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:: [WA-3 DEMOGRAPHIC AND URBAN APPLICATIONS]

Document type:: Multivolume work

Structure type:: Chapter

Chapter

Title:: HIGH ACCURACY AUTOMATIC CLASSIFICATION OF MULTI TEMPORAL DATA. Haruhisa SHIMODA, Kiyonari FUKUE, Tukasa HASHINO, and Toshibumi SAKATA

Document type:: Multivolume work

Structure type:: Chapter

HIGH ACCURACY AUTOMATIC CLASSIFICATION OF MULTI TEMPORAL DATA Haruhisa SHIMODA, Kiyonari FUKUE, Tukasa HASHINO, and Toshibumi SAKATA Tokai University Research and Information Center 2-28-4 Tomigaya, Shibuya-ku, Tokyo 151, Japan ABSTRACT A new automatic landcover classification method for multi temporal data is presented in this paper. This method is based on prior condition that a landcover map of the target area already exists, or at least one of the multi temporal data had been classified. The landcover map or the classified result is used as a reference map to specify training areas of classification categories. The new classification method is consist of five steps, i.e., extraction of training data using the reference map, change detection based upon the homogeneity of training data, clustering of image data within areas where changes were detected, reconstruction of training data using overall image data, and maximum likelihood classification. In order to evaluate the performance of this method, temporal Landsat TM data were classified by using this method and a conventional method. As a result, we could get a landcover change with high reliability and fast throughput, without a skilled operator. Key Words; multi temporal data, change detection, landcover classification, automatic training data selection 1 INTRODUCTION Since the launch of Landsat-1 on 1972, enormous observation data have been integrated in various organizations such as receiving stations, institutes, and application users. Many studies have been trying in order to utilize those data with more effectively, e.g. EosDIS(Earth Observing System Data and Information System) and GIS(geographical Information System). It is assumed that processing technology for multi temporal data have become important from a view point of above the present condition. Utilization objectives of multi temporal data can be divided into two categories; (1) to improve analyzing accuracies, and (2) to analyze change of the earth surface such as land-cover/use, and sea surface temperature. This research presented in this paper is concerned about the later objective, especially for land-cover/use changes. Following two methods have been usually used as change detection methods of land- cover/use; (l)a method detecting change areas from observation image data directly, and (2)a method detecting those from multi temporal thematic maps. In general speaking, the first methods can not detect change areas exactly. One of the reasons due to that it is difficult to compensate influences of variation about observation conditions such as sun elevation, atmospheric condition, and so on. The other one depends on that spectral features of a land-cover/use may change according as seasonal changes, e.g. leaf color of forests, grass rankness and water contents of bare grounds, growing situation of crops, and etc.. On the other hand, the second change detection method has potential to eliminate those problems. However, there are several problems on conventional supervised methods of land- cover classification for a set of multi temporal image data. First, training data must be extracted by a skilled operator for each temporal image in order to get high classification accuracies. Second, the operator should perform ground truth before training data extraction. As well known, ground truth and training data extraction are highly time consuming processes. Third, it is difficult to extract sufficient training data using supervised method especially for high ground resolution sensor data such as Landsat TM and SPOT HRV, because of large variation of image level with pixel-by- pixel/1,2/. For instance, value of pixels in low density urban areas have very different values by a reason which those area are consisted of many kinds of landcovers, e.g. house roofs with several colors, lawns, trees, concretes, asphalts, bare grounds, etc.. Further, it is difficult to analyze landcover changes because of unstable classification accuracies for each temporal data. A method overcoming these problems for conventional supervised classifications is presented in this paper. 2 PROPOSED AUTOMATIC CLASSIFICATION METHOD The major problems on supervised methods of landcover classification for a set of multi temporal image data are induced from supervised training data extraction, as described at previous section. There fore, in order to solve those problems, it is necessary to automate the process of training area selection and adjusting of training data according to each multi temporal image. 228

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