Fusion of sensor data, knowledge sources and algorithms for extraction and classification of topographic objects

baltsavias, emmanuel p.

Bibliographic data

Monograph

Persistent identifier:: 856473650

Author:: Baltsavias, Emmanuel P.

Title:: Fusion of sensor data, knowledge sources and algorithms for extraction and classification of topographic objects

Sub title:: Joint ISPRS/EARSeL Workshop ; 3 - 4 June 1999, Valladolid, Spain

Scope:: III, 209 Seiten

Year of publication:: 1999

Place of publication:: Coventry

Publisher of the original:: RICS Books

Identifier (digital):: 856473650

Illustration:: Illustrationen, Diagramme, Karten

Language:: English

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

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:: Monograph

Collection:: Earth sciences

Chapter

Title:: INTERACTIVE SESSION 1 IMAGE CLASSIFICATION

Document type:: Monograph

Structure type:: Chapter

Chapter

Title:: COMBINING SPECTRAL AND TEXTURAL FEATURES FOR MULTISPECTRAL IMAGE CLASSIFICATION WITH ARTIFICIAL NEURAL NETWORKS. H. He , C. Collet

Document type:: Monograph

Structure type:: Chapter

International Archives of Photogrammetry and Remote Sensing, Vol. 32, Part 7-4-3 W6, Valladolid, Spain, 3-4 June, 1999 181 Landcover and Landuse Classes Classification based on Spectral bands Classifications based on Spectral and Textural bands from matrix 1 Classifications based on Spectral and Textural bands from matrix 2 Crop 1 Crop 2 Trees Grass Parks Apartment-block areas Low density urban areas High density urban areas Water cl c2 c3 c4 c5 c6 c7 c8 c9 90 82 86 90 82 88 96 88 88 78 80 84 84 84 90 96 92 82 98 90 96 92 90 94 96 90 92 98 88 94 94 92 96 94 94 80 68 76 80 85 78 80 92 94 78 68 72 76 78 74 84 96 96 84 64 68 72 78 72 86 90 94 86 62 70 74 76 74 78 92 94 90 98 90 94 92 90 98 96 94 80 Total 80.4 82 84 84.2 81.7 88.2 94.2 92.9 84.4 Table 3. Classification accuracy (%) for different classification methods and window sizes. 4. CONCLUSIONS The classification accuracy with the new procedure (termed procedure 3) is 94.2%, 10% higher than the accuracy achieved using procedures 1 and 2. This reveals that textural features derived from multispectral images are a very valuable source of spatial information and an important clue for landcover and landuse classification. The grey level co-occurrence matrix for textural measure calculations is an important factor, which affects the fidelity of textural features. The textural measures based on matrix 2 can more effectively reveal spatial forms of landcover and landuse types in multispectral images. An inappropriate window size can reduce the classification accuracy, and the window sizes of 5 x 5 and 7x7 can be considered, based on the performed tests, as the appropriate ones for this set of landcover and landuse categories. The new procedure is particularly suitable for classification of images containing complex spectral components, like urban regions. REFERENCES Baraldi A. and Parmiggiani F., 1990. Urban area classification by multispectral SPOT images. IEEE Transactions on Geoscience and Remote Sensing, 28(4): 674-679. Benediktsson E.B., Swain P.H. and Ersoy O.K., 1990. Neural network approaches versus statistical methods in classification of multi-source remote sensing data. IEEE Transactions on Geoscience and Remote Sensing, 28(4): 550-552. Bischof H., Schneider W. and Pinz A.J., 1992. Multispectral classification of Landsat images using neural networks. IEEE Transactions on Geoscience and Remote Sensing, 30(3): 482- 490. Fausett L., 1994. Fundamentals of Neural Networks - Architectures, Algorithms, and Applications. Prentice Hall Intemational.Inc., London. Fung T. and Chan K., 1994. Spatial composition of spectral classes: A structural approach for images analysis of heterogeneous land-use and land-cover types. Photogrammetric Engineering & Remote and Sensing, 60(2): 173-180. Gong P. and Howarth P., 1992. Frequency-based contextual classification and grey-level vector reduction for landuse identification. Photogrammetric Engineering & Remote Sensing, 58(4): 423-437. Haralick R.M., Shanmugam K. and Dinstein I., 1973. Textural features for image classification. IEEE Transactions on System, Man, and Cybernetics, 3(6): 610-621. Jensen J.R., 1979. Spectral and textural features to classify elusive landcover at the urban fringe. Professional Geographer, 31(4): 400-409. Marceau D.J., Howarth P., Duboise J. and Gratton D., 1990. Evaluation of the grey-level co-occurrence matrix method for land-cover classification using SPOT imagery. IEEE Transactions on Geoscience and Remote Sensing, 28(4): 513- 519. Paola J.D. and Schowengerdt R.A., 1997. The effect of neural network structure on a multispectral land-use/land-cover classification. Photogrammetric Engineering & Remote Sensing, 63(5): 535-544. Sadler G.J., Barseley M.J. and Barr S.L, 1991. Information extraction from remotely sensed images for urban land analysis. Proceedings of European Conference on Geographical Information Systems, pp. 955-964. Wharton S.W., 1982. A context-based land-use classification algorithm for high resolution remotely sensed data. Journal of Applied Photographic Engineering, 8(1): 46-5.

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