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

43 .ssification results, ;cts only a linear s, without any loss Contrast En hanced Data 1.13 20.38 12.84 8.69 36.35 8.73 11.88 n (%) lere are only few es in both images. a comparison of ause the exchange pointed out. In this .1 data was used as :he lines show the ation result of the need 5 6 7 - 0.02 0.01 - - 0.11 - 0.09 1.17 0.09 - 0.51 >6.95 0.50 1.83 0.43 97.25 2.01 - - 99.88 : 1048567 le : 98.28 % assification results :cur in the classes issland. The mis- vine. Visual inter- : scene shows, that h are highly struc- nage confirms the 'esults. In homoge- niferous forest and nd agriculture no De explained by the ;s and the contrast ne training areas in lg sites may also iracteristic to the >f such classes is nt will affect more structured classes, her reason can be for every channel differently, which ginal and contrast il maximum-likeli- lange of assigning elude, that differ- inal and contrast isually small. The ;ned to the same ?8 96. 4.2 Classification Results of Rectified and Resampled Original Data The proceeding of data processing for a comparison of original and rectified and resampled data is shown in Figure 2. It is very difficult to compare classification results of original and geometrically preprocessed data, because rectifying the images causes another scale and another form. Therefore comparison is only possible by studying the classified images and the percentage distribu tion of classes in the whole image. Figure 2. Comparison of classification results of geo metrically preprocessed data In Table 2a it can be seen, that classification results of the original and nearest neighbourhood resampled data differ only slightly. A visual examination confirms this result. The classification result of resampled data with bilinear interpolation shows greater differences compared to that of the original data. The apparent differences are caused by geometric transformations, which create more or less new pixels between the rectified pixels. Original Original Data Data Nearest Bilinear Class Neighbour Interpolation 1 Water 1.13 1.15 0.87 2 Coniferous F. 20.40 20.46 20.47 3 Decidous F. 12.99 12.99 12.27 4 Urban Areas 8.44 8.51 8.66 5 Agriculture 37.45 37.53 38.94 6 Grassland 8.77 9.01 8.21 7 Vine 10.82 10.35 10.58 Table 2a. Classified pixels in each class in (%) Because the classification of original and nearest neighbour resampled data are very similar, the following investi gations are carried out only with the rectified data. For this purpose we can refer to Table 2b which shows pixel to pixel comparison, using nearest neighbourhood resampled data as reference image. The Table shows great differ ences between the classification results. Only about 86% of the whole image part are classified the same. The altering can be explained by the method of bilinear interpolation, which uses for the generation of the new pixels the surrounding pixels, while the nearest neighbour algorithm only takes already existing grey-values. Especially a class changing of isolated pixels and other small areas, e. g. Data Original, Bilinear Interpolated Type Class 1 2 3 4 5 6 7 D O _Q 1 68.72 5.84 _ 22.47 0.27 2.65 0.04 SZ —T DO 2 0.11 92.72 3.86 1.12 0.09 0.04 2.07 S3 3 - 8.41 81.63 0.03 0.40 4.35 5.19 4 0.67 1.46 0.01 83.15 10.61 0.01 4.08 ô jö 5 - 0.01 0.02 1.74 92.90 2.28 3.05 6 - 0.02 5.59 0.02 18.22 71.54 4.61 <u z 7 - 2.01 3.55 4.17 14.14 2.99 73.15 Sum of pixels : 1915289 Percentage of pixels classified the same : 86.32 % Table 2b. Pixel by pixel comparison of classification results of resampled data with nearest neighbour and bilinear interpolation. lines, borders, etc. can be recognized. Mainly the changing from riverbanks and lakesides to urban areas is evident. Moreover small areas situated in a larger region have a loss of space, while the greater surfaces increase. In the visual comparison of both classification results it can be seen that the result of nearest neighbour resampled data is more differentiated, but classification of bilinear interpolated data looks more homogeneous. The subtraction of both images from each other shows very distinctly the effects of bilinear interpolation to edges and lines. However only large areas with homogeneous spectral reflectances can be classified correctly (coniferious forest, some areas of agriculture), while highly structured regions will tend to more misclassification. An additional inspec tion of the feature space shows that changing of pixels tends mostly to adjoining classes. 4.3 Classification Results of Rectified and Resampled Contrast Enhanced Data In this case-study for rectification and resampling the contrast enhanced data are used (Figure 3). It is expected, that classification results of this data show only small differences in comparison to the results of chapter 4.2, because the classification of the original and the contrast enhanced data also differ slightly. Figure 3. Comparison of classification results of radiome- trically and geometrically preprocessed data

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