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:: Digital processing of airborne MSS data for forest cover types classification. Kuo-mu Chiao, Yeong-kuan Chen & Hann-chin Shieh

Document type:: Multivolume work

Structure type:: Chapter

18 Table 7. Ranks of 47 best band combinations Percent Mapping KHAT CPU Code Band combination accuracy accuracy value time Rank (%) (%) (min. ) AV8B-R 4,5,6,7,8,9,10,11 98.64 97.45 .982865 160 2 C8B-R 4,5,6,7,8,9,10,11 98.46 97.09 .980647 155 1 C7B-R 5,6,7,8,9,10,11 97.13 94.92 .963907 129 3 C6B-R 5,7,8,9,10,11 97.00 94.87 .962316 109 4 AVÖB 4,5,6,7,8,9,10,11 96.92 94.52 .961431 155 6 AV6B 5,7,8,9,10,11 96.22 93.58 .952531 109 4 AV7B 5,6,7,8,9,10,11 95.54 92.69 .943983 129 7 R8B-R 10/8,10/6,7/9, 9-6/946,10/5, 9/11,4/7,5/11 95.24 92.24 .940128 158 8 A8B-R PC,,PC 2 ,PC 3 , 94.10 91.42 .925931 186 11 10/8,7/9,5/11, 5,9 AV5B 5,7,8,9,11 94.05 90.78 .925385 90 9 C5B-R 5,7,8,9,11 93.92 90.44 .923883 90 9 C4B-R 5,8,9,11 92.89 89.03 .911200 70 12 AV4B 5,8,9,11 91.53 87.35 .894271 70 13 C8B 4,5,6,7,8,9,10,11 89.08 82.87 .865814 150 16 C7B 5,6,7,8,9,10,11 88.35 81.99 .857194 125 15 A3B PC!,PC,,PC,,10/8, 7/9,5/11,5,9 88.10 82.70 .852243 181 17 C6B 5,7,8,9,10,11 86.99 80.35 .840499 105 14 C5B 5,7,8,9,11 85.56 78.72 .822961 85 18 R3B 10/8,10/6,7/9, 9-6/946,10/5, 9/11,4/7,5/11 84.82 77.50 .812077 153 19 A7B PC,,PC,,10/8,7/9 5/11,5,9 84.22 78.26 .804332 157 20 P3B-R PC,,PC,,PC, 5,8,9,il 83.87 81.76 .798504 62 21 C4B 83.21 76.57 .795936 65 22 A6B PC 2 ,10/8,7/9, 5/11,5,9 82.28 77.46 .780216 136 23 A5B PC,,PC,,10/8,• 7/3,9 81.27 75.36 .766500 116 24 C3B-R 6,8,9 80.39 74.55 .758890 47 25 P3B.A3B PC,,PC,,PC 3 10/8,10/6, 79.76 74.69 .752586 60 26 R7B 79.25 74.09 .743437 128 28 9-6/946,10/5, 9/11,4/7,5/11 R6B 10/8,7/9,10/5, 9/11,4/7,5/11 78.22 72.04 .731961 107 27 A4B PC,,PC,,7/9,9 76.58 74.63 .711602 96 31 AV3B 6,8,9 75.70 69.41 .703845 47 29 R5B 10/8,7/9,9/11, 4/7,5/11 75.45 70.61 .697990 88 30 R4B 10/8,7/9,9/11, 4/7 74.31 69.96 .684153 67 34 P2B PCj.PC, 10/8,7/9,5/11 73.66 66.56 .678652 60 33 R3B 73.61 70.53 .675170 46 32 AV2B 5,9 68.94 63.93 .624756 31 35 A2B PC,,7/9 67.61 67.95 .607137 60 37 C3B 6,8,9 65.82 57.01 .599942 45 36 C2B-R 5,9 64.18 63.06 .572122 31 38 C2B 5,9 54.05 48.29 .473751 30 39 R2B 10/8,7/9 50.16 43.28 .434277 31 40 AV1B 6 48.42 51.82 .373982 15 41 C1B-R 6 46.38 54.41 .353796 16 42 PIBjAlB PC! 44.57 41.42 .357885 30 43 C1B 6 41.59 42.82 .321694 15 44 RIB 7/9 29.22 29.98 .228814 15 45 Their ranks were determined by average divergence first and then minimum divergence. For example, among the C2B's 28 possible band combinations, the combina tion of band 5 and band 9 is the best because it possesses the highest average divergence, 1790 (table &). The C5B represents a band combination with 5 bands. Among their 56 possible band combinations, the combination of band 5, 7, 8, 9, 11 and combination of band of 5, 7, 8, 10, 11 possess the same average divergence. According to their minimum divergences, we ranked the former first, the latter second. As previously mentioned, on land cover types classi fication accuracy assessment, error matrix was used. By the error matrix of each band combination, the percent accuracy, mapping accuracy and KHAT value were computed (table 7). The KHAT value is a measure of agreement which represents how well the classifi cation data agree with the reference data (Congalton et al., 1983). Table 8 shows the CPU time of various operations. Generally speaking, the computer time required for the classification increases approximately as double of the number of band used. On the best band combina- Table 8. CPU time of various operations Operation CPU time (min.) Spatial filtering 5 Resampling 5 