5&&3;$*755&>@@(%80*5+?(3#<$7-5&->+?.F57 &3K58 2553 15-17 4*5$ 2553 1 ! Color-orthophoto classification by object-based analysis #$% ( Patcharavadee Thamarux)1 & (Vichai Yiengveerachon)2 1 '' *# +,*#* % !.+%#/ [email protected] 2 * % /03 4#* % *# +, *#* % !.+%#/ [email protected] 3: 1:4000 !"#$" (resolution) 5 % #&"'*!"+ ,-/2"3"/6&"# ,3'-6/'63"/" 6%7"&'*3"/#89-" RGB 33"/'*3"/#;&! '!&"<" #,-/%"!!"!<7'!&3"/#7= ' (Interpretation) <#,&$ (Classification) ; 6%7"#,#!'!& # 3,3"/ -6/!%!#,6%&$ (Object Based Image Analysis, OBIA) "?&&% &;<= (Texture Algorithms) '*!$;&%7&$'D; 6-#, "/ &$"/, segmentation ,#,"/ GLCM homogeneity -%?E ,&;-?= # #,&$-"&;!-F 5 ! !< ! =, ' $/3/ #H? ; %?7<=H#,!"-/!$/& ?=#,3'+ #,6%"7&$-"&;"3' ': , ', #,&$6%&$, <=, GLCM, homogeneity Abstract: Color-orthophoto of Ministry of Agriculture and Cooperatives (MOAC) -- scale 1:4000 with the resolution 5 cm-- deemed as a high-resolution image, which can see more details and can be used in various advantages. However, this digital color-orthophoto is got from scanning in RGB mode, not as the satellite image: the images will always have an error from scanning. Therefore, the interpretation and object-based classification processes on the digital color-orthophoto by classifying the spectrum 07$?! 0>*4^ >G>0&J >$:0058 5&&3;$*755&>@@(%80*5+?(3#<$7-5&->+?.F57 &3K58 2553 15-17 4*5$ 2553 2 cannot be done easily. By using ‘Object Based Image Analysis’ (OBIA) technique with texture algorithms to assist the identification. Through the creation of process known as segmentation and classification by GLCM Homogeneity in directions to classify into 5 classes what were buildings, roads, rivers, field and a group of trees. In this study found that direction of GLCM Homogeneity is a matter in classification. This study is leading to specific object-type classification for later on. Key words: GLCM, homogeneity, classification, Object Based Image Analysis (OBIA), texture 1. 3 'N##$;&H6%7'*7/+"/;; ?+%?,!&F!+/ ,6"&;H 3#3"/# ?< " -'?3 1:4000, 1:25000 "+ "&Q"% , ;& ' 'S 2544-2545 '* 7/+!;!$&='? ,-6/ -'7/+ -6/ "% ,-/3 "/H -6/ "%!+/,+ 7 -6/ <# "&'*3"/#"/!< 89-" RGB 33"/'*6%73"/# ;&!'!&" '"/ "/ % "% &= #-6/ -7 % ' "/!%#!'!&76$"= 3,3"/" -6/!%!#, 6%&$ (Object Based Image Analysis, OBIA) " ?&&% <= (Texture Algorithms) 6 -#, % #/ & $ , segmentation &,#,&% <= GLCM 6%" Homogeneity -%? #-6/ !%!"& -/H#,- "&;!+/" 2. # 2.1. ' (field) '* <= ' + '* 7 2.2. $7/3/ (tree) !<;%/3/7= 6 ;%7; ;%=, 2.3. ! (building) !<! /"/$ 2.4. (road) -6/-&F# '* 2.5. =, (water) ;%=,7& 23"/6&"## ? 3. 