Application of secure semi-supervised fuzzy clustering in object detection from remote sensing images

Abstract

In recent years, landslides are taking place very seriously, and tend to increase in both scope and scale, threatening people's lives and properties. Therefore, timely detection of landslide areas is extremely important to minimize damage. There are many ways to detect landslide areas, in which the use of satellite images is also an option worthy of attention. When performing satellite image data collection, there are many outliers, such as weather, clouds, etc. that can reduce image quality. With low quality images, when executing the clustering algorithm, the best clustering performance will not be obtained. In addition, the fuzzy parameter is also an important parameter affecting the results of the clustering process. In this paper will introduce an algorithm, which can improve the results of data partitioning with reliability and multiple fuzzifier. This algorithm is named TSSFC. The introduced method includes three steps namely as “labeled data with FCM”, “Data transformation”, and “Semi supervised fuzzy clustering with multiple point fuzzifiers”.

The introduced TSSFC method will be used for landslide detection. The obtained results are quite satisfactory when compared with another clustering algorithm, CS3FCM (Confidence-weighted Safe Semi-Supervised Clustering).