Fast Landform Position Classification to Improve the Accuracy of Remote Sensing Land Cover Mapping

Abstract

With the increase in the availability of high resolution remote sensing imagery, land cover classification and mapping by high-resolution remote sensing images is becoming an increasingly useful technique for providing a large area of detailed land-cover information. High-resolution images have the characteristics of abundant geometric and detail information, which are beneficial to detailed classification and mapping. However, in such images, similar features may present different land-cover types in various topographic positions, but these differences are hard to recognize in high remote sensing images. When dealing with such problems, ground surveys or rough classifications of elevations are common methods. Ground surveys are time and labor consuming and lack strong real-time capability. A rough classification cannot reflect subtle changes in terrain. In order to make full use of characteristics of high remote sensing images and avoid their insufficient, a topographic position index landform position classification method is utilized in this research. The meaning of using this method is to reduce the amount of misclassification and wrongly mapping land cover types. The Topographic Position Index landform position classification method compares the elevation of each pixel in a digital elevation model to the mean elevation of the neighborhood and defines landform position class of the each pixel. Such landform position classification method allows a variety of nested landforms to be distinguished. This gives a new input for remote sensing land cover classification and mapping. The experimental results in this research proved that a GaoFen-1（GF-1）remote sensing image land cover classification accuracy is significantly improved by using the Topographic Position Index landform position classification method after image segmentation and classification.