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by: Sallie Lind PhD
Sallie Lind PhD
GPA 3.84


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Class Notes
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This 69 page Class Notes was uploaded by Sallie Lind PhD on Wednesday September 9, 2015. The Class Notes belongs to ESRM 430 at University of Washington taught by Staff in Fall. Since its upload, it has received 15 views. For similar materials see /class/192031/esrm-430-university-of-washington in Environmental Science and Resource Management at University of Washington.

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Date Created: 09/09/15
u Display enhancements amp spectral feature extraction u Radiometric amp atmospheric corrections 4 Geometric amp topographic corrections orthrectification Requirements Digital imager Interior orientation Exterior orientation Ground control and tie points Terrain model Good example Puget Sound River History Project httpriverhistoryesswashingtoneduorthophp I Examples Google Earth Google Maps Microsoft Live NASA World Wind EOS data gateway USGS National Map Seemless data portal Global visualization viewer WAGDA Washington State Geospatial Data Archive GeoMapNW Practice the depth perception exercise p53 ESRM430508 May 22 2008 5222008 Dr L M Moskal 1 Dr L M Moskal ESRM430SO8 May 22 2008 Precision de me an e mg gemeu quotme Why Accuracyen ens meek PSpmdu 7 won 5 not Comp efe mm m have L 2f quahty or our 2 need m dermf we 39 39 nd t u e a uatmn muqbe 3 f km N F ed e mm H ed m thtda 39 m m wmm n depe de tpue 3Hbeazqwrednumuzatmgne d 1p arr amnm utherdata u a award Mzatmmdumeb 5222008 soozzzs 9gt50W 39W quotI 3390 JELLI a e um 7 UEIE 75 r Damn mm Warsaw am umzx Muaun pau aun au am pm man amen H mm LLle sLULm n mpma Hm m Wham mmm 5Han F r gtq11mumua men men 3W5 p Aux 8002 22 New sos39osvwasa ESRM430SO8 May 22 2008 5222008 narrrrrrg Set rdra Krruwrr I 1Yr m r M rr rrr Li 1 is 01 u 38 urban 79 tom pixels were improperly intluded in me orn category u 40 urban Dixels were rrristlassed as forest Dr L M Moskal 4 ESRM430SO8 May 22 2008 5222008 Producer39s accuracy Obtamed b dmdmg mm a had p m each ategm b the L umn mm tuta p e Lmenm h w Coin 2 a Hay nll a 1 Use r s accuracy mum b dmdmg mm mu ed pmekm Egan b the raw mm tuta pm mm m w ea 1 at umrmtt Me a m mmrm um enm ur m y r a me pomtiu epsndenHe Dr L M Moskal 5 9 9gt50W 39W quotI 3390 p u 1 1m u 43 mo p am SOOZZZS 8002 22 New sos39osvwasa ESRM430508 May 22 2008 5222008 J Hardscopy map product Twordlmenslonal thematK map with pseudo eolourtable proleetlon and a the elements ofa map u Summary statlstlcs a Tabular data presenting summary statlstles areal extent for Vertype example oreaduo n Dlgltalflles Dlgltal data les forGlS Integration Metadata 1 Pre classlflcatlon data enhancements A Post classlflcatlon data cleanup and manual enhancements Dr L M Moskal 7 ESRM430SO8 May 8 amp 12 2008 582008 Dr L M Moskal 1 ESRM430SO8 May 8 amp 12 2008 582008 L An increasing amount of very high resolution imagery VHR of astonishing quality provided by new digital airborne and space orne sources is entering the remote sensing market sub meter pixel resolution quot It is characterized by high user interpretability rich information content sharpness accuracy high image clarity and integrity u But the rich information content dramatically complicates the process of pixel labeling Dr L M Moskal 2 ESRM430SOS May 8 amp 12 2008 582008 0 u mmhm n Dr L M Moskal 3 ESRM430SO8 May 8 amp 12 2008 582008 Dr L M Moskal 4 ESRM430SO8 May 8 amp 12 2008 582008 Dr L M Moskal 5 ESRM430SO8 May 8 amp 12 2008 582008 Dr L M Moskal 6 ESRM430SO8 May 8 amp 12 2008 memos amp Nan Vea aisd H Incorporation ofimage spatial features ie texture or autocorrelation statistics into the perpixel classi cations schemes 2 Spatial ltering i Image segmentation and objectbased