Proceedings of the Symposium on Global and Environmental Monitoring: Proceedings of the Symposium on Global and Environmental Monitoring

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Persistent identifier:: 856665355

Title:: Proceedings of the Symposium on Global and Environmental Monitoring

Sub title:: techniques and impacts ; September 17 - 21, 1990, Victoria Conference Centre, Victoria, British Columbia, Canada

Year of publication:: 1990

Place of publication:: Victoria, BC

Publisher of the original:: [Verlag nicht ermittelbar]

Identifier (digital):: 856665355

Language:: English

Document type:: Multivolume work

Volume

Persistent identifier:: 856669164

Title:: Proceedings of the Symposium on Global and Environmental Monitoring

Sub title:: techniques and impacts; September 17 - 21, 1990, Victoria Conference Centre, Victoria, British Columbia, Canada

Scope:: XIV, 912 Seiten

Year of publication:: 1990

Place of publication:: Victoria, BC

Publisher of the original:: [Verlag nicht ermittelbar]

Identifier (digital):: 856669164

Illustration:: Illustrationen, Diagramme, Karten

Signature of the source:: ZS 312(28,7,1)

Language:: English

Usage licence:: Attribution 4.0 International (CC BY 4.0)

Editor:: International Society for Photogrammetry and Remote Sensing, Commission of Photographic and Remote Sensing Data

Publisher of the digital copy:: Technische Informationsbibliothek Hannover

Place of publication of the digital copy:: Hannover

Year of publication of the original:: 2016

Document type:: Volume

Collection:: Earth sciences

Chapter

Title:: [THP-1 HIGH SPECTRAL RESOLUTION MEASUREMENT]

Document type:: Multivolume work

Structure type:: Chapter

Chapter

Title:: OPTIMIZATION OF UNSUPERVISED LEARNING FOR HIGH RESOLUTION REMOTE SENSING DATA. SHIMODA,HARUHISA; MATSUMAE,YOSHIAKI; NATENOPPARATH,AMNARJ; FUKUE,KIYONARI; SAKATA,TOSHIBUMI

Document type:: Multivolume work

Structure type:: Chapter

585 OPTIMIZATION OF UNSUPERVISED LEARNING FOR HIGH RESOLUTION REMOTE SENSING DATA SHIMODA,HARUHISA; MATSUMAE,YOSHIAKI; NATENOPPARATH,AMNARJ; FUKUE,KIYONARI; SAKATA,TOSHIBUMI Tokai University Research & Information Center, Japan ABSTRACT Supervised learning methods have been widely used for land cover classification of satellite imagery. However, these methods have two big problems. First, training areas are selected arbitrary by operators though statistical theory requires random sampling. Second, it is very difficult for high resolution images such as Landsat TM data to extract sufficient number of training classes which are composed of only spectrally pure objects. Unsupervised learning with the aid of clustering can solve above problems. However, we do not have sufficient information to use clustering efficiently for high resolution data, i.e. (1) how many number of sampling data is necessary, (2) how many number of clusters should be generated. In this research, in order to answer to these questions, classification experiments of Landsat TM data were conducted. From the experiments, following results were obtained. (1) There exists the optimal number of clusters according to the sample size. (2) The method used to assign categories to each cluster dominates the classification accuracy. KEY WORDS:Clustering, Unsupervised learning. High resolution data 1 INTRODUCTION With the launch of second generation high resolution sensors like Thematic Mapper(TM) and HRV, many kinds of researches have been done to certificate the capability of these sensors for land use classification. Most of the results of these studies have shown that classification accuracies using these sensors are not so high as expected when applying conventional supervised maximum likelihood classifier using only spectral information. These results have made many researchers to study spatial features like textures or more sophisticated classifier like expert systems, Dempster-Shafer rule or fuzzy classi fiers. In addition, those results also have shown the limitations of supervised learning system. As well known, supervised training area selections cannot be assured as random samples on which all the statistical method are based upon. This problem has not been emphasized in case of treating low resolution images like MSS. It is mainly because the image itself has been composed of rather homogeneous areas made from averaging process. In the case of high resolution sensors, this problem become hi-lightened. In many land use classification studies, estimated classification accuracies calculated from confusion matrix of training data has been very high (usually 90 to 98%) while accuracies estimated from independent samples were very low (typically 60 to 70%). These results apparently show the fact that training data have not actually represented the statistics of their populations. Furthermore, in order to obtain high classification accuracies, an operator should select more than 50 classification classes for high resolution sensor data. From the stand point of operational image processing, this tendency that number of training classes are largely increasing will make the process almost impossible as a matter of fact. From the above reasons, unsupervised learning process or actually speaking, clusterings come to very important tool for land use classification of high resolution data. However, most of the studies on clusterings were mainly concerned about low resolution data like MSS, and optimal conditions or at least the least condition using clustering for high resolution sensor data are not well known. The purpose of this study is to obtain fundamental knowledge about the nature of clustering for high resolution data, and clarify the influence of sampling data size and number of clusters to the classification accuracy. 2 DATA USED IN THE EXPERIMENTS 2.1 Image Data Image data used in the experiments are shown in Fig.l and its specifications are shown below. sensor : Landsat TM date : 1984/Nov./4 path-row : 107-35 area : Hiratuka area Japan pixel size : 25m image size : 512 x 480

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