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

44 By examining Table 3a it is apparent, that only a little change in the distribution of each class in respect to the classification result of the original data, has taken place. This is especially evident for data, which is resampled with the nearest neighbour algorithm. Moreover the classifica tion results of the bilinear interpolated data seems to have only small deviations relative to the original data, but visual interpretation shows significant differences. Original Class Data 1 Water 1.13 2 Coniferous F. 20.40 3 Decidious F. 12.99 4 Urban Areas 8.44 5 Agriculture 37.45 6 Grassland 8.77 7 Vine 10.82 Contrast Enhanced Data Nearest Bilinear Neighbour Interpolation 1.15 1.04 20.43 20.78 13.06 12.30 8.61 9.05 37.36 37.61 8.99 8.45 10.40 10.76 Table 3a. Classified pixels in each class in (%) In order to investigate the exchange of pixels assignment to classes of the differentially resampled data, Table 3b, using data resampled with the nearest neighbour method as reference image, is referred. Generally this Table shows the same tendencies as Table 2b, but with 89% pixels classified the same, the difference in classification results between the resampling algorithms is smaller than in Table 2b. Mainly the agreement of water pixels increases remarkably. Data Contrast Enhanced, Bilinear Interpolated Type „ Class 12 3 4 5 6 7 Figure 4. Comparison of classification results of doubled and not doubled data, radiometrically and geometrically preprocessed u c o jQ 1 83.34 6.74 - 9.88 - rtf jC XL 60 2 0.12 94.69 3.34 0.53 - C LLÎ ’5 3 - 8.62 84.06 0.02 0.05 •M z 4 0.63 0.94 0.01 88.88 5.26 rtf 00 d) 5 - - - 1.95 93.56 +-* C L. 6 - 0.02 4.58 0.01 13.51 o O <D z 7 - 1.48 2.19 4.29 9.45 Sum of pixels : Percentage of pixels classified the same : _ 0.03 Contrast Rectified Data _ 1.31 Enhanced None- Doubled 3.37 3.87 Class Data Doubled 0.03 4.26 1.81 2.69 1 Water 4.22 4.59 4.03 77.81 4.07 2 Coniferous F. 9.12 9.06 9.02 3.22 79.39 3 Deciduous F. 8.89 7.90 8.25 4 Urban areas 28.66 28.05 28.54 1915289 5 Agriculture 34.82 34.16 34.90 89.13 % 6 Grassland 14.29 16.01 15.04 Table 3b. Pixel by pixel comparison of classification results of resampled contrast enhanced data with nearest neigh bour and bilinear interpolation. 4.4 Classification Results of Contrast Enhanced Data, Doubled and None-Doubled before Rectification Classification of rectified data with resampling of nearest neighbour delivers results with only less accuracy loss. But if classification of resampled data with bilinear inter polation is carried out the results will be of unsatisfying accuracy. In order to obtain better classification accuracy, the experiment of doubling the input data before rectifica tion is carried out (Figure 4). Considering the available disk memory only a little part of the so far investigated region is choosen. Consequently new training areas for now six classes are defined. Subse quent classification of contrast enhanced original data and rectified doubled and none-doubled data is executed. Table 4 shows the percentual distribution of classes in the results. It can be pointed out that the classification outcome of rectified data with doubled input is much more similiar to the outcome of geometrically none-processed data. In studying the results visually, better agreement of these data with the original data can be recognized, especially in lines and edges. An additional evaluated difference image shows mainly distinctions in riverbanks, lakesides and edges Table 4. Classified pixels in each class in (%) of greater areas. Also isolated pixels, which disappear in normally rectified data, occur in the result of doubled data. Consequently this shows that doubling of data before rectifying produces an obvious improvement of classifica tion accuracy. 5 CONCLUSIONS It can be concluded that the various kinds of data prepro cessing before classification influence the results. The main results of the above investigations are briefly dis cussed. They underline the different influences of the various preprocessing methods. Classification of original and contrast enhanced data shows only small differences, if linear histogram stretching to each channel is carried out separately. Classification results of nearest neighbourhood re sampled data show only little change in respect to the results of original data. This is valid for re sampling of original and contrast enhanced data. Classification results of bilinear interpolated data show great differences in respect to the result of original data. Most of the changing occurs in isolated pixels, lines and edges. Therefore classes with a lot of I ing. tior sho' - Cla: lati' doul The above influence: geometric of neare; which is * great gee map grid) If bilim fication r those of t Bilineai if a doub improverr ing of cla gâtions. In the \ the influe the purpo of image REFEREi' Forster, E Landsa Photo. Kahler, M ly Pro Mappin 1986 (ir Realnutzi 1:5000C Neckar Frankfi

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