A Comparative Study on Pixel-based Classification and Object-Oriented Classification of Satellite Image

A Comparative Study on Pixel-based Classification and Object-Oriented Classification of Satellite Image
  IJETT-book-cover           
  
© 2022 by IJETT Journal
Volume-70 Issue-8
Year of Publication : 2022
Authors : Hafsa Ouchra, Abdessamad Belangour, Allae Erraissi
DOI : 10.14445/22315381/IJETT-V70I8P221

How to Cite?

Hafsa Ouchra, Abdessamad Belangour, Allae Erraissi, "A Comparative Study on Pixel-based Classification and Object-Oriented Classification of Satellite Image," International Journal of Engineering Trends and Technology, vol. 70, no. 8, pp. 206-215, 2022. Crossref, https://doi.org/10.14445/22315381/IJETT-V70I8P221

Abstract
Artificial intelligence is advancing rapidly in automatically recognising features from satellite imagery. Satellite imagery is of great interest to the computer science community, which seeks to give machines the ability to recognize their environment by classifying satellite images. This type of processing has shown great potential for monitoring large areas at a relatively low cost. Remote sensing and, in particular, satellite imagery provide Earth observation data that are collected, analyzed, and processed for civil and military purposes. They offer many possibilities for mapping and monitoring urban areas. Indeed, the analysis and classification of satellite images have many applications in meteorology, oceanography, fisheries, agriculture, biodiversity, geology, cartography, land use planning, warfare, etc. In this paper, we focus on satellite image classification, which is based on different algorithms belonging to different approaches that differ in terms of accuracy and quality of results. Hence, we propose in this paper to provide a comparative study of these approaches in terms of their algorithms and techniques, image resolutions, and image types and will show and discuss their strengths and weaknesses. In this comparative study, we will introduce each approach, select a set of comparison criteria, and apply a comparative methodology to obtain results. The methodology we have chosen for this purpose is WSM (Weighted Scoring Model), which corresponds to our needs. Indeed, this method allows us to assign a weight to each of our criteria to calculate a final score for each of our compared methods. The results obtained reveal the weaknesses and strengths of each of them and open opportunities for their future improvement.

Keywords
Computer vision, Remote sensing, Satellite image, WSM weighted, Classification.