89&*;$ -%#&= -6/ # (Ministry of Agriculture and Cooperatives-MOAC) 1:4000 ! "#$" (resolution) 5 % 3"/ 543847858, 543847860, 553833608 <= !;!$ ;% , < #& & " !6 7&=?%!""&+' 1 07$?! 0>*4^ >G>0&J >$:0058 5&&3;$*755&>@@(%80*5+?(3#<$7-5&->+?.F57 &3K58 2553 15-17 4*5$ 2553 3 +';; !< [2] N #1 º 1! i , j 30 Pi , j i # j 2 (1) • i : the row number • j : the column number • Pi , j : the normalized value in the cell i, j • N : the number of rows or columns ! GLCM homogeneity !,-%? E 3"/ !< 0°, 45°, 90°, 135° (+' 2) $ %? (all direction)[3] 4& 1 H"7&=? #, & $ -? = - 6/ ' Definiens Professional 5.0 4. ! (Segmentation) Segmentation '*;,&;/&$ 'N##&-/!<Scale factor, Color, Shape, Compactness, Smoothness "! scale parameter '* ! , " heterogeneity (! <!'*<="&) [1] 5. GLCM Homogeneity % GLCM Homogeneity [2] !< “If the image is locally homogeneous, the value is high if GLCM concentrates along the diagonal. Homogeneity weights the values by the inverse of the Contrast weight with weights, decreasing exponentially according to their distance to the diagonal.”[2] 4& 2 "%?E7 GLCM 6. *;$ 6.1. ? , segmentation H #,&$<3 6.1.1. ? , "! scale factor parameter -, segmentation ""&= ! &= 50, 60, 70,…, 140 6.1.2. ?,"! color-shape compactness-smoothness parameter -, segmentation " <! 0.9, 0.7, 0.5,0.3, 0.1 07$?! 0>*4^ >G>0&J >$:0058 5&&3;$*755&>@@(%80*5+?(3#<$7-5&->+?.F57 &3K58 2553 15-17 4*5$ 2553 4 6.1.3. #, , segmentation / "/ GLCM homogeneity 6.2. ?%? GLCM Homogeneity H #,&$<3 6.2.1. , segmentation "-6/ ! #?-7/ 6.1 6.2.2. #,"/ GLCM homogeneity "; ? '* 5 %?E !< 0°, 45°, 90°, 135°, $%? (all direction) 7. @*;$ 4& 3 , segmentation; scale factor = 50, color-shape = 0.5, compactness-smoothness = 0.5 7.1. , Segmentation #? ; 'N ##& -, segmentation !< ! scale factor ; !&; 100, 110 120 -/H/&$-/!& !< 3"/ +' !;!$ & $ !/ "&&= -?#-6/! scale factor&; 100 "& +' 5 ";"-6/ ! scale factor + 100 #!"3 "&+' 6 ! scale factor , 100 #!""& +' 3 4 <,#,! ,"3/ "-6/% GLCM Homogeneity 6%"$ %? ; #,&$<! scale factor / #,&$/<! scale factor 3 "!/&;+'7&$ #H! overall accuracy "#2 scale factor&; 100 # -/ overall accuracy " "& 1 4& 4 , segmentation; scale factor = 70, color-shape = 0.5, compactness-smoothness = 0.5 07$?! 0>*4^ >G>0&J >$:0058 5&&3;$*755&>@@(%80*5+?(3#<$7-5&->+?.F57 &3K58 2553 15-17 4*5$ 2553 5 1 scale factor segmentation overall accuracy Scale factor 140 100 60 50 4& 5 , segmentation; scale factor = 100, color-shape = 0.5, compactness-smoothness = 0.5 Overall accuracy 10.84 73.56 63.38 54.23 KIA *100 0 68.17 57.16 48.24 #= # ? ; , "& ' color-shape -, segmentation &' color 0.5 ;&$!6&"#3"/ ","!