干旱区生态环境知识资源中心(Arid): Self-adaptive Image Segmentation Optimization for Hierarchal Object-based Classification of Drone-based Images

Arid

DOI	10.1088/1755-1315/540/1/012090
	Self-adaptive Image Segmentation Optimization for Hierarchal Object-based Classification of Drone-based Images
	Al-Ruzouq, Rami; Gibril, Mohamed Barakat A.; Shanableh, Abdallah
通讯作者	Al-Ruzouq, R (corresponding author), Univ Sharjah, Dept Civil & Environm Engn, Sharjah 27272, U Arab Emirates. ; Al-Ruzouq, R (corresponding author), Univ Sharjah, GIS & Remote Sensing Ctr, Res Inst Sci & Engn, Sharjah 27272, U Arab Emirates.
会议名称	10th Institution-of-Geospatial-and-Remote-Sensing-Malaysia(IGRSM) International Conference and Exhibition on Geospatial and Remote Sensing (IGRSM)
会议日期	OCT 20-21, 2020
会议地点	ELECTR NETWORK
英文摘要	This study proposes an approach for the quality improvement of feature extraction in unmanned aerial vehicle (UAV)-based images through object-based image analysis (OBIA). A fixed-wing UAV system equipped with an optical (red-green-blue) camera was used to capture very high spatial resolution images over urban and agricultural areas in an arid environment. A self-adaptive image segmentation optimization aided by an orthogonal array from the experimental design was used to optimize and systematically evaluate how OBIA classification results are affected by different settings of image segmentation parameters, feature selection, and single and multiscale feature extraction approaches. The first phase encompassed data acquisition and preparation, which included the planning of the flight mission, data capturing, orthorectification, mosaicking, and derivation of a digital surface model. In the second phase, 25 settings of multiresolution image segmentation (MRS) parameters, namely, scale, shape, and compactness, were suggested through the adoption of an L25 orthogonal array. In the third phase, the correlation-based feature selection technique was used in each experiment to select the most significant features from a set of computed spectral, geometrical, and textural features. In the fourth phase, the ensemble adaptive boosting algorithm (AdaBoost) was used to classify the image objects of segmentation levels in the orthogonal array. The overall accuracy measure (OA) and kappa coefficient (K) were computed to represent a quality indicator of each experiment. The OA and K values ranged from 89% to 95%, whereas the K values ranged from 0.75 to 0.95. The MRS parameter settings that provided the highest classification results (>94%) were analyzed, and class-specific accuracy measures and F-measure were computed. Multiscale AdaBoost classification was conducted on the basis of the computed F-measure values. Results of the multiscale AdaBoost classification demonstrated an improvement in OA, K, and F-measure.
来源出版物	10TH IGRSM INTERNATIONAL CONFERENCE AND EXHIBITION ON GEOSPATIAL & REMOTE SENSING
ISSN	1755-1307
出版年	2020
卷号	540
出版者	IOP PUBLISHING LTD
类型	Proceedings Paper
语种	英语
开放获取类型	gold
收录类别	CPCI-S
WOS记录号	WOS:000617132600090
WOS关键词	PARAMETER ; DISASTER
WOS类目	Environmental Sciences ; Geography, Physical ; Remote Sensing
WOS研究方向	Environmental Sciences & Ecology ; Physical Geography ; Remote Sensing
资源类型	会议论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/365572
作者单位	[Al-Ruzouq, Rami; Shanableh, Abdallah] Univ Sharjah, Dept Civil & Environm Engn, Sharjah 27272, U Arab Emirates; [Al-Ruzouq, Rami; Gibril, Mohamed Barakat A.; Shanableh, Abdallah] Univ Sharjah, GIS & Remote Sensing Ctr, Res Inst Sci & Engn, Sharjah 27272, U Arab Emirates; [Gibril, Mohamed Barakat A.] Univ Putra Malaysia, Fac Engn, Dept Civil Engn, Serdang 43400, Malaysia
推荐引用方式 GB/T 7714	Al-Ruzouq, Rami,Gibril, Mohamed Barakat A.,Shanableh, Abdallah. Self-adaptive Image Segmentation Optimization for Hierarchal Object-based Classification of Drone-based Images[C]:IOP PUBLISHING LTD,2020.