INVESTIGATION OF RESAMPLING EFFECTS ON IRS-1D PAN DATA

Transcription

1 INVESTIGATION OF RESAMPLING EFFECTS ON IRS-D PAN DATA Smpth Kumr P.,*, Onkr Dikshit nd YVS Murthy Geo-Informtics Division, Ntionl Remote Sensing Agency, Hyderd, Indi.-(smpth_k, Deprtment of Civil Engineering, Indin Institute of Technology Knpur, Knpur, Keywords: Resmpling, Geometric rectifiction, Edge detection ATRACT: Imge resmpling is frequently employed in geometric correction s prt of preprocessing of remotely sensed imge. The resmpled imges will hve noticele chnge in imge qulity, which my hve n dverse impct on the ccurcy of susequent imge nlysis for informtion etrction. Informtion of edges is very importnt, especilly in high sptil resolution imgery. The present study hs investigted the effects of resmpling on edge informtion for Indin remote sensing (IRS)-D pnchromtic (PAN) imgery with 5.8 m sptil resolution. PAN imges hve een resmpled during shift resmpling nd geometric rectifiction processes y using iliner, cuic B-Spline nd cuic convolution resmpling methods. The cuic convolution ws implemented with three different prmeter vlues of s -.0, nd The resmpled imges were then used to identify edges. It hs een oserved from the sttisticl properties nd y using edge detected imges tht the cuic convolution method of resmpling with s vlue is the most suitle resmpling pproch for IRS-D PAN imgery, especilly for detecting edges.. INTRODUCTION Resmpling lso known s intensity interpoltion pplied for digitl imge in vrious geometric trnsformtion processes such s scling, rottion, shering, wrping etc. Conceptully, imge resmpling comprises of two stges: imge reconstruction followed y smpling. Although resmpling tkes its nme from the smpling stge, imge reconstruction is the implicit component in this procedure (Wolerg, 990). For remotely sensed (RS) stellite dt, resmpling is necessry, when imges re to e geometriclly corrected or registered into different coordinte system defined y mp, nother imge or sptilly referenced dt set (Roy nd Dikshit, 994). When n imge is sujected to resmpling, there is often noticele chnge in imge qulity. This in turn my hve n dverse impct on the ccurcy of susequent humn nd mchine ssisted imge nlysis for informtion etrction such s clssifiction (Devereu et l., 989), teture (Roy nd Dikshit, 994) nd shrp imge detils or edges. In reltion to resmpling studies, considerle work hs een done y the erlier reserchers. But, limited numer of studies ws concentrted out the effects on RS imgery. The effects of resmpling hve to e studied during vrious imge-processing opertions such s clssifiction, principl component nlysis, edge detection etc. Sme resmpling method my give dissimilr results for seprte sensor dt. This is due to the fct tht performnce of resmpling function depends on the sptil structure of piels nd the viewing geometry. But, nothing hs een reported on the effects of resmpling for Indin remote sensing (IRS) stellite dt. One of the est sptil dt ville to user community from IRS stellites is PAN dt with 5.8 m sptil resolution nd 6-it quntiztion (NRSA, 997). From PAN dt, we cn etrct mjority of edge fetures such s rod network, fly-overs etc, which mke up to 70% fetures tht re ville in :5,000 mps (Ktiyr S. K., 00). Hence, this study presents the investigted effects of resmpling for IRS sensors, especilly the high sptil resolution PAN sensor.. DATA AND TOOLS USED The study hs een conducted for three study sites consisting of rpidly chnging Indin urn environments, which were locted in Knpur, nd Bhopl. The Level dt ws cquired from Ntionl remote sensing gency (NRSA), Hyderd, which is corrected only for rdiometric errors. It is not corrected geometriclly, which mens it hs not undergone resmpling. For this reson, Level dt of IRS-D, PAN sensor eing used to chieve the ojectives of present study. From every imge test site of size 55 piels hve een etrcted. These test sites hve een selected so tht they contin mjor lnd cover fetures such s wter odies, urn, rurl regions, vegettion, frm lnds etc., nd prominent edge fetures like rods, cnls, strems etc. These test sites re shown in Figure nd detils re given in tle. c d Figure : Test imges showing () Knpur, (), (c) Bhopl, nd (d) their loction in Indin mp.

