Land cover classification is an important and basis work to desertification monitoring and control in the desertification area. In this paper, a hierarchical object oriented classification method for extracting the information of sandy land with ALOS Imagery is presented. The ample
spectral features, textural features and customized features such as Vegetation Coverage are used synthetically. There are two steps in the hierarchical object oriented method. Firstly, the image is segmented with multi-resolution segmentation method. Secondly, the hierarchical classification
rules are constructed, and the different features are used to distinguish sandy land and other typical land cover types in the nodes of the hierarchical classification rules, while sandy land is classified into mobile sand, semi-fixed sand and fixed sand. Through the confusion matrix, the
total accuracy of the method proposed in this paper is enhanced from 69.73% (pixel-based minimum distance classification), 80.25% (object oriented nearest neighbor classification) to 85.19%. The result indicates that the hierarchical method with specific features gains a high classification
accuracy and is with a high degree of automation.
The growing interest and activity in the field of sensor technologies requires a forum for rapid dissemination of important results: Sensor Letters is that forum. Sensor Letters offers scientists, engineers and medical experts timely, peer-reviewed research on sensor science and technology of the highest quality. Sensor Letters publish original rapid communications, full papers and timely state-of-the-art reviews encompassing the fundamental and applied research on sensor science and technology in all fields of science, engineering, and medicine. Highest priority will be given to short communications reporting important new scientific and technological findings.