A deep learning-based SAR image change detection using spatial intuitionistic fuzzy C-means clustering
With the rapid progress of technologies in the arena of remote sensing and satellite imagery, Synthetic Aperture Rader (SAR) images have become an important source of data for research concerning changed detection. Out of the numerous techniques and approaches available for change detection in a particular location, most of them are initially targeted toward producing the difference image. In this article, a change detection approach is suggested that produces the result without finding a difference image. We are motivated toward the design of such an approach to reduce the effect of the difference image. In this method, we generate the training and testing sample for CNN classification directly from the original SAR image without any pre-processing operations. This reduces the effect of the difference image on the final classification result. Since traditional fuzzy c means (FCM) are highly susceptible to noise and do not give desired results, we use spatial fuzzy c means (sFCM) with an intuitionistic approach. The intuitionistic approach refers to the degree of hesitation resulting from a lack of information. The approach is less hampered by noise and yields better outcomes. The basic idea of this method is to find false levels using spatial intuitionistic fuzzy c means clustering. Thereafter, the CNN is trained using the samples that are selected from the original samples using false labels. Finally, the classification results are generated through the trained CNN. Investigational results using the proposed approach show promising results when applied over two SAR image datasets compared to several existing methodologies.