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Synthetic aperture radar (SAR) plays a vital role in change detection (CD) analysis due to the ability to produce remote sensing images throughout the day, regardless of weather conditions. Nowadays, deep learning methods have gained popularity in multitemporal SAR image CD applications. However, false labels generated during the preclassification stage limit the performance of the CD process. This work employs a fast and robust fuzzy c-means clustering to generate the pseudo-label samples and lightweight spatial pyramid pooling attention network (SPPANet) to detect changes in multitemporal SAR images. The spatial pyramid structure in SPPANet applies adaptive pooling layers to provide better contextual information without incurring computational overhead. The log-ratio operator is used to generate the difference image (DI), and the pseudo-label samples are created from DI. The pseudo-label samples are used to create the training and testing patches. Finally, the trained SPPANet is used to classify testing samples into unchanged and changed classes. The SPPANet achieves an accuracy of 98.70%, 99.06%, 96.40%, and 99.10% for Ottawa, San Francisco, Yellow River, and Farmland datasets, respectively. [ABSTRACT FROM AUTHOR]