Classification Performance of TM Satellite Images

  • Laith A. Al Ani Departments of Physics, Al-Nahrain University, Jadiriya 64055, Baghdad, Iraq
  • Hasan S. Al Tahir Departments of Physics, Al-Nahrain University, Jadiriya 64055, Baghdad, Iraq
Keywords: Supervised classification-method, Maximum likelihood, Principle component analysis


In the present work, the remotely sensed images had gone through different stages before it become ready to be classified, firstly by transforming the raw images by using principle component analysis method (PCA). Applying PCA has shown that the first three bands gives 97.86% of the overall information that has been provided from the scene. RGB coloring method is adopted as an unsupervised method to classify the scene according to the RGB color combination for three principle component images, this unsupervised method guide us to a better selection of the ROI (region of interest),  it’s becoming more clarity. Histogram equalization method has been used to enhance the colored bands. The results showed that the selecting of ROI from the original TM gave classification accuracy (85.84%), whereas, after applying the RGB coloring model, the accuracy raised to (90.13%).  The accuracy has been shown to be equal (97.08%) after applying the enhancement method, and by applying the same methods on the PC- images the accuracy of classification raised to become (98.76%).


[1] Foody, G. M.; Mathur A.; “Toward intelligent training of supervised image classifications: directing training data acquisition for SVM classification”; Remote Sensing of Environment 93, 107–117, 2004.
[2] Foody, G. M.; Mathur A.; “The use of small training sets containing mixed pixels for accurate hard image classification: Training on mixed spectral responses for classification by a SVM”; Remote Sensing of Environment 103, 179–189, 2006.
[3] Al-Sebahi, A.; Al-Ani, L.; “Classification of Digital Satellite Images Using Selective Bands and Principal Component Images” M. Sc. Thesis, Al-Nahrain University, College of Science, 2002.
[4] Zhou, H.; Zhang, Y.; Yu, Z.; “Image classification based on region of interest detection”, Proc. SPIE 9813, MIPPR: Pattern Recognition and Computer Vision, Vol. 9813, 2015.
[5] Al-Ani, L.; “Classification of Digital Satellite Images”, Ph.D. Thesis, Al-Nahrain University, College of Science, 1996.
[6] Palaniswami, C.; Upadhyay, A. K.; Maheswarappa, H. P.; "Spectral mixture analysis for sub pixel classification of coconut", Current Science, 91(12), 1706 -1711, 2006.
[7] Perumal, K.; Bhaskaran, R.; “Supervised Classification Performance of Multi-Spectral Images”. J. Computing 2, 124-129, 2010.
[8] David, C.; “Environmental Science and Information Application Technology”, CRC press, 2015.
[9] Langer, H.; Falsaperla, S.; Masotti, M.; Campanini, R.; Spampinato, S.; Messina, A.; “Synopsis of supervised and unsupervised pattern classification techniques applied to volcanic tremor data at Mt Etna, Italy”, Geophys. J. Int. 178, 1132–1144, 2009.
[10] Richards, J. A.; ”Multispectral Transformations of Image Data”, Springer-Verlag Berlin Heidelberg, 2013.
[11] Al-Ani, L.; Al-Ani, A.; Alyaa, H. A.; “Principal Component Analysis of Multi-Temporal Image Pairs”, Iraqi Journal of Science 47(1), 220-226, 2006.
[12] Rodarmel, C.; Sha, J.; “Surveying and Land Information Systems", Vol. 62, No. 2, 115-000, 2002.
[13] Keerthana, P.; Sivasankar, A.; “The Impact of Lossy Compression on Hyper spectral Data Adaptive Spectral Un-mixing and PCA Classification”, IJISM, E 1, 35-37, 2013.
[14] Maini, R.; Aggarwal, H.; “A Comprehensive Review of Image Enhancement Techniques”. Journal of computing 2, 8-13, 2011.
[15] Korgaonkar, G. S.; Sedamka, R. R.; Bhandari, K.; “Hyper spectral Image Classification on Decision level fusion”, International Conference & Workshop on Recent Trends in Technology, (TCET), 2012.
How to Cite
Laith A. Al Ani, & Hasan S. Al Tahir. (2020). Classification Performance of TM Satellite Images. Al-Nahrain Journal of Science, 23(1), 62-68. Retrieved from