Reference
[1] P. A. Longley and V. Mesev, “on the Measurement and Generalisation of Urban Form,” Environ. Plan. A, vol.32, no. 3, pp. 473–488, 2000.
[2] M. F. Goodchild, Recent Advances In Geographic Information Science, 2010.
[3] H. Ouchra and A. Belangour, “Satellite Image Classification Methods and Techniques: A Survey,” 2021 Ieee Int. Conf. Imaging Syst. Tech., pp. 1–6, Aug. 2021.
[4] S. D. Jawak, P. Devliyal, and A. J. Luis, “A Comprehensive Review on Pixel Oriented and Object Oriented Methods for Information Extraction From Remotely Sensed Satellite Images with a Special Emphasis on Cryospheric Applications,” Adv. Remote Sens., vol.04, no. 03, pp. 177–195, 2015.
[5] A. P. Cracknell and L. B. W. Hayes, “Introduction to Remote Sensing,” Introd. to Remote Sens,1991.
[6] S. Borra, R. Thanki, and N. Dey, Satellite Image Analysis : Clustering and Classification, 2019.
[7] N. Manohar, M. A. Pranav, S. Aksha, and T. K. Mytravarun, “Classification of Satellite Images,” Smart Innov. Syst. Technol., vol.195, no. 5, pp. 703–713, 2021.
[8] S. Jabari and Y. Zhang, “Very High Resolution Satellite Image Classification Using Fuzzy Rule-Based Systems,” Algorithms, vol.6, no. 4, pp. 762–781, 2013.
[9] M. K. Firozjaei, I. Daryaei, A. Sedighi, Q. Weng, and S. K. Alavipanah, “Homogeneity Distance Classification Algorithm (Hdca): A novel Algorithm for Satellite Image Classification,” Remote Sens., vol.11, no. 5, 2019.
[10] C. Pelletier, G. I. Webb, and F. Petitjean, “Temporal Convolutional Neural Network for the Classification of Satellite Image Time Series,” Remote Sens., vol.11, no. 5, pp. 1–25, 2019.
[11] Z. D. Uca Avci, M. Karaman, E. Ozelkan, and I. Papila, “A Comparison of Pixel-Based and Object-Based Classification Methods, A Case Study: Istanbul, Turkey,” 34th Int. Symp. Remote Sens. Environ. - Geoss Era Towar. Oper. Environ. Monit., no. February 2015, 2011.
[12] G. Yan, J. F. Mas, B. H. P. Maathuis, Z. Xiangmin, and P. M. Van Dijk, “Comparison of Pixel-Based and Object-Oriented Image Classification Approaches - A Case Study In A Coal Fire Area, Wuda, Inner Mongolia, China,” Int. J. Remote Sens., vol.27, no. 18, pp. 4039–4055, 2006.
[13] M. S. Tehrany, B. Pradhan, and M. N. Jebuv, “A Comparative Assessment Between Object and Pixel-Based Classification Approaches for Land Use/Land Cover Mapping Using Spot 5 Imagery,” Geocarto Int., vol.29, no. 4, pp. 351–369, 2014.
[14] D. C. Duro, S. E. Franklin, and M. G. Dubé, “A Comparison of Pixel-Based and Object-Based Image Analysis with Selected Machine Learning Algorithms for the Classification of Agricultural Landscapes Using Spot-5 Hrg Imagery,” Remote Sens. Environ., vol.118, pp. 259–272, 2012.
[15] L. Ma, M. Li, X. Ma, L. Cheng, P. Du, and Y. Liu, “A Review of Supervised Object-Based Land-Cover Image Classification,” Isprs J. Photogramm. Remote Sens., vol.130, pp. 277–293, 2017.
[16] L. Sparfel Et Al., “Des Sols En Zone Côtière to Cite This Version : Hal Id : Hal-00559730 Approche Orientée-Objet De L ’ Occupation Des Sols En Zone,” 2011.
[17] H. Ismanto, A. Doloksaribu, D. S. Susanti, and D. F. Septarini, “the Accuracy of Remote Sensing Image Interprepation on Changes In Land Use Suitability In Merauke Regency Papua,” Int. J. Eng. Trends Technol., vol.68, no. 10, pp. 42–47, 2020.
[18] N. I. Vargas-Cuentas, S. J. Ramos-Cosi, and A. Roman-Gonzalez, “Air Quality Analysis In Pasco Peru Using Remote Sensing,” Int. J. Eng. Trends Technol., vol.69, no. 12, pp. 30–38, 2021.
[19] H. Ouchra and A. Belangour, “Object Detection Approaches In Images: A Weighted Scoring Model Based Comparative Study,” Int. J. Adv. Comput. Sci. Appl., vol.12, no. 8, pp. 268–275, 2021.
[20] R. Defries, “Remote Sensing and Image Processing,” In Encyclopedia of Biodiversity: Second Edition, Elsevier Inc, pp. 389–399, 2013.
[21] J. Qian, Q. Zhoua, and Q. Houa, “Comparison of Pixel-Based and Object-Oriented Classification Methods for Extracting Built-Up Areas In Aridzone,” Undefined, 2007.
[22] A. Griffith and J. D. Headley, “Using a Weighted Score Model as an Aid to Selecting Procurement Methods for Small Building Works,” Constr. Manag. Econ., vol.15, no. 4, pp. 341–348, 1997.
[23] D. R. Baughman and Y. A. Liu, “Classification: Fault Diagnosis and Feature Categorization,” Neural Networks Bioprocess. Chem. Eng., pp. 110–171, Jan. 1995.
[24] S. Bouhsissin, N. Sael, and F. Benabbou, “Enhanced Vgg19 Model for Accident Detection and Classification From Video,” Proc. - 2021 Int. Conf. Digit. Age Technol. Adv. Sustain. Dev. Icdata 2021, pp. 39–46, 2021.
[25] M. Wurm, T. Stark, X. X. Zhu, M. Weigand, and H. Taubenböck, “Semantic Segmentation of Slums in Satellite Images Using Transfer Learning on Fully Convolutional Neural Networks,” Isprs J. Photogramm. Remote Sens., vol.150, no. February, pp. 59–69, 2019.
[26] M. Kuffer, K. Pfeffer, and C. Persello, “Special Issue ‘Remote-Sensing-Based Urban Planning Indicators,’” Remote Sens., vol.13, no. 7, 2021.
[27] H. Ouchra and A. Belangour, “Object Detection Approaches In Images: A Survey,” Thirteen. Int. Conf. Digit. Image Process. (Icdip 2021), vol.11878, P. 118780h, 2021.
[28] V. Lucieer and G. Lamarche, “Unsupervised Fuzzy Classification and Object-Based Image Analysis of Multibeam Data to Map Deep Water Substrates, Cook Strait, New Zealand,” Cont. Shelf Res., vol.31, no. 11, pp. 1236–1247, 2011.
[29] D. Zhang, L. Zhang, V. Zaborovsky, F. Xie, Y. Wu, and T. Lu, “Research on the Pixel-Based and Object-Oriented Methods of Urban Feature Extraction with Gf-2 Remote-Sensing Images,” 2019.
[30] A. Achbun Et Al., “Comparative Study of the Object-Oriented Classification of a Spot5 Image for Land Cover Mapping Via Ecognition ® 9 to Cite This Version: Hal Id: Hal-00915067 Comparative Study of the Oriented Classification of a Spot5 Image for,” 2013.
[31] M. Labied and A. Belangour, “Automatic Speech Recognition Features Extraction Techniques: A Multi-Criteria Comparison,” Int. J. Adv. Comput. Sci. Appl., vol.12, no. 8, pp. 177–182, 2021.
[32] Y. Matrane, F. Benabbou, and N. Sael, “Sentiment Analysis Through Word Embedding Using Arabert: Moroccan Dialect Use Case,” Proc. - 2021 Int. Conf. Digit. Age Technol. Adv. Sustain. Dev. Icdata 2021, pp. 80–87, 2021.
[33] Syeda Sara Samreen, Hakeem Aejaz Aslam, "Hyperspectral Image Classification using Deep Learning Techniques: A Review," International Journal of Electronics and Communication Engineering, vol. 9, no. 6, pp. 1-4, 2022. Crossref, https://doi.org/10.14445/23488549/IJECE-V9I6P101.