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:: Man-machine interactive classification technique for land cover mapping with TM imagery. Shunji Murai, Ryuji Matsuoka & Kazuyuli Motohashi

Document type:: Multivolume work

Structure type:: Chapter

58 3 REDUCTION OF DIMENSION FOR THEMATIC MAPPER DATA Six bands except a thermal band of TM 6 may be used for land cover classification. For visual inter pretation of image and histogram planes, the dimen sion should be reduced to three modes as follows. 1) Selected three bands A combination of band 1,4 and 5 was selected because it showed the maximum determinant of variance covariance matrix. 2) Principal components First, second and third components were utilized with the cumulative contribution of 95 %. 3) HSI elements Hue, saturation and intensity are obtained by a transform of six bands. Image plane is generated by assigning red, green and blue colors to the above three selected or transformed components. Two dimensional histogram plane can be generated to select two of three. From the result of experiment, HSI elements were the best for visual interpretation. 4 PROCEDURES OF MAN-MACHINE CLASSIFICATION FOR LAND COVER MAPPING Step 1: Step 2: Step 3: Step 4: Step 5: generate an image on a graphic display with use of HSI elements also generate the corresponding histogram on another graphic display locate a map on a tablet digitizer and establish geometric transform between image and map, classify the peaks portions in the histo gram by checking the corresponding image classify the rest with use of three planes of image, histogram and map planes Some areas in an image plane can be categorized manually by using free cursor on the image or on the digitizer. Some areas in an map can be also assigned and classified into a certain class. Figure 2 An example of Histogram plane (a) Assignment of polygon in histogram plane (b) The corresponding image file Sympc Space of gee B.A.Ncu Moscow S ABSTRAC1 used in fore in bination the tech into ort ctical a sis and Pamiro-A res of s correlat ture (ob rizontal cartomet delling ds of re Figure 3 FRO-IMG command Figure 2 shows a histogram plane and a map plane. Figure 3 shows an example of assignment of polygon in a histogram plane and the corresponding image in red color ( original is color ). 5 ADVANTAGES OF MAN-MACHINE CLASSIFICATION AS COMPARED WITH MAXIMUM LIKELIHOOD METHOD Both of man-machine classification technique and the conventional maximum likelihood method were applied to a test area in Tokyo Bay Area with use of TM data taken on January 23, 1985. As compared with the result from the maximum likeli hood method, the following advantages were pointed out. 1) To able to obtain ground truth data in a real time and to train them for classification in a real time, 2) to be able to learn or consider the spectral characteristics by intercorrelating the image and the histogram plane, 3) to be able to set up the priority of classes to be classified and 4) to be able to give the known geographic infor mation from the existing map. Of speci tation o presents solving problems eved to kes use account of the E interpre zaics ar fcy imerg; microsca! velopmen cartograj From tl maps it : an integ] tain fra^ its geogr complex i preted ar te integi It is thi graphs ai the hiers JIHHT , 1C some cornu as select ality, si to actual well as t nee. On t sential d ly manife their abs city (Neu Modern ly used i can be pr tometry, metric an tion of d should oe

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