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All Journal International Journal of Remote Sensing and Earth Sciences (IJReSES)
Danang Surya Candra
Indonesian National Institute of Aeronautics and Space (LAPAN), Jakarta
Earth & Planetary Sciences

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ANALYSIS OF SPOT-6 DATA FUSION USING GRAM-SCHMIDT SPECTRAL SHARPENING ON RURAL AREAS Danang Surya Candra
International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 10, No 2 (2013)
Publisher : National Institute of Aeronautics and Space of Indonesia (LAPAN)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (921.345 KB) | DOI: 10.30536/j.ijreses.2013.v10.a1846

Abstract

Image fusion is a process to generate higher spatial resolution multispectral images by fusion of lower resolution multispectral images and higher resolution panchromatic images. It is used to generate not only visually appealing images but also provide detailed images to support applications in remote sensing field, including rural area. The aim of this study was to evaluate the performance of SPOT-6 data fusion using Gram-Schmidt Spectral Sharpening (GS) method on rural areas. GS method was compared with Principle Component Spectral Sharpening (PC) method to evaluate the reliability of GS method. In this study, the performance of GS was presented based on multispectral and panchromatic of SPOT-6 images. The spatial resolution of the multispectral (MS) image was enhanced by merging the high resolution Panchromatic (Pan) image in GS method. The fused image of GS and PC were assessed visually and statistically. Relative Mean Difference (RMD), Relative Variation Difference (RVD), and Peak Signal to Noise Ratio (PSNR) Index were used to assess the fused image statistically. The test sites of rural areas were devided into four main areas i.e., whole area, rice field area, forest area, and settlement. Based on the results, the visual quality of the fused image using GS method was better than using PC method. The color of the fused image using GS was better and more natural than using PC. In the statistical assessment, the RMD results of both methods were similar. In the RVD results, GS method was better then PC method especially in band 1 and band 3. GS method was better than PC method in PSNR result for each test site. It was observed that the Gram-Schmidt method provides the best performance for each band and test site. Thus, GS was a robust method for SPOT-6 data fusion especially on rural areas.
ORTORECTIFICATION OF SPOT-4 DATA USING RATIONAL POLYNOMIAL COEFFICIENTS Danang Surya Candra
International Journal of Remote Sensing and Earth Sciences (IJReSES) Vol 9, No 1 (2012)
Publisher : National Institute of Aeronautics and Space of Indonesia (LAPAN)

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (794.009 KB) | DOI: 10.30536/j.ijreses.2012.v9.a1827

Abstract

Orthorectification  of  satellite  imagery  can  be  done  in  two  ways  i.e.,  rigorous sensor  model  and  the  approximation  model  of  the  satellite’s  orbit.  Dependence  on  physicalparameters,  to  make  rigorous  sensor  model  is  more  complicated  and  difficult  to  apply.  The approximation  model  can be either  Rational Polynomial Coefficients (RPC)  model  or  parallel projection  system.  RPC  is  a  mathematical  model  which  is  not  depends  on  the  sensor.  It  is used to improve the positioning accuracy when the parameter of the physical sensor model is  unknown.  This  study  assessed  orthorectification  of  SPOT-4  using  the  RPC  model  with  7 coefficients. Root Mean Square Error (RMSE) of GCPs obtained from the study  was less than 1  pixel.  RPC  did  not  depend  on  physical  and  satellite  orbit  parameters.  Thus  the  RPC  was simpler and easier to apply.
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