Abstract
Automatic Land Usage Identification is one of the most demanded research areas in Remote Sensing. One of the primitive sources for Land Usage Identification is Aerial images. Automatic Land Usage Identification is implemented by exploring different feature extraction methods whereas, these features are categorized into local and global content description of image. Fusion of local and global features may be a potential approach for land usage identification. Accordingly, the major contribution of work presented here is fusion of global color features extracted using TSBTC n-ary method (applied on entire image) and local features extracted using Bernsen thresholding method applied on 3*3 windows of image for land usage identification. Consideration of more than one machine learning classifiers as an ensemble has shown better results than that of individual machine learning classifiers. In proposed work here, Thepade’s Sorted n-ary Block Truncation Coding (TSBTC n-ary) is explored in aerial image feature extraction with nine variations from TSBTC 2-ary till TSBTC 10-ary. The performance appraise of proposed Land Usage Identification technique is done using UC Merced Dataset having 2100 images categorized into 21 land usage types. In consideration performance measures like Accuracy, F Measure and Matthews Correlation Coefficient (MCC); the TSBTC 10-ary global features extraction method has given better land usage identification as compare to Bernsen thresholding local feature extraction method. The proposed method enhances the identification of land usage through feature level fusion of TSBTC 10-ary global features and Bernsen thresholding local features. Along with nine individual machine learning algorithms, ensembles of varied machine learning algorithms are used for further performance improvement of the proposed land usage identification technique.
Highlights
In today’s era of satellites, Drones and Unmanned Aerial Vehicle (UAV); the technology allows acquisition of aerial imagery with details about what is there on the earth surface.Revised Manuscript Received on February 05, 2020
The 228 variations of proposed technique are explored with set of nine machine learning algorithms and three ensemble combinations, nine variations of using TSBTC n-ary global feature level extraction method, Bernsen local feature level extraction method and nine variations of feature level fusion of global and local features
Performance based on percentage accuracy for proposed land usage identification technique using different variations of TSBTC n-ary global level feature extraction method is given in Fig. 3 for examined machine learning algorithms and ensembles
Summary
In today’s era of satellites, Drones and Unmanned Aerial Vehicle (UAV); the technology allows acquisition of aerial imagery with details about what is there on the earth surface. The advancements in machine learning may help for automatic detection of the types of land usage through such aerial images. The paper uses appropriate machine learning algorithms to understand the extracted features from aerial images to uncover the usages of land in aerial images. Along with individual machine learning algorithms, the ensembles are being explored to get better performance.
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: International Journal of Engineering and Advanced Technology
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