feature extraction u Methods 1 torder image texture 2nd order image texture based on the co occurrence matrix a Image semivariancespatia autocorrelation Dr L M Moskal 582008 ESRM430SO8 May 8 amp 12 2008 582008 Maka 2 Frant llnzun 1 Apply a 3 pier by 3 pixel average lter over the NIR annel 2 Run a 3 pixel by 3 pixel Rule ased Maxima lter over the average channel 3 Perform a logical AND operation with stems and crown image Dr L M Moskal ESRM430SOS May 8 amp 12 2008 1 What are features and oh eus39 err it differencem pixelrb at r r shape ax rrarrq sed procedures is b Feature momma azysr sdaesnme t single pixels m h e ex t e r 6I p The segment ion llowthelm em bebroken up39 ne er q39 me etworkofhomoqe d nychosen resolutionb se lch e 39 ct trcsoftheim e ed for Dr L M Moskal JECt pllmltwes V he1 ion step a 39 e Ions anthem al I represem m geinforn ion m 582008 ESRM430SO8 May 8 amp 12 2008 582008 Spatial Extent Grain size r Beyond purely spectral information image contain a lot of additional attributes which can be used for classification An astonishing characteristic ofobject oriented image analysis is the multitude ofadditional information which can be derived based on image objects In a conceptual perspective the available can be distinguished as intiinieteatiii tiieabieet pietniediea mld39r pii I iiieii aie deteiniined bi tiie 1 id tiie i a r en and iiinniinatian e abie ee an be be calm t ieai l3 ii it 1 iniieiited liiDUgli iiieiaiei n t Tupulu izal Ea L betiiegeanietiieieiatian between ing iett iigiit m in aeeitain dilancetu tiiintiiein e t Eriimtmrelatiu fliip eg Dr L M Moskal 10 ESRM430SO8 May 8 amp 12 2008 582008 Dr L M Moskal 11 ESRM430SOS May 8 amp 12 2008 582008 Dr L M Moskal 12 ESRM430SOS May 8 amp 12 2008 582008 May 15 we wwH have 3 guest ecturers presen m u Focus on research and proposa s thws shou d be Very he pfu forthe fma exammatwon Dr L M Moskal 13 ESRM430SO8 May 8 amp 12 2008 582008 Dr L M Moskal 14 I httpuwnewsorgpublicprintuweeltasp We will meet in Anderson 302 as three separate groupsYbUNHbeas gnedtoyourgroupbased on the first letters of your last name Each group will have a specific 30 minute time slot allocated inthelab 9301000 Group 1 Last name starts AJam 10001030 Group 3 Last name starts JanRaym 10301100 Group 3 Last name starts RaynZ Be on time Q Ma m3 raphy m we I There is a difference between Photogrammetry amp Photo Interpretation Photo Interpretation The act of examining aerial photographsimages for the purpose of identifying objects and judging their significance IDENTIFICATION Photogrammetry The science or art ofobtaining reliable measurements by means of hoto39ra h MEASURMENT 2 2 23222 22 22 22 22 22 22 22 22 22 22 22 2222222222 E 22222 22 22 22 222222222222 22 222222222 2 2 2 22 2 52222 2 2 E 2 2222222222222 22 22 22222 2 2 2 2 22222222222222 2 222a 222222 2222222 2 22M 5 22222222 222222 2 2 22 w 2 2 22 22 E2 2 E 2 t 2222622222 2W 222222 2 2 2 22 22 quot 2 2 22 22 a a 2 2 2 2222 a k 2 i is 2 is 22 2 22 2 2 2 2 2 2 2 2 2 2 22 2222222222222me 2 22 2 2 a 22 22 22 m 2 2 2 22 222 222 i 22 2 222222222 2 21 222 a 2 2222222 222 2 2 2 22 2 2 2222 2m 2 2 22 a 353 2 2 22222 2 22 2222 2f is 222 ii is a is is 2 2 a 22 2222222 quot n n I 2 2 2 2 22 lt 22 quot222 222 a quot 2222322222222 22 22 22222 2 2 2 2 2 2222222 2 2 WW 2222 2 22 2222 2 2 22 22 2 2222222 22 222 2222 222 E 2 iii 22 2222 222222222222 22 3 22 22 2 22222222222222 2222 222 2222222 2 2 222222 a f w 2 2 b 2 2222 2 I 2 2 22 m 222222 22222222 2 2 222 2 22 22 2222 22222 2 2 2 2 2 22 2222 2 2 222222222222222222 22 2 i2 2 2 2 2 2222 22 Easi rd est t Take more ti Provide definite identification 0 recognize me and effort HM 391 Second Order Geometric Arrangements ofObjects Size isimportantin discrimination of objects and features Size of stadium can estimate capacity 399 Second Order Geometric Arrangements ofObjects Shape