期刊名称：ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
印刷版ISSN：2194-9042
电子版ISSN：2194-9050
出版年度：2012
卷号：XXXIX-B3
页码：223-226
DOI：10.5194/isprsarchives-XXXIX-B3-223-2012
出版社：Copernicus Publications
摘要：Now that the agricultural facilities are being increase owing to development of technology and diversification of agriculture and the ratio of garden crops that are imported a lot and the crops cultivated in facilities are raised in Korea, the number of vinyl greenhouses is tending upward. So, it is important to grasp the distribution of vinyl greenhouses as much as that of rice fields, dry fields and orchards, but it is difficult to collect the information of wide areas economically and correctly. Remote sensing using satellite imagery is able to obtain data of wide area at the same time, quickly and cost-effectively collect, monitor and analyze information from every object on earth. In this study, in order to analyze the utilization of satellite imagery at detection of agricultural facility, image classification was performed about the agricultural facility, vinyl greenhouse using Formosat-2 satellite imagery. The training set of sea, vegetation, building, bare ground and vinyl greenhouse was set to monitor the agricultural facilities of the object area and the training set for the vinyl greenhouses that are main monitoring object was classified and set again into 3 types according the spectral characteristics. The image classification using 4 kinds of supervise classification methods applied by the same training set were carried out to grasp the image classification method which is effective for monitoring agricultural facilities. And, in order to minimize the misclassification appeared in the classification using the spectral information, the accuracy of classification was intended to be raised by adding texture information. The results of classification were analyzed regarding the accuracy comparing with that of naked-eyed detection. As the results of classification, the method of Mahalanobis distance was shown as more efficient than other methods and the accuracy of classification was higher when adding texture information. Hence the more effective monitoring of agricultural facilities is expected to be available if the characteristics such as texture information including satellite images or spatial pattern are studied in detail