Ratio images 3 Principal component transformation 15 Classification with 8 bands 150 Classification with 7 bands 125 Classification with 6 bands 105 Classification with 5 bands 85 Classification with 4 bands 65 Classification with 3 bands 45 Classification with 2 bands 30 Classification with 1 bands 15 tion selection, computer time needed should be the top consideration. For instance, according to the testing of significance, there is no siginficant difference between AV8B-R and C8B-R. But AV8B-R spent more CPU time on data processing, therefore C8B*R ranked first, and AV8B-R second. 4 CONCLUSIONS Bearing in mind the objective to find the optimal band combinations of airborne MSS data for forest cover types classification, the following conclusions are drawn. (1) The result has shown that the past experience, maximum accuracy in cover type classifi cation can be obtained by using all wavelength bands available, is true. From table 7, one can find that the best band combination contains more bands. (2) Resampling and spatial filtering techniques increase 7-10% classification accuracy because both of them have the ability in sharpening the images to approach a better resolution. (3) Principal component transformations seem no effect on improving the accuracy of forest cover types classification because they are less effective on contrast enhancement for vegetations. (4) Ratio images are also not good enough for forest cover types classification because the ratio images tend to suppress the difference in albedo. Thus dissimilar materials with different albedos may be inseparable on ratio images. (5) Six out of eight components of mixed bands are unable to improve classification accuracy. The mixed bands perform naturally insufficiently. ACKNOWLEDGMENTS The authors would like to express their appreciation to the Remote Sensing Planning Committee, Council of Agriculture, Republic of China, for providing the financial support. Thanks are also due to the Center for Space and Remote Sensing Research, National Central University, for data processing services. REFERENCES Congalton, R.G.; R.G. Oderwald; R.A. Mead 1983. Assessing Landsat classification accuracy using discrete multivariate analysis statistical tech niques. Photogrammetric Engineering and Remote Sensing, Vol. 49, No.12, Dec. 1983, pp.1671-1678. Fleming, M.D.; R.M. Hoffer 1977. Computer-aided analysis techniques for an operational system to map forest lands utilizing Landsat MSS data, LARS technical report 112277, Purdue University, West Lafayette, Indiana, U.S.A. pp.1-231. Kalensky, Z.; L.R. Scherk 1975. Accuracy of forest mapping from Landsat Computer Compatible tape. Proceedings of the 10th international symposium on Remote Sensing of environment. PP.1155-1163. Walsh, S.J. 1980. Coniferous tree species mapping using Landsat data. Remote sensing of Environment 9. PP.11.26. Symposiur Method for auto R.I.Elman All-Union ’Les 1 ABSTRACT: / sidered. Th matlon degt fects in cc ur networks into the ve code thread lay process 1 CONTOUR L In the fore use of info space photo on contour 1 contour bas operations i characterisi complicacy < of contour : In the wo: input of th< help of digJ put is effe< not too comj segments» T1 in case of i ous contour tion of whi< thod is slo* a lot of mar A higher t tion of the can be achic cal-mechanic case means c urs on photc units to be having a cl« al and space ated automat threshold pr detail in li Sobel, Kirsc cultural tas burns, cutov part of the unextracted. rarely, in p A more com thod of the parating dif other is bas with a furth segments. In Potapov, Svi Pamorozski 1' re features i on. For exam] tapov, Sviri<

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