Publication Type : Conference Paper
Publisher : 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI)
Source : 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI) (2017)
Url : https://ieeexplore.ieee.org/document/8125870/(link is external)
Keywords : agriculture, automatic identification, Big data, big data challenge, Crops, Dimensionality reduction, Feature extraction, geophysical image processing, geophysical techniques, Huge dimension, hyperspectral crop classification, hyperspectral image processing, Hyperspectral imaging, image classification, Minimum Noise Transform, Minimum Noise Transform technique, Principal component analysis, spatial features, spatial indices, spectral features, spectral indices, spectral similarity problem, Supervised classification, support vector machine classifier, Support vector machines, vegetation indices, Vegetation mapping
Campus : Coimbatore
School : School of Engineering
Center : Electronics Communication and Instrumentation Forum (ECIF)
Department : Electronics and Communication
Verified : Yes
Year : 2017
Abstract : The world keeps on generating massive amounts of data daily, which lead to the demands for finding new methods to deal with challenges related to `Big Data'. Automatic identification of crops is one of the major applications in the field of the hyperspectral image processing. The major curbs involved in crop classification are: i) Huge dimension of the data and ii) Spectral Similarity amongst crops. This paper proposes a new method of crop classification in which fusion of spectral, spatial and vegetation indices is used as the feature set to overcome the limitation of spectral similarity problem. Here the processing is done in two stages: dimensionality reduction and supervised classification. The dimensionality reduction is done using Principal Component Analysis (PCA) and Minimum Noise Transform (MNF) technique and the selected dimensions are classified using Support Vector Machine Classifier. The results obtained using the proposed technique show that on integrating the vegetation indices along with the spectral and spatial features have raised the accuracy to 98.0749% and helped achieve a kappa coefficient of 0.9769.
Cite this Research Publication : S. Reshma, S. Veni, and George, J. E., “Hyperspectral Crop Classification using Fusion of Spectral, Spatial Features and Vegetation Indices: Approach to the Big Data Challenge”, in 2017 International Conference on Advances in Computing, Communications and Informatics (ICACCI), 2017.