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:: Comparison of classification results of original and preprocessed satellite data. Barbara Kugler & Rüdiger Tauch

Document type:: Multivolume work

Structure type:: Chapter

4 INVESTIGATIONS The investigations are carried out on the image processing system of the Department of Photogrammetry and Carto graphy of the Technical University of Berlin, where soft- ware-packages for radiometric and geometric image pro cessing as well as for multispectral classification are implemented. For the investigations a part of the Landsat-scene 210/26 from 9. August 1975 is used. This part covers a region of Baden-Württemberg, with the Oberrheingraben and the cities Mannheim and Karlsruhe. The seven most frequented classes in the concerned area should be dis tinguished with the conventional maximum-likelihood classifier. The following case-studies, which are also presented in Figures 1-4, are carried out: Comparison of classification results of original and contrast enhanced data - Comparison of classification results of original data, rectified and resampled with the methods of nearest neighbour and bilinear interpolation Comparison of classification results of contrast enhanced data, rectified and resampled with the methods of nearest neighbour and bilinear inter polation Comparison of classification results of contrast enhanced data; on one hand, row- and column-doubled before rectification and resampling (bilinear inter polation), and on the other hand without doubling before geometric processing ence will be expected between the classification results, because the histogram stretching affects only a linear extended distribution of the grey values, without any loss of information. Original Contrast En- Class Data hanced 1 Water 1.13 1.13 2 Coniferous F. 20.40 20.38 3 Deciduous F. 12.99 12.84 4 Urban areas 8.44 8.69 5 Agriculture 37.45 36.35 6 Grassland 8.77 8.73 7 Vine 10.82 11.88 Table la. Classified pixels in each class in (%) In Table la it can be seen, that there are only few differences in the distribution of classes in both images. Table lb is much more distinct for a comparison of classification results than Table la, because the exchange of pixels from one class to another is pointed out. In this Table the classification result of original data was used as reference image. The percentages in the lines show the class changing of pixels in the classification result of the contrast enhanced data. Data Contrast Enhanced Type For all rectifications the same transformation polynomials were used. The coefficients were evaluated considering the coordinates of nine control points. In all cases the training areas for the classes were defined in a colour composite of the contrast enhanced data. Subsequently the edge coordinates of the training areas, which have been obtained during definition, were calculated for the respec tive preprocessed data. These new coordinates could be used to obtain the features (grey-values) of the rectified and resampled data. This method guarantees comparison of the results, because all classifications use statistics de rived from the same training areas. However the sites differ through their features and/or the number of features when contrast enhancement, rectification and resampling is carried out. Class 1 2 3 4 5 6 7 1 99.49 0.13 _ 0.35 _ 0.02 0.01 2 0.01 99.70 0.12 0.06 - - 0.11 3 - 0.29 98.44 - - 0.09 1.17 4 0.01 - - 99.39 0.09 - 0.51 5 - - - 0.72 96.95 0.50 1.83 6 - - 0.32 - 0.43 97.25 2.01 7 - - - 0.12 - - 99.88 Sum of pixels : 1048567 Percentage of pixels classified the same : 98.28 % Table lb. Pixel by pixel comparison of classification results of original and contrast enhanced data 4.1 Classification Results of Original and Contrast Enhanced Data This case-study compares the classification results of orig inal and contrast enhanced data (Figure 1). For contrast enhancement of the used part of the scene a linear histogram stretching was carried out for each channel separately. The result is more appropriate for visual inter pretation particulary for defining training areas. No differ- Figure 1. Comparison of classification results of radiome- trically preprocessed data In Table lb the greatest differences occur in the classes deciduous forest, agriculture and grassland. The mis- classified pixels are assigned mainly to vine. Visual inter pretation of the investigated part of the scene shows, that misclassifications appear in areas, which are highly struc tured. The creation of a difference image confirms the visual comparison of both classification results. In homoge- nious regions like those covered with coniferous forest and in greater parts of decidous forest and agriculture no differences can be detected. The reasons for misclassification can be explained by the spectral reflectance of the special classes and the contrast enhancement. It is very difficult to define training areas in highly structured regions. Such training sites may also contain grey-values, which are not characteristic to the considered classes. So the variance of such classes is relatively high. The contrast enhancement will affect more in increasing of the variance of highly structured classes, than that of homogenous classes. Another reason can be explained by the histogram stretching for every channel separately. This influences each channel differently, which results in different covariances for original and contrast enhanced data. By using a conventional maximum-likeli hood classifier these facts effect a change of assigning pixels to such classes. By means of these facts we can conclude, that differ ences between classification of original and contrast enhanced data exist, but they are usually small. The percentage of pixels, which are assigned to the same classes in both images, amount to about 98 %.

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