2 City Dte Row Pth Su scene Bhopl D8 Knpur B A3 Tle : Detils of IRS-D, PAN dt Resmpling for two study cses were implemented in Mtl 6.5 commercil softwre. The process of Geometric correction ws vlidted with the corresponding module present in ILWIS 3.0 softwre. GPS survey ws crried out using hnd held GPS (GS5) nd differentil GPS (SR530) from Leic Geosystems. mtri,,, specify liner trnsformtion for scling, shering, nd rottion, while 3, 3 produces trnsltion. Interpoltion or resmpling is common process of filling in unknown vlues (output) in sequence y emining the known vlues (input). One-dimensionl interpoltion is illustrted in Figure 3(Wolerg, 990), where the interpolting function h() is centered t, which is the loction of point to e interpolted. The vlue t tht point is equl to the sum of the vlues of the discrete input scled y the corresponding vlues of the interpolting function. 3. METHODOLOGY Resmpling process dels with imge piel vlues only. Tht mens only rdiometric modifictions re epected due to resmpling. Geometric rectifiction process modifies n imge oth geometriclly nd rdiometriclly. Therefore, investigtion for resmpling effects cnnot e justified y only nlyzing the geometriclly rectified imges. For this reson, resmpling of imges is lso performed y shifting n imge in oth directionl es. The sme hs een clled s the Shift resmpling nd used in present study. Hence, two eperiment cses re shift-resmpling (SR) nd geometric rectifiction (GR). The methodology implemented is represented in the following flowchrt. imge Figure 3. Interpoltion of single point In oth the cses, three resmpling methods were used viz, Biliner (), B-Spline () nd Cuic Convolution (CC). Implementtion of CC ws done long with three different vlues of prmeter s -.0, nd The lgorithms given y Wolerg (Wolerg, 990) re eing dopted in the study. They re: Shift-resmpled/ Rectified imge 0 < h ( ) = 0 () Sttistics Figure : Methodology flowchrt In shift-resmpling cse, shifting vlue is referred in terms of piel units. A shift of 0.5 piel vlues on oth the directions ( nd y) ws used, since this vlue gives the mimum reconstruction error (Shlien, 979). This shift hs een dpted for nlyzing the chnge of piel vlues due to resmpling lone. While geometric rectifiction ws done using precise Ground Control Point (GCP) coordintes. These GCPs were collected during n etensive Glol Positioning System (GPS) survey work conducted in ll the three cities nd were used for geometric rectifiction of imges using ffine trnsformtion (Wolerg, 990). u = + v = + y + y + Anlysis 3 3 Edge detection Percentge edge piels Where, u, v refers to output coordintes corresponding to input piel coordintes, y. The coefficients of trnsformtion () < ( ) ( 3) 0 h ( ) = < 0 < h ( ) = < 0 The shift-resmpled or geometriclly rectified imges re used etrction of edges using Cnny edge lgorithm (Cnny, 986). Detection of edges ws done for ech rw nd resmpled imge using cnny edge detector. Cnny ssumed tht step edge suject to Gussin noise nd lso ssumed tht, the edge detector s convolution filter tht smoothens the noise nd loctes the edge using the Gussin function. The form of Gussin function is given elow G ( ) σ ( ) e (3) (4) = (5) σ π Where, s is the (one dimensionl) spce constnt of the Gussin function, nd is the distnce etween the point of interest to the middle of the Gussin function. The Cnny edge detector works in multi-stge process s given elow:

3 i) Imge is smoothed y Gussin convolution filter. ii) Directionl grdients re computed y numericlly differentiting the smoothened imge to compute the nd y grdients, nd compute the resultnt grdient or mgnitude. iii) Non-mimum suppression finds peks in mgnitude-imge. iv) Thresholding loctes prominent edges. The size of Gussin smoothing filter is tken s 3w, where w = σ is the width of the centrl ecittory region of the filter (Huerts, 986), hence the size of filter will e proportionl to the s vlue. Gussin filter ws used for smoothening imges with prmeter (s) s.0. After mny trils, it ws found tht this vlue ws suited est for finding the prominent edges. The sme vlue ws used for ll eperiments of study. Threshold vlues were otined y computing uto thersholding vlue using Trussel method (Trussel, 979). In Trussel method of thresholing, optimum threshold vlue is otined y itertion process. The eqution used for itertion process is given in (Eq - 6). 