Remote sensing for resources development and environmental management: Remote sensing for resources development and environmental management

Damen, M. C. J.

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Multivolume work

Persistent identifier:: 856342815

Title:: Remote sensing for resources development and environmental management

Sub title:: proceedings of the 7th international Symposium, Enschede, 25 - 29 August 1986

Year of publication:: 1986

Place of publication:: Rotterdam; Boston

Publisher of the original:: A. A. Balkema

Identifier (digital):: 856342815

Language:: English

Additional Notes:: Volume 1-3 erschienen von 1986-1988

Editor:: Damen, M. C. J.

Document type:: Multivolume work

Volume

Persistent identifier:: 856343064

Title:: Remote sensing for resources development and environmental management

Sub title:: proceedings of the 7th international Symposium, Enschede, 25 - 29 August 1986

Scope:: XV, 547 Seiten

Year of publication:: 1986

Place of publication:: Rotterdam; Boston

Publisher of the original:: A. A. Balkema

Identifier (digital):: 856343064

Illustration:: Illustrationen, Diagramme

Signature of the source:: ZS 312(26,7,1)

Language:: English

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

Editor:: Damen, M. C. J.

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:: 1 Visible and infrared data. Chairman: F. Quiel, Liaison: N J. Mulder

Document type:: Multivolume work

Structure type:: Chapter

Chapter

Title:: Image optimization versus classification - An application oriented comparison of different methods by use of Thematic Mapper data. Hermann Kaufmann & Berthold Pfeiffer

Document type:: Multivolume work

Structure type:: Chapter

33 Fig. 5 a Feature space original data Fig. 5 b Feature space optimized data Fig. 5 c Feature space H/S components spectral classes. An advantage of the unsupervised classification is that information about the number and the spatial distribution of the spectral classes can be quickly achieved. Based on this classification the selection of training fields for the different spectral classes will now be simplified. Furthermore the unsupervised classifi cation refers to such classes which are spectrally homogeneous. In the remaining inhomogeneous areas, textural features should be calculated and con sidered as additional information in the super vised classification. The supervised classification of the KARLSRUHE area will be given in Fig. 7, oased on 8 main landuse classes, classified with a combination of channels 1/3/4. The classification accuracy de creases with another channel combination and also by using more than 3 channels simultaneously. A much better differentiation in the agricultural and wooded areas could be achieved through a com bination of well suited spectral channels. Also in the settlements a better discrimination could be expected using textural features. First results with different textural parameters confirm this speculation. More intensive investi gations will be done in the near future. The intention of this paper is the discussion of the classification results using different prepro cessed data, and not the detailed discussion of the optimal classification strategy. Therefore the comparison of the classification with spectral and/ or textural features will not be discussed, because the textural analysis of Thematic Mapper data is a separate subject. 2.3 Comparative Discussion of the Classification Results Original data Tab. 2 shows that most of all 1972 pixels from 25 training fields have been classified with a high accuracy (version a). A visual inspection of the classification result (Fig. 6a) indicates that the substantial geological units, such as the Schistose complex (right part of the imagery), the Basalt com plex (left above and in the center) and the Granite complex between Schistose and Basalt have been classified correctly. Otherwise a more detailed analysis of the result also shows miscalculations mainly near the strong relief in the left part of the imagery. To improve the classification in such areas one should take into account the topographic effects. Optimized data The classification of the enhanced data (IHS-trans- formation with saturation enhancement) results in a separation identical to the classification of the original data (Tab. 2). A comparison between the two feature spaces in Fig. 5, demonstrates that these classification results could be expected. The ellipses indicate that the IHS-transformation did not change their general arrangement and orientation. This transformation results in an optimization of the saturation component without any modification of the color frequencies and the total albedo of the different surfaces. Image optimization using IHS-transformation through enhancement of the saturation, can be characterized as a method which emphasizes the original color différencies without falsifying the information contents of the original data. IHS-components In this section we describe the classification using the IHS-components directly. These components are an intermediate product of the whole IHS-transfor mation (chapter 2.1). The classification imagery in Fig. 6b (and Tab. 2, Version c) show nearly the classification of the original data in Fig. 6 a.

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