Rough Clustering Based Unsupervised Image Change Detection
This addresses the problem of detecting changes in images over time for applications like remote sensing, but it appears incremental as it builds on existing rough set concepts.
The paper tackles unsupervised change detection in multitemporal images by using rough clustering, resulting in a less noisy method that avoids prior knowledge about region distributions.
This paper introduces an unsupervised technique to detect the changed region of multitemporal images on a same reference plane with the help of rough clustering. The proposed technique is a soft-computing approach, based on the concept of rough set with rough clustering and Pawlak's accuracy. It is less noisy and avoids pre-deterministic knowledge about the distribution of the changed and unchanged regions. To show the effectiveness, the proposed technique is compared with some other approaches.