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

229 3 EXPERIMENTS Areas of landcover changes are usually a little in object images. If a landcover map of the object area exists before analyses of a set of multi temporal images, most of pixels included in each category on the map should correspond to correct training area of its category. Thus roughly correct training area can be decided by overlaying the the existing landcover map on a object image in a set of multi temporal images. Training data of each category correspond to all pixels data in its training area on the object image. The used existing landcover map and obtained training data is named as a reference map and initial training data, respectively. Since a few number of pixels included in the initial training data may be incorrect training data because of land- cover change between the reference map and the object image, it is necessary to remove these pixels from the initial training data. Those pixels are named as change pixels. Change pixels can be detected by searching pixels at a long distance from a centroid of each category in feature space. And classification classes, which change pixels should be assigned, can be generated by using clustering methods. Consequently, we may use the data combined generated classes by clustering for change pixels and remaining initial training data after removing of change pixels as final training data. As a result of above discussions, a following five stages procedure can be obtained(Fig. 1 ) . At the first stage, initial training data are extracted by overlaying a reference map on each object image. If a reference map can not be prepared, one object image in a set of multi temporal image data should be classified using a conventional method. And the classified result may be used as reference map. At the second stage, change pixels are extracted from the initial training data. In this study, Mahalanobis' distance is used to search change pixels. At the third stage, clustering is performed for change pixels and new classification classes are generated. At the fourth stage, final training data are construct ed by combining generated new classes and remaining of the initial training data. At the final stage, a object image is classified using obtained final training data. Maximum likelihood classifier are used in this study. Multi temporal land- cover maps can be obtained by applying this procedure to each image of a set of multi temporal images. In order to evaluate the proposed automatic classification procedure, following four temporal Landsat TM data were classified by using this method and a conventional method. [object area] Sagami River basin(12.8Kmxl2.0km) [observation date] Nov.4(1984), Jan.23(1985), Aug.6(1986), May 21(1987) [image size] 512x480 pixels, 25mx25m/pixel [channels] 1 , 2,3,4,5, and 7 Classification results of conventional method are used to compare to classifi cation results of proposed method. And also, there are used as reference maps. The reason of preparing four reference maps is to evaluate dependences of a used reference map. Items in left hand side in Table 1 shows classification categories which were used in the conventional classification. The number of categories are fourteen. But the total number of classification classes are about sixty since each category has several cub-classes. Classification accuracies were estimated quantitatively by using digital test site data as shown in Figure 2. Test site data contain about 60 1 and-cover/use categories. As the categories used in land-cover/use test site data differ from those used in landcover classifications, fourteen landcover categories were merged to five major categories as shown in Table 1. Accuracy evaluations were performed based on these five major categories. Change pixels were extracted by searching pixel which exist at longer than 2.5 Mahalanobis' distance from a centroid of each categories containing these pixels in the initial training data. Figure 2 shows obtained change pixels of each temporal image. Change pixels had a area about 20%-30% for each temporal image. About 40 to 60 classes were generated by clustering for change pixels. Used clustering method is a hierarchical Ward method, namely, the number of classifi cation classes of final training data is about 100-120 classes. Table 2 shows estimated classification accuracies of classification results of the conventional methods and the proposed method for each temporal image. Mean accuracies shown in Table 2 were area weighted mean values for each category. The results of conventional method were derived by using training data which are extracted and modified by skilled operator. The skilled operator performed detailed ground truth before training area selection and during modification procedure of extracted training data.

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