Neural network-based segmentation of satellite imagery for estimating house cluster of an urban settlement from Google Earth images

8th International Symposium of the Digital Earth

地球科学;计算机科学

Wardaya, P.D.^1 ; Ridha, S.^1

Geosciences and Petroleum Engineering Faculty, Universiti Teknologi PETRONAS, Tronoh, Perak Darul Ridzuan 31750, Malaysia^1

关键词: Back propagation neural networks;    Computational time;    Google earths;    Hidden layers;    Image-based;    Training data;   

Others  :  https://iopscience.iop.org/article/10.1088/1755-1315/18/1/012019/pdf
DOI  :  10.1088/1755-1315/18/1/012019

学科分类：计算机科学（综合）

来源: IOP

【 摘 要 】

In this paper a backpropagation neural network is utilized to perform house cluster segmentation from Google Earth data. The algorithm is subjected to identify houses in the image based on the RGB pattern within each pixel. Training data is given through cropping selection for a target that is a house cluster and a non object. The algorithm assigns 1 to a pixel belong to a class of object and 0 to a class of non object. The resulting outcome, a binary image, is then utilized to perform quantification to estimate the number of house clusters. The number of the hidden layer is varying in order to find its effect to the neural network performance and total computational time.