Version 1
: Received: 2 January 2018 / Approved: 3 January 2018 / Online: 3 January 2018 (04:46:44 CET)
How to cite:
Yang, X.; Zhang, C.; Gao, S.; Yu, F.; Song, D.; Wei, Q.; Chen, Y.; Wang, S. A Model of Extracting Building from High Resolution Remote Sensing Image Based on Bayesian Convolutional Neural Networks. Preprints2018, 2018010019. https://doi.org/10.20944/preprints201801.0019.v1
Yang, X.; Zhang, C.; Gao, S.; Yu, F.; Song, D.; Wei, Q.; Chen, Y.; Wang, S. A Model of Extracting Building from High Resolution Remote Sensing Image Based on Bayesian Convolutional Neural Networks. Preprints 2018, 2018010019. https://doi.org/10.20944/preprints201801.0019.v1
Yang, X.; Zhang, C.; Gao, S.; Yu, F.; Song, D.; Wei, Q.; Chen, Y.; Wang, S. A Model of Extracting Building from High Resolution Remote Sensing Image Based on Bayesian Convolutional Neural Networks. Preprints2018, 2018010019. https://doi.org/10.20944/preprints201801.0019.v1
APA Style
Yang, X., Zhang, C., Gao, S., Yu, F., Song, D., Wei, Q., Chen, Y., & Wang, S. (2018). A Model of Extracting Building from High Resolution Remote Sensing Image Based on Bayesian Convolutional Neural Networks. Preprints. https://doi.org/10.20944/preprints201801.0019.v1
Chicago/Turabian Style
Yang, X., Yan Chen and Shouyi Wang. 2018 "A Model of Extracting Building from High Resolution Remote Sensing Image Based on Bayesian Convolutional Neural Networks" Preprints. https://doi.org/10.20944/preprints201801.0019.v1
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
Subject
Computer Science and Mathematics, Analysis
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
