Abstract
This paper highlights the study regarding the classification of crop types using the techniques based on Gray Level Co-occurrence Matrix (GLCM) and support vector machine (SVM). The dataset used was from IRS-LISS IV sensor with 5.8 m spatial resolution having three spectral bands of date 4-October 2014 for our chosen location at 20°07′13.5″N 75°23′05.3″E. Classification of all three bands followed by classification of GLCM measures (of all three bands) was accomplished by using Support Vector Machine classifier with Radial Basis Function. The accuracy of classification obtained from GLCM was 90.29% with the Kappa coefficient 0.88 whereas the corresponding values obtained from three band classification were 86.04% and 0.83, indicating the superiority of the GLCM-based approach.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Gao, J (2008) Digital analysis of remotely sensed imagery. McGraw-Hill Professional
Han Y, Kim H, Choi J, Kim Y (2012) A shape–size index extraction for classification of high resolution multispectral satellite images. Int J Remote Sens. 33(6):1682–1700
Dhumal RK, Yogesh Rajendra KV, Mehrotra SC (2013) Classification of crops from remotely sensed images: an overview. Int. J. Eng. Res. Appl. (IJERA) 3(3):758–761
http://www.onefivenine.com/india/villages/Aurangabad-District/Phulambri/Kanhori Accessed 27 Mar 2016
NRSA, 2003, IRS P6 Data User Manual, IRS-P6/NRSA/NDC/HB-08/03 Accessed Apr 2016
Albregtsen, F (2008) Statistical texture measures computed from gray level coocurrence matrices. Image processing laboratory, vol 5, Department of informatics, university of oslo
Dhumal RK, Vibhute AD, Nagne AD, Rajendra YD, Kale KV, Mehrotra SC (2015) Advances in classification of crops using remote sensing data. Int. J. Adv. Remote Sens. GIS 4(1):1410
Anys H, Bannari A, He DC, Morin D (1994) Texture analysis for the mapping of urban areas using airborne MEIS-II images. Proc. First Int. Airborne Remote Sens. Conf. Exhib. 3:231–245
Hall-Beyer, M (2006) The GLCM tutorial home page. http://www.fp.ucalgary.ca/mhallbey/tutorial.htm. Updated February 2007. Accessed Mar 2016
Haralick R, Shanmugan K, Dinstein I (1973) Textural features for image classification. IEEE Trans. Syst. Man Cybern. 3(6):610–621
Hsu CW, Chang CC, Lin CJ (2003) A practical guide to support vector classification
Mountrakis G, Im J, Ogole C (2011) Support vector machines in remote sensing: A review. ISPRS J. Photogram. Remote Sens. 66(3):247–259
Waske B, Benediktsson J, Sveinsson, J (2009) Classifying remote sensing data with support vector machines and imbalanced training data. Multiple Classifier Syst. pp 375–384
Richards, JA, Richards, JA (1999). Remote sensing digital image analysis, vol 3. Springer, Berlin
Vibhute AD, Kale KV, Dhumal RK, Mehrotra SC (2015) Soil type classification and mapping using hyperspectral remote sensing data. In: International Conference on Man and Machine Interfacing (MAMI), IEEE, pp. 1–4.
Dixit A, Hedge N, Reddy BE (2017) Texture feature based satellite image classification scheme using SVM. Int. J. Appl. Eng. Res. 12(13):3996–4003
Zhang X, Cui J, Wang W, Lin C (2017) A study for texture feature extraction of high-resolution satellite images based on a direction measure and gray level co-occurrence matrix fusion algorithm. Sensors, 17(7), 1474 Appendix: Springer-Author Discount
Acknowledgements
Authors would like to convey our thankfulness to Department of Electronics and IT, Government of India intended for financial support under Visvesvaraya Ph.D. Scheme. We extend our honest gratitude to DST FIST and UGC SAP program for providing infrastructure facilities under UGC SAP (II) DRS Phase I F.No.-3-42/2009, Phase II 415/2015/DRS II sanctioned to Department of Computer Science & IT, Dr. Babasaheb Ambedkar Marathwada University, Aurangabad (MS) India.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Dhumal, R.K. et al. (2019). A Spatial and Spectral Feature Based Approach for Classification of Crops Using Techniques Based on GLCM and SVM. In: Panda, G., Satapathy, S., Biswal, B., Bansal, R. (eds) Microelectronics, Electromagnetics and Telecommunications. Lecture Notes in Electrical Engineering, vol 521. Springer, Singapore. https://doi.org/10.1007/978-981-13-1906-8_5
Download citation
DOI: https://doi.org/10.1007/978-981-13-1906-8_5
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-1905-1
Online ISBN: 978-981-13-1906-8
eBook Packages: EngineeringEngineering (R0)