sample libraries. Learn these sample libraries respectively, get their own set of feature vector; Mixture Gauss model these feature vector set, evaluate the conditional probability density function of mixture of noise object and roofs; Improve the standard FCN from the 2 aspect:(1) Introduce atrous convolution. (2) Take conditional probability density function as the activation function of the last convolution. Carry out experiment using unmanned aerial vehicle(UVA) image, the results show that BCNET model can effectively eliminate the influence of trees and roads, the building extraction accuracy can reach 97%. Keywords:High resolution remote sensing image; Convolutional Neural Networks; Full Convolution Networks; Bayesian Convolutional Neural Networks; building extraction; conditional probability density function. 1.IntroduceIt is of great significance to obtain the detailed distribution map of urban buildings in the social management and development plan. High resolution remote sensing images contain lots of object features, including color, size, shape, texture and layout of the relationship between objects, which makes it possible to accurately extract buildings from high resolution remote sensing images [1]. The absence of low-cost high resolution multi spectral image with meter/sub-meter accuracy provided reliable source of data for the building extraction. In remote sensing image, the outline of the building is characterized mainly by the shape and distribution of the roof [2]. Because the material, color, shape, size and orientation of the roofs are diverse, and some of the roofs even shaded by trees or other tall buildings, which makes it difficult to extract building from high resolution multi spectral remote sensing image.Because the urban environment is complicated, the spatial pattern of buildings is relatively complex, object has high spectral variability in remote sensing image. same objects with different spectrum is common in the urban remote sensing image. Thus it is difficult for auto/semi-auto classification base on pixel spectral signature [3]. Studies have shown that although the 4 multi spectral bands of high resolution remote sensing image can be able to distinguish the land cover types of town's water, bare soil, vegetation, shadow and impervious ground [4,5]. But only with these low level spectral statistical characteristics, it is hard to extract road, parking and building from impervious ground [6]. In recent years, methods combining spatial and spectral analysis have been put forward. It is considered that spatial cha...