Soil Erosion Remote Sensing Image Retrieval Based on Semi-Supervised Learning

Soil erosion is one of the most typical natural disasters in China. However, due to the limitation of current technology, the investigation of soil erosion through remote sensing images is currently by human beings manually which depends on human interpretation and interactive selection. The work burden is so heavy that errors are usually inevitably unavoidable. This paper proposes the technique of content-based image retrieval to tackle this problem. Due to the large amount of computation in co-training retrieval based on multiple classifier systems, and for the purpose of improving efficiency, an improved approach using co-training in two classifier systems is proposed in this paper. Prior to retrieving, we firstly select the optimal color feature and texture feature respectively, and then use the corresponding color classifier and texture classifier for co-training. By this approach, the time of co-training is reduced greatly, meanwhile, the selected optimal features can represent color and texture features better for remote sensing image, resulting in better retrieval accuracy. Experimental results show that the improved approach using co-training in two classifier systems needs less amount of computation and less retrieval time, while it can lead to better retrieval results.