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• W2972153831 abstract "The inadequate land cover data have predominantly limited the significance and impacts of land cover. Even though remote sensing or satellite imaging has been deployed in mapping several temporal and spatial scales, but its full effort was not yet recognized. Hence, this paper intends to implement a novel land cover classification model through optimal deep learning architecture. Here, it includes three main phases namely, (i) Segmentation (ii) Feature Extraction and (iii) Classification. Initially, the land cover image is segmented, and subjected for feature extraction process. For feature extraction, Vegetation Indexes (VI), such as Simple Ratio (SR), Normalized Difference Vegetation Index (NDVI) and Kauth–Thomas Tasseled Cap, are extracted. Furthermore, these features are classified using Neural Network (NN) and Convolutional Neural Network (CNN). In both the classifiers, the number of hidden neuron is optimized by modified Water wave Optimization (WWO) called Modified Propagation Update based WWO (MPU-WWO). The optimization of hidden neurons is done in such a way that the classification accuracy should be high, which is taken as the main objective." @default.
• W2972153831 created "2019-09-12" @default.
• W2972153831 creator A5012431721 @default.
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• W2972153831 date "2019-09-04" @default.
• W2972153831 modified "2023-10-06" @default.
• W2972153831 title "Classification of land cover images using modified water wave Optimization-based hybrid classifier" @default.
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• W2972153831 doi "https://doi.org/10.1080/1206212x.2019.1660835" @default.
• W2972153831 hasPublicationYear "2019" @default.
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