BUILDING FOOTPRINT EXTRACTION FROM HIGH RESOLUTION SATELLITE IMAGERY USING SEGMENTATION

International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences

Khatriker, S.^1¹ 

[1] Indian Institute of Remote Sensing, Dehradun, India^1

关键词: Segmentation;    Building Extraction;    Multi-Resolution Algorithm;    Rule-Based Feature Extraction;    High Resolution Satellite Imagery;   

DOI  :  10.5194/isprs-archives-XLII-5-123-2018

学科分类：地球科学(综合)

来源: Copernicus Publications

【 摘 要 】

Identification and mapping of urban features such as buildings and roads are an important task for cartographers and urban planners. High resolution satellite imagery supports the efficient extraction of manmade objects. For the planning and designing of Smart cities, building footprint information is an essential component, and geospatial technologies helps in creating this large mass of data inputs for designing and planning of smart cities. In this study segmentation approach is followed for building extraction. For extraction of buildings especially from the high resolution imagery, number of various semiautomatic and automatic methods have been developed till date to reduce the time and efforts required in manual building mapping. In this study, two semiautomatic image segmentation techniques are used for building extraction from high resolution imagery using algorithms- Multi-resolution segmentation and Rule based feature extraction, which are applied on Worldview 2 (2010) imagery of Dehradun area. The segmented image were further classified to extract buildings from the segmented image features. The study identify the usefulness of both the methods in building extraction and finds the optimum set of rules for extracting buildings from high resolution data sets. The True Positive Rate using Rule based feature extraction is 88.11 % compared to 85.46 % from Multi-resolution segmentation algorithm. The False Negative Rate (FNR) of Multi-resolution segmentation algorithm (16.5 %.) is very less compared to Rule based feature extraction (67.5 %). In the study the buildings were extracted with the accuracy of 88.9 %.

【 授权许可】

CC BY