研究目的
To develop software that uses image processing for roof detection from satellite images to estimate rooftop area receiving solar exposure and the number of individual buildings receiving solar exposure, making PV system planning feasible and affordable for various scales of installation.
研究成果
The developed software and GUI provide a feasible and affordable solution for planning PV systems by automating rooftop detection from satellite images. Future work includes estimating demand from the detected buildings and improving the classification algorithm by machine learning.
研究不足
The accuracy of the algorithm for building classification varies with pixel density and requires user adjustments in the GUI for optimal performance. Certain regions may require specific adjustments depending on local landscape features similarly colored to rooftops.
1:Experimental Design and Method Selection:
The program uses a four-step processing algorithm for PV planning, including satellite image selection, processing and post-processing (RGB to HSI and Erosion Dilatation), and classification to determine roof area and expected PV capacity.
2:Sample Selection and Data Sources:
Satellite images with 1920x1080 resolution, each pixel covering a
3:8m by 8m surface. List of Experimental Equipment and Materials:
MATLAB for seeded region growing algorithm development.
4:Experimental Procedures and Operational Workflow:
Conversion of RGB image to HSI format, processing in a seeded region growing algorithm, post-processing with morphological opening operators and a pixel cluster filter, and feature extraction for classification.
5:Data Analysis Methods:
Analysis of the size of each pixel cluster to estimate total sun-exposed roof area and the number of buildings.
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