4. Shift resmpling (SR) 4. RESULTS AND DISCUSSION Imges sujected to shift resmpling re visully interpreted nd few sttisticl prmeters were computed. The visul interprettion ws difficult find ny nomlies s the chnges were very minute, i.e out 0. piel vrition in men (Figure 4.) only. The sttisticl prmeters include men, stndrd devition for individul imges nd coefficient of correltion of resmpled imges with respect to corresponding rw imge. These vlues depicted in Figure 4. In these plots few terms,,,, nd re used, which represents rw imge nd SR imges otined using Biliner, Cuic Convolution with prmeter vlues -.0, -0,75, -0.5 nd cuic B-spline respectively. Then there re three es in ech plot, ech of them represent one city dt, colours re mentioned s legend. All the es represent sme i.e. either men, stndrd devition etc Tk ini () i= 0 i= Tk + k+ = + Tk N T ni () i= 0 N i= Tk + ini () ni () (6) Men Where T k Threshold t k th itertion i Piel vlue in mgnitude imge n(i) Frequency of grey level i, 0 i N N Numer of piel vlues Further, sttisticl prmeters for ll the rw nd resmpled imges were computed for further nlysis. While, edge detection etrcted the oundries of prominent fetures such s rods, wter odies, stdiums, cnls etc, were identified s edges in rw imges. Then eperiment results were nlyzed with respect to the rw imges. The sme results nd conclusions re presented in the following sections. In present study. The edge piel identified y the Cnny edge detector ws ssumed s true edge piel. Bsed on this ssumption, the percentge of edge piels corresponding to rw nd resmpled imges wre clculted. This percentge is the rtio of numer of edge piels to numer of dt piels in corresponding input imge. Chnge in edge informtion ws lso otined y prepring clssified edge imges from oth rw nd processed (shiftresmpled). These clssified imges contin 4 clsses of piels, whose description is given elow. Coefficient of correltion Stndrd devition Bhopl Knpur Clss Unchnged edge piels Clss Chnged from edge to non-edge Clss 3 Chnged from non-edge to edge Clss 4 Unchnged non-edge piels Resmpled imges in oth cses were nlyzed sed on visul nd sttisticl prmeters. The corresponding results re presented with oservtions nd interprettions in the following section. c Resmpling methods Figure 4: Sttisticl prmeters () Men, () Stndrd devition nd (c) Coefficient of correltion for rw nd SR imges. From the sttisticl properties it ws oserved tht, the stndrd devition of imge piel vlues ws reduced due for nd resmplers. For resmpled imges with CC, the chnge in stndrd devition is reltively less. For s -0.75, the stndrd devition ws nerest to tht of the rw imge. While, the coefficient of correltion of rw imge with other resmpled

4 imges ws highest in cse of resmpled imges nd the lest for CC resmpled imge with s -.0.The mimum difference etween rw nd resmpled imges for the minimum piel vlue is. The minimum piel vlue did not chnge for Bhopl nd for CC resmple with s nd For Knpur, however, it remined unchnged with s -.0. The used high resolution dt, gve very lrge numer of edges when Cnny method ws pplied fter Gussin smoothing. The percentge of edge piels in the edge imges listed in Tle 4. nd corresponding grph is provided in Figure 4.. Bhopl Knpur Tle. Percentge edge piels for SR imges 7 in cse of CC with = for Bhopl nd Knpur study sites nd with -.0 vlue for re. Most of the new edge piels (Clss3) were visully seen to e locted cross the direction of shift i.e. top-left nd ottom-right digonl direction. It ws lso oserved tht there ws minimum loss in continuity of edge fetures, where s for some fetures continuity ws estlished fter SR. 4. Geometric Rectifiction (GR) The co-ordintes of ground control points (GCPs) re very importnt in terms of sptil interpoltion, which governs the position of new piel where the resmpled vlues will e plced. The imges in present study were geo-referenced with GPS survey crried in three cities, out fifty well distriuted control points were collected. These were identified from the imges nd survey of Indi topo-sheets s well. Due to the good ccurcy of collected GCPs, geometric rectifiction ws le to chieve less thn 0.5 piel error. In this cse sttisticl prmeters men nd stndrd devitions were computed. The coefficient of correltion ws not computed due to difference in size of rw nd resmpled imges. The plots of these vlues re given in Figure 7. Percentge Edge Piels Resmpling methods Bhopl Knpur Figure 5. Percentge edge piels (Tle ) From these vlues, it cn e oserved tht percentge edge piels incresed due to CC resmpler with s -.0 nd The numer of piels did not chnge much in SR imge otined y s However, the percentge of edge piels decresed in cse of nd methods. Further the retention rtio of sme piels, i.e. numer of clss- piels were computed from the clssifiction imges nd