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A novel synthetic aperture radar image change detection system using radial basis function-based deep convolutional neural network

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Abstract

Today, the automatic change detection and also classification as of the Synthetic Aperture Radar (SAR) images remain a hard process. In the existing research, the availability of Speckle Noise (SN), high time-consumption, and low accuracy are the chief issues. To resolve such issues, this paper proposed a novel SAR image change detection system utilizing a Radial Basis Function-based Deep Convolutional Neural Network (RBF-DCNN). The proposed methodology comprises six phases, namely, pre-processing, obtaining difference image, pixel-level image fusion, Feature Extraction (FE), Feature Selection (FS), and also change detection (CD) utilizing the classifier. Initially, the noise is eliminated as of the input, SAR image 1 and SAR image 2, utilizing the NLMSTAF approach. Subsequently, the difference image is attained by utilizing a Log-ratio operator (LRO) and Gauss-LRO, and the attained difference image is then fused. Next, the LTrP, WST, edge, and MSER features are extracted from the fused image. As of those features that were extracted, the necessary features are selected utilizing the Hybrid GWO-GA algorithm. The features (selected) are finally inputted to the RBF-DCNN classifier for detecting the changes in an image. Experimental outcomes established that the proposed work renders better performance on considering the existing system.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Sethu Institute of Technology, Kariapatti, Virudhunagar, India

    B. Pandeeswari & M. Parvathy

  2. Department of Computer Science and Engineering, AAA College of Engineering and Technology, Sivakasi, India

    J. Sutha

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  1. B. Pandeeswari
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  2. J. Sutha
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  3. M. Parvathy
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Correspondence to B. Pandeeswari.

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Pandeeswari, B., Sutha, J. & Parvathy, M. A novel synthetic aperture radar image change detection system using radial basis function-based deep convolutional neural network. J Ambient Intell Human Comput 12, 897–910 (2021). https://doi.org/10.1007/s12652-020-02091-y

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  • DOI: https://doi.org/10.1007/s12652-020-02091-y

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