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Mehra, Raghav
- Supervised SVM Classification of Rainfall Datasets
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Authors
Affiliations
1 Wells Fargo India Solutions Pvt Ltd, Hyderabad - 500081, Telangana, IN
2 CSE Department, VNRVJIET, Hyderabad - 500090, Telangana, IN
3 CSE Department, Bhagwant Institute of Technology, Muzaffarnagar - 251315, Uttar Pradesh,, IN
1 Wells Fargo India Solutions Pvt Ltd, Hyderabad - 500081, Telangana, IN
2 CSE Department, VNRVJIET, Hyderabad - 500090, Telangana, IN
3 CSE Department, Bhagwant Institute of Technology, Muzaffarnagar - 251315, Uttar Pradesh,, IN
Source
Indian Journal of Science and Technology, Vol 10, No 15 (2017), Pagination:Abstract
Objectives: The model built in this paper is used to classify the rainfall datasets in identifying districts of more rainfall and of lesser rainfall in the state of Andhra Pradesh. Methods: In this paper support vector machine, random forest, Knearest neighbor and decision tree classification methods have been used to classify rainfall data sets which is divided into training set and test set for classification and later validation of the obtained results. Findings: Based on various statistical parameters like sensitivity, prevalence, detection rate, specificity, and detection prevalence it has been concluded that support vector machine classification methods is better than any other classification method used in the research. Rainfall data sets are used to initially build the classification model and the results are tested against the test set. Using the confusion matrix thus obtained the mentioned statistical parameters are obtained to establish the supremacy of support vector machine classification method. Applications: Examples of satellite imagery has become ever more significant in numerous application domains such as ecology monitoring and alternative discovery. Rainfall classification is the application used herein.Keywords
Classification, Data Mining, Classifier, Support Vector Machines, SVM.Full Text
- Extraction of Antarctic Ice Features Using Hybrid Polarimetric RISAT-1 SAR Data
Abstract Views :81 |
PDF Views:59
Authors
Affiliations
1 Physics Department, Gujarat University, Ahmedabad 380 009, IN
2 Space Applications Centre, Ahmedabad 380 015, IN
1 Physics Department, Gujarat University, Ahmedabad 380 009, IN
2 Space Applications Centre, Ahmedabad 380 015, IN
Source
Current Science, Vol 124, No 12 (2023), Pagination: 1445-1453Abstract
Compact polarimetry has gained popularity due to its advantages, such as larger swath, simple architecture and low power consumption. The backscattered signal and scattering decomposition vary for different targets based on their electrical, geometrical and structural properties. As of now, the potential of hybrid polarimetric synthetic aperture radar (SAR) data for exploring Antarctic ice features is not fully explored. Here, we present a comprehensive polarimetric feature analysis and classification results of the hybrid polarimetric dataset acquired by RISAT-1 near the Indian Antarctic research station Maitri. The single-look complex images have been subjected to polarimetric data processing for extracting Antarctic ice features using POLSARPRO software. The polarimetric coherence matrix is generated and then filtered to eliminate speckles. Raney m–χ decomposition technique has been utilized to understand the scattering mechanism of the targets. The decomposed RGB image is classified using Wishart-supervised classification, and classification accuracy is assessed using a confusion matrix. It is found that the comparatively simple hybrid polarimetric SAR provides sufficient information to detect and discriminate various Antarctic ice features. Features such as rifts, ice–rises, ice shelves and icebergs are clearly discriminated using Wishart-supervised classification. It is also found that the overall accuracy of the classification of study areas varies between 80% and 97%, suggesting a good classification outcome.Keywords
Classification Accuracy, Confusion Matrix, Hybrid Polarimetry, Ice Features, m–χ Decomposition, Synthetic Aperture Radar Data.Full Text
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