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

Damen, M. C. J.

Bibliographic data

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

34 A detailed comparison yields small differencies between both images, e.g. at the shadowed parts of the central Basalt complex. The feature space in Fig. 5 c indicates an unfavourable arrangement of some classes in comparison to their arrangement in the original - or optimized data (Fig. 5a,b). This could lead to more confusion in the classifi cation. The application of the IHS-components for the classification did not result in an improvement of classification. 3 COMPARATIVE ESTIMATION In general, both methods presented, cannot be seen as different techniques which lead to comparable results. As shown above, there are several areas of overlap during processing. For instance, the choice of training areas needed for supervised classification is carried out visually by enhanced images. On the other hand, various statistical data are used to select processing parameters for image optimization. It can be stated that for applications where structural information is of high importance (geology), optimized image products are prefered For data acquisition of various surface signatures over wide areas (landuse), the use of classification methods seems to be more advantageous, particularly as relative proportions of single categories are determined simultaneously. The essential advantages of both methods can be summarised as follows: - The unsupervised classification is an objective method for separation of different signatures on a statistical basis. This is also true of su pervised classification, even though the choice of training areas is of subjective character. - A highly selective reduction of information can be made for any particular surface category, in con trast to image processing algorithms (PC's or ratios), where reduction depends on choice of bands and processing parameters. - Any color can be assigned to the resulting cate gories so that neighbouring classes are well differentiated. - Percentages of all classes are determined simul taneously. - The principal advantage of image optimization products lies in the fact that relief information is preserved. Such products are a portrayal of the earth's surface, which can at least be evalu ated and interpreted in photogeological terms. - Besides the possibilities for planning and execution of field work (classification results are not usable due to the absence of reference points), several strategies for structural ana lyses become feasible. - In contrast to classification results, enhanced products do not present a final result. Conse quently, the subjective character of data evalu ation can be left in the hands of the interpre ters. This can be seen as an significant advan tage. For drawing conclusions on, for instance, genetic events, a simultaneous evaluation of structural and spectral features is indispensable. - Furthermore, the recognition and localization of unknown spectraldiagnostic enoinalies is only obtainable by choice of suitable bands, and not by given training areas. - The comparative assessment has shown, that ad vantages of one method are not necessarily a dis advantage in using the other one. From a compu tational point of view there is little differen ce, and the choice of methods depends on the required objectives. The principal advantages of both methods can be summed up as: - An objective computer supported classification. - Preservation of relief information during the optimization process. Tab. 2 Statistic of classified training aeras Version a: original data 1/4/7 Version b: optimized data 1/4/7 Version c: IFIS-components No. name i ver- I i 2 I i 3 i 4 ! 5 6 7 8 9 1°i 11 12 13 14 sum sion ! i 3ga sba sbb sbc ;tix ea jra grb xya xyb j tbra il tbx gp 1 gga: I a 178 1 179 Granite b 178 1 179 Granodiorite C 178 1 179 2 sba: a 204 2 206 Schistose b 202 1 3 206 complex C 204 2 206 3 sbb: a 71 1 72 Schistose b 71 1 72 complex C 71 1 J- 72 4 sbc: a 1 307 308 Schistose b j 1 307 I 308 complex C i i i 307 I 308 5 tix: a 69 69 Laterite *> 69 69 (Kaolinite) c 69 69 6 vea: a 39 39 Vege- b 39 39 tation C 39 39 7 gra: a 160 160 b 160 160 Granite C 160 160 8 grb: a 68 68 b 68 68 Granite C 68 68 9 xya a 63 1 ; 1 65 b 63 1 1 1 65 ? C 63 2 65 10 xyb a 1 77 78 b 1 77 78 ? C 1 I 77 78 11 tbra: a 1 60 61 b 1 60 61 Ryolithe C 1 60 61 12 til: a 22 22 Laterite b 22 22 (Limonite] C 22 22 13 tbx: a 1 105 106 b 1 105 106 Basalt C 1 105 106 14 gp: a 322 329 Alkali - b 1 ¡323 329 granite C 1 '322 ¡329 Fig. 7 shows classification results in the Karlsruhe area. Fig. 8 shows an example of combining the advantages of image optimization and classification. This combination has been achieved using the IHS approach, were intensity (I) represents relief and Flue and Saturation (H,S) are derived from classi fication analysis. This can be seen as a suitable strategy for future developments.

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