A Refined Quadtree-based Automatic Classification Method for Remote Sensing Image
In pixel-based remote sensing image classification, the long processing time limits application of classification. Image segmentation is adopted to accelerate the classification speed. Image segmentation is a procedure of dividing an image into separated homogenous regions. These regions are considered as objects to be classified. A refined quadtree-based segmentation algorithm is proposed in the paper. The windowed aggregation method is designed to solve the problem of over-segmentation, which occurs in quadtree-based segmentation. A spot 5 remote sensing image in Qingdao was selected as the test image. Three experiments were implemented on the test image: the first is pixel-based classification; the second is quadtree-based classification; the third is refined quadtree-based classification. The pixel-based classification obtains the highest accuracy while takes more time. The refined quadtreebased classification is superior to quadtree-based classification in time consumed and accuracy.
remote sensing image image segmentation classification
Liu Jinmei Wang Guoyu
School of Information Science and Engineering Ocean University of China Qingdao.China School of Scie School of Information Science and Engineering Ocean University of China Qingdao,China
国际会议
哈尔滨
英文
1703-1706
2011-12-24（万方平台首次上网日期，不代表论文的发表时间）