XXII ISPRS Congress 2012: Technical Commission VIII

Shortis, M.; Shimoda, H.; Cho, K.

Bibliographic data

Multivolume work

Persistent identifier:: 1663813779

Title:: XXII ISPRS Congress 2012

Sub title:: Melbourne, Australia, 25 August-1 September 2012

Type of content:: Konferenzschrift

Year of publication:: 2013

Place of publication:: Red Hook, NY

Publisher of the original:: Curran Associates, Inc.

Identifier (digital):: 1663813779

Reihe:: ISPRS archives

Language:: English

Additional Notes:: Kongress-Thema: Imaging a sustainable future

Editor:: International Society for Photogrammetry and Remote Sensing

Author:: International Society for Photogrammetry and Remote Sensing, 22.; 2012; Melbourne

Document type:: Multivolume work

Volume

Persistent identifier:: 1663822514

Title:: Technical Commission VIII

Scope:: 590 Seiten

Type of content:: Konferenzschrift

DOI:: 10.14463/KXP:1663822514

Year of publication:: 2014

Place of publication:: Red Hook, NY

Publisher of the original:: Curran Associates, Inc.

Identifier (digital):: 1663822514

Illustration:: Illustrationen, Diagramme

Reihe:: ISPRS archives (volume 39, B8 (2012))

Signature of the source:: ZS 312(39,B8)

Language:: English

Additional Notes:: Erscheinungsdatum des Originals ist ermittelt.; Literaturangaben

Usage licence:: Attribution 4.0 International (CC BY 4.0)

Editor:: Shortis, M.; Shimoda, H.; Cho, K.

Editor:: International Society for Photogrammetry and Remote Sensing

Author:: International Society for Photogrammetry and Remote Sensing, 22.; 2012; Melbourne

Publisher of the digital copy:: Technische Informationsbibliothek Hannover

Place of publication of the digital copy:: Hannover

Year of publication of the original:: 2019

Document type:: Volume

Collection:: Earth sciences

Chapter

Title:: [VIII/8: Land]

Document type:: Multivolume work

Structure type:: Chapter

Chapter

Title:: GLOBAL LAND COVER CLASSIFICATION USING MODIS SURFACE REFLECTANCE PRODUCTS Haruhisa Shimoda, Kiyonari Fukue

Document type:: Multivolume work

Structure type:: Chapter

of land covers. Each elements (i, j) of the time-domain co- occurrence matrix is defined as probability that two pixels with a specified time-separation delta-t in the same spatial position have pixel value i and j. Conventional co-occurrence matrix(that is spatial domain co-occurrence matrix) represents spatial texture while the proposed time-domain co-occurrence matrix represents time-series signature. Figure 3a shows pixel values of annual time-series data conceptual. The time-domain co-occurrence matrices shown in Figure 3b are derived from this time-series data in the case of one month separation. That is, a time-series changing pattern of pixel values produces the corresponding probability distribution pattern in the matrix. Time-domain co-occurrence matrix takes advantage of robustness against data loss and noise derived from cloud and undesirable fluctuation of calculated reflectance values. * Deciduous Forest * Desert E a & 0 = # F (1 6,68 à 6 6 6,8 à 9 > 8 E © X i d a ge a JAN A FEB à MAR 4 APR A MAY A JUN A UE = AUG A SEP i SOU dj NOV DEC 4 (a) annual time-series (b) time-domain co-occurrence matrices Figure 3. Conceptual examples of time-domain co- occurrence matrix. In our experiments, two kinds of pixel value were examined. The first one is surface reflectance. The second one is spectral cluster that is extracted by clustering in seven spectral bands for 46 scenes data set. It is expected that spectral clusters absorb undesirable fluctuation of surface reflectance. And time separation delta-t from one to six months were examined in order to search proper delta-t. 3.2 Classifier The non-parametric minimum distance classifier was introduced for time-domain co-occurrence matrix. Euclidean distance dE(x,c) and cosine distance dn(x,c) between a pixel-x and a training class-c were examined in this experiments. The distance dE(x,c) and dn(x,c) are defined as Eq.(1) and Eq.(2), respectively. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B8, 2012 XXII ISPRS Congress, 25 August — 01 September 2012, Melbourne, Australia de(x,c)= } | 2) 2. (Maij) -MesCij))2 E à (D S^ 3 Y" Maij) Mes ij) dn(x,0)=— LS 2 12 we , 2 2 1 MG) bz1 Q) M,»(i.j) is a component (i, j) of the time-domain co-occurrence matrix measured from band-b in time-series data set for a pixel- X. M;p(i,j) is that measured from band-b time-series data set for the training area of a class-c. 4. CLASSIFICATION EXPERIMENTS 4.1 Land Cover Category Table 1 presents the land cover categories which are same with IGBP Land cover categories. These 17 categories were used in our classification experiments. Table 1. Land cover categories(IGBP legend). . Water EN . Evergreen Needleleaf Forest . Evergreen Broadleaf Forest . Deciduous Need leaf Forest . Deciduous Broadleaf Forest . Mixed Forests . Closed Shrublands . Open Shrublands 9. Woody Savannas © NN ON tA AW ON — 10. Savannas 11. Grasslands 12. Permanent Wetlands 13. Croplands 14. Urban and built-up 15. Cropland/Natural Vegetation Mosaic 16. Permanent snow and ice 17. Barren/Sparsely vegetated BE 4.2 Training and Accuracy Estimation 84 classification classes were prepared for IGBP 17 categories, because each category consists of several classification classes. About 9,000 pixels on the average for each class and about 400,000 pixels in total have been extracted as training data. Figure 4 and Figure 5 show examples of training data for "evergreen needleleaf forest" and " barren/sparsely vegetated”, respectively. Figure 6 shows examples of obtained time-domain co-occurrence matrix for "deciduous needleaf forest" and " savannas". (c)tim. one area Figure (c) tin one pi Figure (a)" Figure

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