+ <!, -7 &'compactness-smoothness 3H, segmentation 7.2. #,"/% GLCM #,-?=3"/#,&$ 5 6%" !< ! =, ' $/3/ " ##,!& 2 ! !< plant non-plant (&+' 7) /##,&$ 5 6%" 6&= &==3"/?H#,"/% GLCM Homogeneity -%?E !< 0°, 45°, 90°, 135°, $%? (all direction) 4& 6 , segmentation; scale factor = 140, color-shape = 0.5, compactness-smoothness = 0.5 07$?! 0>*4^ >G>0&J >$:0058 5&&3;$*755&>@@(%80*5+?(3#<$7-5&->+?.F57 &3K58 2553 15-17 4*5$ 2553 6 ! =, $7/3/ ' 4& 7 &"#, plant/ Non- plant ;; GLCM Homogeneity $%? # 2 ; H#,&= 5 !- (overall accuracy) KIA -/H-/! & "%? 45° -/!,$" ##%#3"/ %?7 GLCM Homogeneity #3H #,&$&= 5 6%" 4& 8 "#, Rode/ House/ River/ Field/ Tree ;; GLCM Homogeneity $%? 2 !"#$# %&'$# GLCM Homogeneity Accuracy Overall KIA (*100) All 0° 45° 90° 135° direction 73.56 73.56 71.26 72.41 73.56 68.71 68.71 65.38 66.46 68.06 < % # - " "/ '% "/ #; %?7 GLCM Homogeneity H #, & $ 6% " "; #,!"!<"&"+' 8-12 " &F& "&= 4& 9 "#, Rode/ House/ River/ Field/ Tree ;; GLCM Homogeneity %? 0° 07$?! 0>*4^ >G>0&J >$:0058 5&&3;$*755&>@@(%80*5+?(3#<$7-5&->+?.F57 &3K58 2553 15-17 4*5$ 2553 7 4& 10 "#, Rode/ House/ River/ Field/ Tree ;; GLCM Homogeneity %? 45° 4& 12 "#, Rode/ House/ River/ Field/ Tree ;; GLCM Homogeneity %? 135° H#,&$!"!<-6%" 7 #,"/% GLCM Homogeneity -%?E "&"- 3 3 $#()& All direction Building 15 Road 6 Water 12 Tree 5 Field 3 4& 11 "#, Rode/ House/ River/ Field/ Tree ;; GLCM Homogeneity %? 90° 0° 17 15 9 5 4 45° 19 12 2 5 4 90° 135° 3 15 5 12 11 8 4 0 5 0 # 3 #2 #,"/% GLCM Homogeneity , & ; !-% ? 90° , & ; -%? 90° $%? ,&;=,- %? 45° ,&;$/3/'- %? 135° !!"!</$" 07$?! 0>*4^ >G>0&J >$:0058 5&&3;$*755&>@@(%80*5+?(3#<$7-5&->+?.F57 &3K58 2553 15-17 4*5$ 2553 8 "& &= #, 6% & $ 7 1:4,000 "/ % GLCM Homogeneity &= %?7 GLCM H #,&$&= 5 ! "&$'- 4 &7-2; ! GLCM Homogeneity - %?&=7&$6%" All dir X (0.08-0.16) 0° 45° Field Tree Water Building Road 4 * $#&'$# GLCM Homogeneity !(%+* 90° X (0.09-0.17) X 135° 9. [1] Definiens professional 5.0 User Guide. [2] Rafael C. Gonzales and Richard E. Woods, Digital Image Processing. 3rd edition, Pearson Education, 2008 [3] Pornphan Dulyakarn and Yuttapong Rangsanseri, Textural classification of urban environment using Gray-level Cooccurrence Matrix. (0.03-0.08) X (0.01-0.10) X (0.04-0.07 X (0.08-0.11) 8. ! ' 6%73"/#- " RGB "/'!%&=, 3"/"-6/<= (texture) "/% #,6%&$ "-6/ GLCM Homogeneity Algorithm -%? & #,&$&= 5 ! !< ! =, ' $/3/ 3"/ ,-/6 "-'"/ #-6/ !7/ "H3"/='*-? #, 6% & $ , &; !- " &$3' 07$?! 0>*4^ >G>0&J >$:0058
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