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.

如今，自动变化检测以及合成孔径雷达（SAR）图像的分类仍然是一个艰巨的过程。在现有研究中，斑点噪声（SN）的可用性，高时间消耗和低准确性是主要问题。为了解决这些问题，本文提出了一种新的利用基于径向基函数的深度卷积神经网络（RBF-DCNN）的SAR图像变化检测系统。所提出的方法包括六个阶段，即预处理，获得差异图像，像素级图像融合，特征提取（FE），特征选择（FS）以及利用分类器的变化检测（CD）。最初，使用NLMSTAF方法消除了输入SAR图像1和SAR图像2的噪声。后来，利用对数比算子（LRO）和高斯LRO获得差分图像，然后将获得的差分图像融合。接下来，从融合图像中提取LTrP，WST，边缘和MSER特征。从提取的那些特征中，使用Hybrid GWO-GA算法选择必要的特征。最后，将特征（选定的特征）输入到RBF-DCNN分类器，以检测图像中的变化。实验结果表明，在考虑现有系统的情况下，拟议的工作表现出更好的性能。最后，将特征（选定的特征）输入到RBF-DCNN分类器，以检测图像中的变化。实验结果表明，在考虑现有系统的情况下，拟议的工作表现出更好的性能。最后，将特征（选定的特征）输入到RBF-DCNN分类器，以检测图像中的变化。实验结果表明，在考虑现有系统的情况下，建议的工作表现出更好的性能。