A new training approach based on ECOC-SVM for SAR image retrieval. (28th July 2021)

• Record Type: Journal Article
• Authors: Krishna, G. Siva; Prakash, N.
• Is Part Of: International journal of intelligent enterprise. Volume 8:Number 4(2021)
• Page Start: 492
• Page End: 517
• Publication Date: 2021-07-28
• Subjects: SAR image retrieval -- synthetic aperture radar -- SAR -- error correcting code based SVM -- feature extraction -- soft threshold -- particle swarm optimisation -- PSO -- denoising; Organizational learning -- Periodicals; Knowledge management -- Periodicals; Business intelligence -- Periodicals; Business enterprises -- Design -- Periodicals; Organizational effectiveness -- Periodicals; 658.005
• Languages: English
• ISSNs: 1745-3232

Abstract

Synthetic aperture radar (SAR) is a high-resolution remote sensing imagery which is used in environment monitoring, earth-resource mapping and military systems. The objective of the paper is to perform the image retrieval of the SAR by using the error correcting code based support vector machine (ECOC-SVM) and denoising of SAR images. The features from the SAR images as texture, colour and shape are extracted by using different techniques such as texture spectrum (TS), grey level difference method (GLDM), scale-invariant feature transform (SIFT) and hue, saturation, value (HSV) model. The bi orthogonal wavelet transform (BWT) with particle swarm optimisation (PSO) is used for optimising the soft threshold for denoising the SAR images. The result is analysed with one existing method named IIRM in terms of average accuracy of proposed the SAR image retrieval and precision 98.438%, 70.31% is high for ocean image compared with the average precision of IIRM method that is 63.5%.