Study on the Accuracy of Agricultural Landuse Classification by Multi-scale Remote Sensing Imagery

• Main Authors: Shiu Jia Sheng, 徐家盛
• Other Authors: 陳莉
• Format: Others
• Language: zh-TW
• Published: 2003
• Online Access: http://ndltd.ncl.edu.tw/handle/87169110533926753293

碩士 === 中華大學 === 土木工程學系碩士班 === 91 === As the widespread of remote sensing application and the availability of various commercial satellites, there is an increasing number of satellite images could be adopted in research. Each satellite provides different image and scale characteristics. Generally speaking, large scale image provides wide range of information while small scale image provides local and detailed information. It should be able to find the most appropriate image scale for the purpose of different applications. The scale transfer is to convert one image scale to another in order to take advantage of the benefit from each image scale. The purpose of the study was to use the bilinear interpolation and the cubic convolution scale transferring techniques, converting high resolution Quick Bird image to 1, 2, 4, 10, 30, 100, and 250 m resolutions, respectively, and to examine the accuracy of the classified images, in order to find the most appropriate image scale for agricultural use in Taiwan. The results showed that the high resolution images after downscale transfer had not necessary providing more information. The pixels were just interpolated into a finer resolution. As a result, the disk space to hold the images increased exponentially. The up-scaled images tend to aggregate some of ground information, so that they seemed fuzzy and smooth. The overall accuracy of the paddy field land use classification indicated that the most appropriate image scale emerged at the size of land parcel. It showed no apparent difference between bilinear interpolation and cubic convolution methods. The results of the study indicated that it is not the higher the better for image scale. The scale should be in accordance with the ground characteristics. High resolution images usually denote high cost, smaller coverage, and longer processing time. According to the results of the study, it is not necessary to use very high resolution images for paddy field classification. A resolution approximately to the size of land parcel is the most appropriate since it reduces the cost while maintaining enough accuracy.