ICTACT Journal on Image and Video Processing

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Ensemble Classifier Based Multiclass Vegetation Classification System


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1 Department of Information Science and Engineering, SDM College of Engineering and Technology, India
     

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The applicability of remote sensing is improving hand in hand with time. Various research works focus on remote sensing technology, as it is one of the hottest research topics. This paper is all about satellite image crop classification. The crops being present in a particular location is differentiated by means of a classification algorithm. However, it is difficult to attain reasonable accuracy rates, as the images are captured from a greater altitude. This research article focuses to present a satellite image classification system for distinguishing between the crops being present in the agricultural area. To achieve the research goal, the entire work is broken down into satellite image pre-processing, feature extraction and classification. The satellite images are mostly affected by noise and poor contrast. These issues are addressed by employing bilateral filter and adaptive histogram equalization technique. The Gabor Local Vector Pattern (GLVP) based Scale Invariant Feature Transform (SIFT) features are extracted from the pre-processed images. The crops being present in a location are distinguished by means of ensemble classifier, which is a combination of k-Nearest Neighbour (k-NN), Support Vector Machine (SVM) and Extreme Learning Machine (ELM). The performance of the ensemble classifier is compared with the individual classifiers, and the ensemble classifier outperforms the other classifiers in terms of classification accuracy, sensitivity and specificity rates.

Keywords

Extreme Learning Machine, SIFT, Ensemble Classifier, Classification System.
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  • Pradeep Kumar, Rajendra Prasad, Varun Narayan Mishra, Dileep Kumar Gupta Arti Choudhary and Prashant K. Srivastava, “Artificial Neural Network with Different Learning Parameters for Crop Classification using Multispectral Datasets”, Proceedings of International Conference on Microwave, Optical and Communication Engineering, pp. 18-20, 2015.

  • Mustafa Teke and Yasemin Yardimci, “Classification of Crops using Multitemporal Hyperion Images”, Proceedings of 4th International Conference on Agro-Geoinformatics, pp. 20-24, 2015.

  • Benson Kipkemboi Kenduiywo, Damian Bargiel and Uwe Soergel, “Higher Order Dynamic Conditional Random Fields Ensemble for Crop Type Classification in Radar Images”, IEEE Transactions on Geoscience and Remote Sensing, Vol. 55, No. 8, pp. 4638-4654, 2017.

  • Suhas S. Athani and C.H. Tejeshwar, “Support Vector Machine-Based Classification Scheme of Maize Crop”, Proceedings of IEEE 7th International Conference on Advance Computing, pp. 1-7, 2017.

  • Preetha Rajan, B. Radhakrishnan and L. Padma Suresh, “Detection and Classification of Pests from Crop Images using Support Vector Machine”, Proceedings of International Conference on Emerging Technological Trends, pp. 21-27, 2016.

  • Rajesh K. Dhumal, Amol D. Vibhute, Ajay D. Nagne, Yogesh D. Rajendra, Karbhari V. Kale and Suresh C. Mehrotra, “Fuzzy Convolution Tactic for Classification of Spatial Pattern and Crop Area”, Proceedings of International Conference on Intelligent Systems and Control, pp. 341-348, 2017.


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  • Ensemble Classifier Based Multiclass Vegetation Classification System

Abstract Views: 259  |  PDF Views: 0

Authors

Anita Dixit
Department of Information Science and Engineering, SDM College of Engineering and Technology, India

Abstract


The applicability of remote sensing is improving hand in hand with time. Various research works focus on remote sensing technology, as it is one of the hottest research topics. This paper is all about satellite image crop classification. The crops being present in a particular location is differentiated by means of a classification algorithm. However, it is difficult to attain reasonable accuracy rates, as the images are captured from a greater altitude. This research article focuses to present a satellite image classification system for distinguishing between the crops being present in the agricultural area. To achieve the research goal, the entire work is broken down into satellite image pre-processing, feature extraction and classification. The satellite images are mostly affected by noise and poor contrast. These issues are addressed by employing bilateral filter and adaptive histogram equalization technique. The Gabor Local Vector Pattern (GLVP) based Scale Invariant Feature Transform (SIFT) features are extracted from the pre-processed images. The crops being present in a location are distinguished by means of ensemble classifier, which is a combination of k-Nearest Neighbour (k-NN), Support Vector Machine (SVM) and Extreme Learning Machine (ELM). The performance of the ensemble classifier is compared with the individual classifiers, and the ensemble classifier outperforms the other classifiers in terms of classification accuracy, sensitivity and specificity rates.

Keywords


Extreme Learning Machine, SIFT, Ensemble Classifier, Classification System.

References