气候变化领域集成服务门户(CCPortal): Fast building detection using new feature sets derived from a very high-resolution image, digital elevation and surface model

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DOI	10.1080/01431161.2024.2313991
	Fast building detection using new feature sets derived from a very high-resolution image, digital elevation and surface model
	Gunen, Mehmet Akif
发表日期	2024
ISSN	0143-1161
EISSN	1366-5901
起始页码	45
结束页码	5
卷号	45 期号:5
英文摘要	Detecting building rooftops with very high-resolution (VHR) images is an important issue in many fields, including disaster management, urban planning, and climate change research. Buildings with varying geometrical features are challenging to detect accurately from VHR image due to complicated image scenes containing spectrally similar objects, illumination, occlusions, viewing angles, and shadows. This study aims to detect building rooftops with high accuracy using a new framework that includes VHR image, visible band difference vegetation index, digital surface and elevation models, the terrain ruggedness and the topographic position index. Five distinct feature sets were generated in order of importance by exposing the ten related stacking features to a feature selection procedure using the maximum relevance minimum redundancy method. Then, Auto-Encoder, k-NN, decision tree, RUSBoost, and random forest machine learning algorithms were utilized for binary classification. Random forest yielded the highest accuracy (97.2% F-score, 98.72% accuracy) when all features (F10) were used, while decision tree was the least successful (59.16% F-score, 83.56% accuracy) for RGB feature set (FRGB). It was revealed that classification of F10 with random forest increased F-score by about 23% compared to classification with FRGB. Additionally, McNemar's tests showed no statistically significant difference between random forest vs k-NN and decision tree vs RUSBoost.
英文关键词	Building detection; very high-resolution image; machine learning; deep learning
语种	英语
WOS研究方向	Remote Sensing ; Imaging Science & Photographic Technology
WOS类目	Remote Sensing ; Imaging Science & Photographic Technology
WOS记录号	WOS:001162324900001
来源期刊	INTERNATIONAL JOURNAL OF REMOTE SENSING
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/296770
作者单位	Gumushane University; Gumushane University
推荐引用方式 GB/T 7714	Gunen, Mehmet Akif. Fast building detection using new feature sets derived from a very high-resolution image, digital elevation and surface model[J],2024,45(5).
APA	Gunen, Mehmet Akif.(2024).Fast building detection using new feature sets derived from a very high-resolution image, digital elevation and surface model.INTERNATIONAL JOURNAL OF REMOTE SENSING,45(5).
MLA	Gunen, Mehmet Akif."Fast building detection using new feature sets derived from a very high-resolution image, digital elevation and surface model".INTERNATIONAL JOURNAL OF REMOTE SENSING 45.5(2024).

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