re present in the Tle. An emple of resmpled imge nd its corresponding edge clssified imge re presented in the figure. Bhopl Knpur Tle 3. Percentge of unchnged edge piels (clss) Men Stndrd Devition Resmpling methods Bhopl Knpur Figure 7. () Men nd () Stndrd devition plots for rw, GR imges The oservtions mde from the vlues nd grphs include tht, the Men vlues for ll GR imges re similr to rw imge vlue. For ll study sites, the difference in stndrd devition is minimum in cse of CC with s nd the mimum in cse of method. The chnge of minimum piel vlues ws lest in CC resmpling method. Clss Clss Clss3 Clss4 Figure 6. Prt of Resmpled nd its edge clssfied imges Form these clssified imges it ws oserved tht More thn 54 percent of edge piels re common in oth rw nd SR edge imges. Mimum percentge of Clss piels were oserved Bhopl Knpur Tle 4. Percentge edge piels for rw nd GR imges

5 In GR imges, there were mny lnk piels due to rottion of imge. So, numer of dt piels ws only clculted nd used for otining the percentge of edge piels, ut clssified imges were not computed due to size mismtch. The percentge vlues re tulted in Tle 4 nd the corresponding grphs re shown in Figure 8. recommended for performing the resmpling process on IRS- D, PAN imgery, especilly if the im is to detect edges. Becuse, it gives good numer of edge piels with minimum correltion devition from rw dt. References from Journls: Percentge of Edge Piels Resmpling methods Bhopl Knpur Figure 8. Percentge edge piels (Tle 4) From the tle nd grph, it ws oserved tht in ll GR imges the numer of edge piels ws more thn tht ws identified in rw imge. The mimum percentge ws oserved using CC with = -.0. Bsed on the results otined in geometric rectifiction, following interprettions cn e mde: Cnny, J., 986, A computtionl pproch to edge detection, IEEE Trnsctions on Pttern Anlysis nd Mchine Intelligence, 8, Huerts, A., 986, Detection of Intensity chnges with supiel ccurcy using Lplcin-Gussin msks, IEEE Trnsctions on Pttern Anlysis nd Mchine Intelligence, 8, Roy, D. P., nd Dikshit, O., 994, Investigtion of imge resmpling effects upon the teturl informtion content of high sptil resolution remotely sensed imge, Interntionl journl of remote sensing, 5, Shlien, S., 979, Geometric correction, registrtion nd resmpling of Lndst imgery, Cndin journl of remote sensing, 5, Trussel, H. J., 979, Comments on picture thresholding using n itertive selection method, IEEE trnsctions on Systems, Mn, nd Cyernetics, 9, 3. References from Books: (i) (ii) (iii) Chnge in men vlue is significntly less. This could e due to good ccurcy of geometric rectifiction s indicted y smller vlues of sigm. The chnge in stndrd devitions hs shown the sme trend s SR. This mens tht the informtion content ws unffected due to the trnsformtion processes. Percentge of edge piels ws significntly incresed in ll GR imges. This could e due to wrping of imge involved in ffine trnsformtion. However, the trend in vrition of percentges ws similr to edge imges otined y SR (Figure 5). 5. CONCLUSIONS Wolerg, G., 990, Digitl imge wrping, IEEE computer society press, Los Almitos, Cliforni. References from Other literture: Devereu, B.J., Fuller, R. M., nd Roy, D.P., 989, The Geometric Correction of Airorne Themtic Mpper Imgery, Proceedings of the NERC workshop on Airorne Remote Sensing. Ktiyr, S. K., 000, Report of stte-of-the-rt seminr on Geometric correction of IRS-C/D dt, Deprtment of Civil Engineering, IITKnpur. The present study hs investigted the performnce of resmpling methods y nlyzing the sttisticl prmeters of imges otined y vrious resmpling lgorithms. The investigtions hve demonstrted tht resmpling techniques hve profound effect on detection of edge fetures. Although Biliner () nd cuic B-spline () methods mintin high correltion etween rw nd resmpled imges due to their positive interpoltion vlues, these lgorithms smoothen the imge cusing disppernce of edge fetures. Cuic convolution (CC) method with three different prmeter vlues hs shown similr performnce. However, resultnt imges re very different from the rw imges (indicted y low correltion vlues). The CC method with = vlue hs shown similr vlues of sttisticl properties nd numer of edges in most of resmpled imges to tht of rw imge vlues. Although the trends of stndrd devition nd percentge of edges were similr for SR nd GR imges, the numer of edge piels significntly incresed in GR imges, which could e due to wrping of imges in trnsformtion process. Hence, sed on vrious results, CC method with s is NRSA, 997, IRS-D Dt users hndook. Ntionl Remote Sensing Agency, Hyderd.