can provide diagnostic clues that aid identification 2 Second Order Geometric Arrangements of Objects Spatial arrangement of colorpattern is scale dependent l Paint swatches 2 Second Order Geometric Arrangements of Objects Pattern is the spatial arrangement of objects it can be location specific Third Order Location or Positional Elements Site is how the objects are arranged with respect to one another Third Order Location or Positional Elements Association is the identification ofan object based on the confirmation of another 2 Third Order Location or Positional Elements n Height is Intormatlon that can be determined through interpretation Shadow is the angle of the sun in respect to an object allowing it s identification 1 1 tab y grkzg geaagzyu f w l9 c xyl ap 397 3 i V 3quot V 1 27 i 3 quot ji vi 4 NE W k fz mti rgjx gl F4 53311quot 3 39 I i quotHagar Wi39rk r439i 1i p I s A Haw fkwwvg quotng 2 quotquot39 A 39 39 1 531 y h J I Arquot nt39 TR Xo S F iwd z i I H 3 quot quot e v V i 3 Field Observations are required when the image and its relationship are not understood and require further identification 3 Direct recognition is the application ofthe interpreter s ex erience skill andjudgment to associate the image patterns u Probabilistic interpretation are efforts to narrow the range of possible interpretations A photoimage interpretation key is a set of guidelines used to assist interpreters in rapidly identifying photoimage features Mapping projects of different types in different areas will require different keys There are two basic types Selective keys select closest class Elimination keys stepwise elimination of all but proper class Level 1 Urban or Builtup La 2 Agricultural Land 3 Rangeland 4 Forest Land 5 Water 6 Wetland 7 Barren Land 8 Tundra 9 Perennial Snow or Ice Level II 11 Residential 12 Commercial and Services 13 Industrial 14 Transportation Communications and Utilities 15 Industrial and Commercial Complexes 16 Mixed Urban or Builtup Land hgjrne Pafkg 17 Other Urban or Builtup Land V T Lodging 21 Cropland and Pasture 22 Orchards Groves Vineyards Nurseries and Ornamental Horticultural 23 Confined Feeding Operations 24 Other Agricultural Land 31 Herbaceous Rangeland 32 Shrub and Brush Rangeland 33 Mixed Rangeland 41 Deciduous Forest Land 42 Evergreen Forest Land 43 Mixed Forest Land 51 Streams and Canals 52 Lakes 53 Reservoirs 54 Bays and Estuaries 61 Forested Wetland 62 Nonforested Wetland 71 Dry Salt Flats 72 Beaches 73 Sandy Areas other than Beaches 74 Bare Exposed Rock 75 Strip Mines Ouarries and Gravel Pits 76 Transitional Areas 77 Mixed Barren Land 81 Shrub and Brush Tundra esmnw 82 Herbaceous Tundra 83 Bare Ground Tundra 84 Wet Tundra 85 Mixed Tundra 91 PerennialAreas im mr l quots Harman5 I Commercial 39 Institutmnal l Urban Rnuuiimal ll Quarrins Dumps l If l l wn ic Levellmapusingme Anderson LULC iassimaaon 197s rpdfon lass website Iyplcal data characteristics LAN DSAT formerly ERTS type of data Highaltitude data at 0 000 ft 12 00 m or above less than l80000 scale Mediumaltitude data taken between 10000 and 40000 ft 3100 and 12400 m 120000 t0 180000 scale Lowaltitude data taken below 10000 ft 3100 m more than 120000 scale C1 GU e S a G e 4 Cricket Iootball ESRM 430 Lecture Notes Sem1vanance statistic for measunng spatial autocon39elanon 1 11 2 znxsrzthIN e1 Wham yh 151112 sem1m1ancefmthzlag1nurnalh z11smeasuessamplevalue ezepmm z1h1smusxed sample value 11pm 11 1 15 me um number uf sample pm 11 me lag mtexval 1 umesme number afpmxs anhe lag dlstance Stepsfm calculaungthz semmnmee lag calculate me difference and men square me afferent e 111 1 pm Fax eachpmx separatedbyl sum 1 sum arrexenees and mag bylwm e number a Thesamusdmefmathzrlag stmces ms gves yau me semwanance yh 11m can be med m draw a magm Ex mp1e u 1 n n 1 z n 1 1 1


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