For the past few years, the automated addressing of changes in remote sensing images plays a significant role. However, the change detection (CD) model often suffers from the issue of speckle noise. More investigations have been proceeded to overcome this obstacle. This paper also considers the same issue and proposes a new CD model in synthetic aperture radar (SAR) images. Here, two SAR images that are captivated at different times will be considered as the input of the detection process. At first, discrete wavelet transform is incurred for image fusion, where the coefficients are optimally selected through a hybrid model that hybridizes the gray wolf optimization and dragonfly (DA) optimization. At last, the fused images after inverse transform are clustered via the fuzzy c-mean (FCM) clustering approach, and a similarity measure is performed between the segmented image and the ground truth image. The proposed model, wolf hunting-based DA with FCM, compares its performance over other conventional methods in terms of measures like accuracy, specificity, sensitivity, precision, negative predictive value, F₁ score and Matthews correlation coefficient. Similarly, the negative measures are false positive rate, false negative rate and false discovery rate, and the betterment is proven.

中文翻译：

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WHDA-FCM：具有模糊C均值聚类的基于狼狩猎的蜻蜓，用于SAR图像中的变化检测

在过去的几年中，自动处理遥感图像中的变化起着重要作用。但是，变化检测（CD）模型通常会受到斑点噪声的困扰。为了克服这一障碍，已经进行了更多的研究。本文还考虑了相同的问题，并提出了合成孔径雷达（SAR）图像中的新CD模型。在这里，在不同时间被捕获的两个SAR图像将被视为检测过程的输入。首先，需要进行离散小波变换进行图像融合，其中通过将灰狼优化和蜻蜓（DA）优化混合​​的混合模型来最佳选择系数。最后，通过模糊c均值（FCM）聚类方法对逆变换后的融合图像进行聚类，并且在分割图像与地面真实图像之间进行相似度测量。所提出的模型（带有基于FCM的基于Wolf Hunt的DA）在准确性，特异性，敏感性，精确度，阴性预测值，F ₁得分和马修斯相关系数。同样，消极措施是假阳性率，假阴性率和错误发现率，并证明了改进。