Change detection (CD) is a process of identifying dissimilarities from two or more co-registered multitemporal images. In this paper, we have introduced a $\alpha$-cut induced Fuzzy layer to the Deep Neural Network ($\alpha$FDNN). Deep neural networks for change detection normally rely on the pre-classified labels of the clustering. But the pre-classified labels are more coarse and ambiguous, which is not able to highlight the changed information accurately. This challenge can be addressed by encapsulating the local information and fuzzy logic into the deep neural network. This takes the advantage of enhancing the changed information and of reducing the effect of speckle noise. As the first step in change detection, a fused difference image is generated from the mean and log ratio image with the advent of Stationary Wavelet Transform (SWT). It not only eliminates the impact of speckle noise but also it has good ability to identify the trend of change thanks to the shift invariance property. Pseudo classification is performed as the next step using Fuzzy C Means (FCM) clustering. Then, we apply reformulated $\alpha$-cut induced Fuzzy Deep Neural Network to generate the final change map which facilitates a final representation of data more suitable for the process of classification and clustering. It also results into a noteworthy improvement in the change detection result. The efficacy of the algorithm is analyzed through the parameter study. Experimental results on three Synthetic Aperture Radar (SAR) datasets demonstrate the superior performance of the proposed method compared to state-of-the art change detection methods.

变更检测（CD）是从两个或多个共同注册的多时间图像中识别出差异的过程。在本文中，我们介绍了$\alpha$切诱导的模糊层进入深层神经网络（$\alpha$FDNN）。用于变化检测的深度神经网络通常依赖于聚类的预分类标签。但是预分类的标签更加粗糙和模糊，无法准确地突出显示更改的信息。可以通过将本地信息和模糊逻辑封装到深度神经网络中来解决此挑战。这具有增强改变的信息并减少斑点噪声的效果的优点。作为变化检测的第一步，随着平稳小波变换（SWT）的出现，从均值和对数比图像生成融合差分图像。它不仅消除了斑点噪声的影响，而且由于位移不变性，还具有识别变化趋势的良好能力。下一步，使用模糊C均值（FCM）聚类执行伪分类。然后，我们重新制定$\alpha$剪切诱导的模糊深度神经网络生成最终变化图，该图便于最终表示更适合分类和聚类过程的数据。这也导致变化检测结果的显着改善。通过参数研究分析了算法的有效性。在三个合成孔径雷达（SAR）数据集上的实验结果表明，与最新的变化检测方法相比，该方法具有更好的性能。