BibTex RIS Cite

ASSESSMENT OF PRINCIPAL COMPONENT ANALYSIS (PCA) FOR MODERATE AND HIGH RESOLUTION SATELLITE DATA

Year 2005, Volume: 6 Issue: 2, 39 - 58, 05.08.2016

Levent Genc Scot Smith

Abstract

Çalışmanin amacı, aynı çalışma alanında farklı uydu görüntüleri için ana bileşenlerin (PCs) belirlenmesi ve karşılaştırılmasıdır. Bu amaçla beş değişik uydu görüntüsü test edildi. Bunlar: (a) orta çözünürlükteki (20-30m) uydu görüntüleri: (1) Amerikan Landsat Enhanced Thematic Mapper Plus (ETM+), (2) Hindistan Remote Sensing Satellite (IRS), ve (3)Fransız Satellite Pour l'Observation de la Terre (SPOT) ve (b) yüksek çözünürlükteki uydu görüntüleri (1) (4m) Amerikan IKONOS ve (2) Kanada teknoloji yüksek çözünürlükteki çok kanallı hava görüntüsüdür (1m) (CASI). Orta ve yüksek çözünürlükteki görüntülerin ana bileşenler analizi (PCA) sonuçları karşılaştırıldığında, ilk üç bileşenlerinin orijinal uydu görüntüsünün % 99.9’unu temsil ettiği tespit edilmiştir, geri kalan kanalların ise gürültü sinyallerinden oluştuğu görülmüştür. Bu veriler doğrultusunda, tarım ve ıslak alanlar için yapılacak bitki örtüsü sınıflandırmalarında ilk üç bileşenin, orijinal görüntülerin yerine kullanılmasının tercih edilebileceği belirlenmiştir.

Keywords

CASI Landsat ETM+ IRS SPOT IKONOS Temel Element Analizi

References

  • CARR, J., and K. MATANAWI. 1999. Correspondence analysis for principal components transformation of multispectral and hyperspectral digital images. Photogrammetry Engineering and Remote Sensing 65(8):909-914.
  • ERDAS Imagine, 1999, Field Guide. Copyright 1982 - 1999 by ERDAS, Inc. Atlanta, Georgia.
  • GENC, L. SMITH, S, DEWITT, B, 2005. Using Satellite Imagery and LIDAR Data to Corroborate an Adjudicated Ordinary High Water Line. International Journal of Remote Sensing (accepted for publication February 2005)
  • GONZALEZ, R. and WOODS, R., 1993. Digital Image Processing, Addison—Wesley Publishing Company, Menlo Park, CA pp. 148-156.
  • HUNTER, E. L. and POWER, C. H., 2002. An assessment of two classification methods for mapping Thames Estuary inertial habitat using CASI data. International Journal of Remote Sensing 23: 2989-3008.
  • JENSEN, J. R., 1996. Introductory digital image processing: a remote sensing perspective London: Prentice-Hall Inc., 2nd edition pp. 172-176.
  • LILESAND, T.M., and KIEFER, R.W. 2000. Remote Sensing and Image interpretation. 4th Edition. John Wiley & Sons. New York. Pp. 572-596.
  • MATHER P. 1999. Computer Proce New York, NY, USA pp.126-137.
  • RICHARDS, J.A. and JIA, X., 1999. Digital Image Processing, Springer-Verlag: NewYork. pp. 133-143.
  • ROGERSON, A. P, 2001. Statistical Methods for Geography, SAGE Publication, Thousand Oaks, California pp. 194-197.
  • SINGH, A. and HARRISON A., 1985, Standardized Principal Components. International Journal of Remote Sensing 6: 883-896.
  • VANI, K., SHAMMUGAYEL, S., and MARRUTHACHALAM, M., 2001. Fusion of IRS-LISS III and Pan Images Using Different Resolution Ratio. The 22nd Asian Conference on Remote Sensing 5-9 November 2001, Singapore.

ANA BİLEŞENLER ANALİZİ YARDIMIYLA ORTA VE YÜKSEK ÇÖZÜNÜRLÜKTEKİ UYDU GÖRÜNTÜLERİNİN İNCELENMESİ

Year 2005, Volume: 6 Issue: 2, 39 - 58, 05.08.2016

Levent Genc Scot Smith

Abstract

The objective of this study was to determine and compare the principal components for different satellite imagery in the same study area. Five different remote sensing data sources were tested. They are: (a) (i) the moderate resolution satellite images from the Landsat Enhanced Thematic Mapper Plus (ETM+), (ii) the Indian Remote Sensing Satellite (IRS), and (iii) French Satellite Pour l'Observation de la Terre (SPOT) and (b) (iv) high-resolution satellite images from IKONOS and (v) airborne hyperspectral images taken by the Compact Airborne Spectral Imaging system (CASI). Among all the principle components (PCs) for all the datasets, the first three PCs contain most of the variance of the original datasets and all the other PC bands contain noise for both moderate and high-resolution images. From these results, it was concluded that instead of original images the first three PCs could be used for classifications in agricultural and wetland areas.

