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:: [WP-1 ADVANCED COMPUTING FOR INTERPRETATION]

Document type:: Multivolume work

Structure type:: Chapter

Chapter

Title:: LANDUSE CLASSES DISCRIMINATION WITH SATELLITE IMAGES BASED ON SPECTRAL KNOWLEDGE. Vladimir Cervenka , Karel Charvót

Document type:: Multivolume work

Structure type:: Chapter

328 relation, if this relation holds for subset S; and does not hold for the subset S>. The final decision (selection of the spectral relation for the node) is performed by the analyst. The tree classifier has several advantages, especially with multimodal data, because all features are not equally effective for the description of all target classes. Finaly, the computer program, which realizes the classification algorithm, is generated in an automatic way. It is relatively easy, because the classifier is always of any binary tree character- A3.1 data necessary for the tree generation are available in the spectral knowledge data base. Conclusion The method described has been successfully used at the Earth Remote Sensing Centre of the Geodetic and Cartographic Enterprise in Prague, especially for the classification of Thematic Mapper data. The main goal was to define the land use. Several thematic map from the Northeast Bohemia region have been produced. The best classification result was approximately 92 percent accuracy over 14 target classes (using the resubstitution estimate of error). The design of binary tree classifier based on spectral knowledge is rather a time consuming process. On the other hand, the own classification of image data does not require a large amount of computing time and storage. References [ 1 ] Wharton, S. W.: A Spectral-Knowledge-Based Approach for Urban Land-Cover Discrimination. IEEE Transactions on Geoscience and Remote Sensing, 1987, No. 3. [ 2 ] Charvdt, K., Cervenka, V., Soukup, P.: Using Statistical Tests for Computation of the Classification Parameters in Remote Sensing of Earth (in Czech). In: Application of Artificial Intelligence AI 87, DISK, Prague, 1937 . [ 3 ] Cervenka, V., Charvat, K., Soukup, P.: Automatic Interpretstion of TM Image Data for Defining the Land Use (in Czech). In: Application of Earth Remote Sensing Data in National Economy, CSVTS, Bratislava, 1390. [ 4 ] Crist, F. P., Ciccne, R. C.: A Physically-Based Transformation of TM Data - the Tasseled Cap. IEEE Transaction on Geoscience and Remote Sensing, 1584, No.3. [ 5 ] Richardson, A. J., Wiegand, C. L.: Distinguishing Vegetation from Soil Background Information. Photograrn. Eng. and Remote Sensing, 1 977 , pp. 1541-1552. [ 6 ) Dusek, D. A., Jackson, R. D., Musick, J. T.: Winter Wheat Vegetation Indices Calculated from Combinations of Seven Spectral Bands. Remote Sensing of Environment, 1985, pp. 255 - 267. [ 7 ] Cervenka, V., Charvat, K.: Nonparametric Classification Methods in Remote Sensing. In: Application of Artificial Intelligence AI 90, UISK, Prague, 1990. [ 8 ] Cervenka, V., Charvdt, K.: Classification of Multispectrad Imagery Based on Spectral Knowledge £in Czech). In: Digital Image Processing 89, CSVTS TESLA VUST A. S. Popova, Prague, 19S9.

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