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

32 Fig. 1 Location of test areas - further, the algorithms should transform data in dependently of variations in contrast. This faci litates the construction of mosaics and enables the interpretation of data across boundaries between different Landsat records. BASIC CONCEPT In a first step the correlation matrix was calcula ted to select three geologically significant bands, which are needed for additive color coding (Tab. 1). Two bands (5,6) are not useable fortnis concept. The relativ low correlation of SWIR-band 5 (1.6 pm) is caused by an overflow of the TM-detectors (on LANDSAT 4 and 5) and is typical for high sun ele vations (58° for this data of 04.17.85). Emission signals of band 6 are mostly depending on relief (sloping site) due to early morning overpass during heating up phase. Fig.2/3were calculated by use of TM band 1 (blue/ coded blue), TM band 4 (NIR/coded green) and TM band 7 (SWIR/ coded red). Although there was a good separability in brightness (albedo) and hues (domi nant color frequencies), the interpretability of the original composite was restricted by low satu ration values. This deficiency is caused by well balanced reflectance characteristics, related to the lack of strong absorption bands associated with most rock surfaces. The saturation cannot be optimized by means of additive (Red, Green, Blue) color processes e.g. in the photolaboratory and instead, the following pro cedure is used. Precondition is a digital transfor mation into three new mutually independent compo nents called Intensity (I), Hue (H) and Saturation (S) (Haydn et al., 1982) After a linear shift of saturation levels to the high saturation part within the given 8-bit range, the modified Saturation component and the original Intensities and Hues are retransformed into RGB- outputs. In order to enhance subtle variations in structu ral contrast, highpass-filtering was applied to the data using a 3 x 3 matrix and added back to the already I,H,S-calculated input components. The dis- Tab. 1 Correlation matrix for As-Sirat area ch 1 ch 2 ch 3 ch 4 ch 5 ch 6 ch 7 ch i 1.0000 0.9685 0.9265 0.8965 0.7384 -0.0998 0.7762 ch 2 1.0000 0.9804 0.9556 0.7772 -0.1349 0.8437 ch 3 1.0000 0.9849 0.8239 -0.0971 0.8970 ch 4 1.0000 0.8650 -0.0910 0.9094 ch 5 1.0000 0.0721 0.9042 ch 6 1.0000 0.0447 ch 7 1.0000 advantageous effect of reduced spectral differences can be compensated by the former Saturation increase explained above (Bodechtel & Kaufmann 1985). The last step is histogram computing and stretch for display on screen or transmission via photowrite system. Fig. 2 shows the result, which offers optimum conditions for lithological and structural mapping within one single image product. 2.2 Classification Whereas the results of the image optimization pro cess mostly serve as a well suited image product, for the following interpretation procedure the classification methods lead to evaluation results which are mainly produced by the computer. Most of the classification procedures are working pixel- oriented using only spectral information. The un supervised methods combine the pixels with similar features to spectral classes, for example with the euclidean distance in the feature space as a cri terion for the discrimination. The individual pixels are then assigned to the class whose center is nearest or whose distance is smaller than a maximum distance. For the supervised methods accu rate ground truth is needed to calculate the features of the individual classes before classi fication. A classification of all pixels to one of the given classes will then be carried out using a specific classification rule. The well known maximum likelihood method, for example, is based on the calculation of the likelihood with which the individual pixels are members of these classes. A classification is also assigned to the class with the maximum likelihood (SWAN, DAVIS, 1978). To get the likelihood, statistical descriptions of the sample classes, such as the mean values in each channel and the covariance matrix, are needed. For the estimation of these statistics training fields with known landuse are introduced. The main problem of the supervised procedures is the se lection of really representative sample areas for the presented classes. Both kinds of classification methods, the unsupervised and supervised pro- ceuures, can also oe applied for the classi fication with features, which aescribe the texture and the shape of objects. A multispectral classification with textural features for example, car be carried out based on textural features as additional channels, whereas the textural parame ters have been calculated for each pixel in a window fittet around each pixel. Well known textu ral features are based on a statistical evaluation of the grey dependencies between neighouring pixels (HARALICK, SHANMUGAM, DINSTEIN, 1973). The calcula tion of so called HARALICK-parameters for each of the 7 Thematic Mapper bands leads to 7 additional textural channels. For a subsequent supervised maximum likelihood classification, with both the spectral and the textural features, a highly so phisticated channel selection or channel combina tion procedure must be carried out. Furthermore, a hierarchical classification procedure (WU, 1975) should be applied to use the full information con tents of all spectral and textural channels in a cost effective way. EXAMPLES Fig. 4 shows an unsupervised classification of the test area KARLSRUHE, with the euclidean distance as discrimination criterion using channels 3/4. Using a large euclidean distance (d=20 grey values) only a few spectral classes will be discriminated, whereas with smaller distances more spectral classes occur. For the presented Thematic Mapper data (color composite in fig. 3, date 7.7.1984) a small euclidean distance (d = 7) seems to be a favourable measure for the classification. Fig. 4 demonstrates that this euclidean distance leads to more than 10

Access restriction

Copyright

fullscreen: Remote sensing for resources development and environmental management (Volume 1)

Multivolume work

Volume

Chapter

Chapter

Table of contents

Cite and reuse

Volume

Chapter

Image

Image fragment

Citation links

Citation recommendation