Journal article Open Access

Identifying Land Usage from Aerial Image using Feature Fusion of Thepade's Sorted n-ary Block Truncation Coding and Bernsen Thresholding with Ensemble Methods

Sudeep D. Thepade; Piyush R. Chaudhari; Rik Das

Sponsor(s)
Blue Eyes Intelligence Engineering & Sciences Publication (BEIESP)

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.

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