Keywords

CASI Landsat TM IRS SPOT IKONOS Principal Component

References

  • CARR, J., and K. MATANAWI. 1999. Correspondence analysis for principal components transformation of multispectral and hyperspectral digital images. Photogrammetry Engineering and Remote Sensing 65(8):909-914.
  • ERDAS Imagine, 1999, Field Guide. Copyright 1982 - 1999 by ERDAS, Inc. Atlanta, Georgia.
  • GENC, L. SMITH, S, DEWITT, B, 2005. Using Satellite Imagery and LIDAR Data to Corroborate an Adjudicated Ordinary High Water Line. International Journal of Remote Sensing (accepted for publication February 2005)
  • GONZALEZ, R. and WOODS, R., 1993. Digital Image Processing, Addison—Wesley Publishing Company, Menlo Park, CA pp. 148-156.
  • HUNTER, E. L. and POWER, C. H., 2002. An assessment of two classification methods for mapping Thames Estuary inertial habitat using CASI data. International Journal of Remote Sensing 23: 2989-3008.
  • JENSEN, J. R., 1996. Introductory digital image processing: a remote sensing perspective London: Prentice-Hall Inc., 2nd edition pp. 172-176.
  • LILESAND, T.M., and KIEFER, R.W. 2000. Remote Sensing and Image interpretation. 4th Edition. John Wiley & Sons. New York. Pp. 572-596.
  • MATHER P. 1999. Computer Proce New York, NY, USA pp.126-137.
  • RICHARDS, J.A. and JIA, X., 1999. Digital Image Processing, Springer-Verlag: NewYork. pp. 133-143.
  • ROGERSON, A. P, 2001. Statistical Methods for Geography, SAGE Publication, Thousand Oaks, California pp. 194-197.
  • SINGH, A. and HARRISON A., 1985, Standardized Principal Components. International Journal of Remote Sensing 6: 883-896.
  • VANI, K., SHAMMUGAYEL, S., and MARRUTHACHALAM, M., 2001. Fusion of IRS-LISS III and Pan Images Using Different Resolution Ratio. The 22nd Asian Conference on Remote Sensing 5-9 November 2001, Singapore.
There are 12 citations in total.

Details

Other ID JA55RN98DT
Journal Section Articles
Authors

Levent Genc This is me

Scot Smith This is me

Publication Date August 5, 2016
Published in Issue Year 2005 Volume: 6 Issue: 2

Cite

APA Genc, L., & Smith, S. (2016). ASSESSMENT OF PRINCIPAL COMPONENT ANALYSIS (PCA) FOR MODERATE AND HIGH RESOLUTION SATELLITE DATA. Trakya Üniversitesi Fen Bilimleri Dergisi, 6(2), 39-58.
AMA Genc L, Smith S. ASSESSMENT OF PRINCIPAL COMPONENT ANALYSIS (PCA) FOR MODERATE AND HIGH RESOLUTION SATELLITE DATA. Trakya Univ J Sci. August 2016;6(2):39-58.
Chicago Genc, Levent, and Scot Smith. “ASSESSMENT OF PRINCIPAL COMPONENT ANALYSIS (PCA) FOR MODERATE AND HIGH RESOLUTION SATELLITE DATA”. Trakya Üniversitesi Fen Bilimleri Dergisi 6, no. 2 (August 2016): 39-58.
EndNote Genc L, Smith S (August 1, 2016) ASSESSMENT OF PRINCIPAL COMPONENT ANALYSIS (PCA) FOR MODERATE AND HIGH RESOLUTION SATELLITE DATA. Trakya Üniversitesi Fen Bilimleri Dergisi 6 2 39–58.
IEEE L. Genc and S. Smith, “ASSESSMENT OF PRINCIPAL COMPONENT ANALYSIS (PCA) FOR MODERATE AND HIGH RESOLUTION SATELLITE DATA”, Trakya Univ J Sci, vol. 6, no. 2, pp. 39–58, 2016.
ISNAD Genc, Levent - Smith, Scot. “ASSESSMENT OF PRINCIPAL COMPONENT ANALYSIS (PCA) FOR MODERATE AND HIGH RESOLUTION SATELLITE DATA”. Trakya Üniversitesi Fen Bilimleri Dergisi 6/2 (August 2016), 39-58.
JAMA Genc L, Smith S. ASSESSMENT OF PRINCIPAL COMPONENT ANALYSIS (PCA) FOR MODERATE AND HIGH RESOLUTION SATELLITE DATA. Trakya Univ J Sci. 2016;6:39–58.
MLA Genc, Levent and Scot Smith. “ASSESSMENT OF PRINCIPAL COMPONENT ANALYSIS (PCA) FOR MODERATE AND HIGH RESOLUTION SATELLITE DATA”. Trakya Üniversitesi Fen Bilimleri Dergisi, vol. 6, no. 2, 2016, pp. 39-58.
Vancouver Genc L, Smith S. ASSESSMENT OF PRINCIPAL COMPONENT ANALYSIS (PCA) FOR MODERATE AND HIGH RESOLUTION SATELLITE DATA. Trakya Univ J Sci. 2016;6(2):39-58.