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Dixit, Anita
- Curvelet Based Satellite Image Natural Resource Classification System Using EIM
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Authors
Affiliations
1 Department of Computer Science, Jawaharlal Nehru Technological University, Anantapur, IN
2 Department of Computer Science, Vasavi College of Engineering, IN
3 Department of Computer Science, JNTUA College of Engineering, IN
1 Department of Computer Science, Jawaharlal Nehru Technological University, Anantapur, IN
2 Department of Computer Science, Vasavi College of Engineering, IN
3 Department of Computer Science, JNTUA College of Engineering, IN
Source
ICTACT Journal on Image and Video Processing, Vol 8, No 4 (2018), Pagination: 1759-1763Abstract
Remote sensing is one of the hottest topics of research, which intends to study or analyze a particular object in the topographic map. The monitoring and management is possible when it is possible to differentiate the objects in the satellite image. However, satellite image classification is not easy, as it consists of numerous minute details. In addition to this, the accuracy and faster execution of the classification system are significant factors. This article presents a satellite image classification system that is capable of differentiating between soil, vegetation and water bodies. To achieve the goal, we categorize the entire system into three major phases; they are satellite image pre-processing, feature extraction and classification. The initial phase attempts to denoise the satellite image by the adaptive median filter and the contrast enhancement is done by Contrast Limited Adaptive Histogram Equalization (CLAHE). As the satellite image possess many important features, this work extracts curvelet moments by applying curvelet transform. The feature vector is formed out of these curvelet moments and the ELM classifier is used to train these features. The performance of the proposed approach is observed to be satisfactory in terms of sensitivity, specificity, and accuracy.Keywords
Remote Sensing, Satellite Image Classification, Feature Extraction.
References
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- Ensemble Classifier Based Multiclass Vegetation Classification System
Abstract Views :243 |
PDF Views:0
Authors
Affiliations
1 Department of Information Science and Engineering, SDM College of Engineering and Technology, IN
1 Department of Information Science and Engineering, SDM College of Engineering and Technology, IN
Source
ICTACT Journal on Image and Video Processing, Vol 10, No 2 (2019), Pagination: 2076-2082Abstract
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
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- Nataliia Kussul, Mykola Lavreniuk, Sergii Skakun and Andrii Shelestov, “Deep Learning Classification of Land Cover and Crop Types Using Remote Sensing Data”, IEEE Geoscience and Remote Sensing Letters, Vol. 14, No. 5, pp. 778-782, 2017.
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- Mustafa Teke and Yasemin Yardimci, “Classification of Crops using Multitemporal Hyperion Images”, Proceedings of 4th International Conference on Agro-Geoinformatics, pp. 20-24, 2015.
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