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Objectives:To present an extraction technique for the classification of the hyperspectral crop using the spatial-spectral feature. Methods: This paper presents a spatial-spectral feature extraction method employing the Image fusion technique and intrinsic feature extraction and a model for Improved Decision Boundary (IDB) using Support Vector Machine (SVM). Findings:The experiments have been conducted by using the Indian pines dataset which was extracted using the AVIRIS sensor. The dataset comprises of 16 distinctive classes such as corn, wheat, oats etc, which have used for evaluation of our model. Before the evaluation of the dataset the model has been trained using different training datasets in order to increase the accuracy and reduce misclassification. Moreover, the Spatial-Spectral Feature (SSF) model aided in distinguishing between crop intrinsic features and shadow element under dynamic environment condition. Our model attained 99.54%, 99.4%, 99.25% and 9.8 sec for OA accuracy, AA accuracy, Kappa and Time respectively. Furthermore, the overall accuracy of the model for the Support Vector Machine-3-dimensional discrete wavelet transform (SVM-3DDWT), Convolutional Neural Network and Spatial-Spectral Feature Extraction Technique showed result of 94.28%, 96.12% and 99.4% respectively. Other existing models showed a low accuracy for the same dataset. Further, for addressing class imbalance issues this work modelled an improved decision boundary model for SVM by considering soft-margin rather than hard margin. The SSF-IDBSVM model achieves much better accuracies with less misclassification in comparison with recent deep learning-based HSI classification model. Novelty: Recently, several feature extraction and deep learning-based crop classification model have been modelled. However, existing crop classification fails to distinguish crop intrinsic feature concerning shadow feature; further, consider hard decision boundary; as a result, high misclassification is induced for smaller class size. Hence, this study provides an extraction feature which provides the classification of the crop in less time with higher classification and less misclassification.

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The complexity in 3D virtual environment over the web is growing rapidly every day. This 3D virtual environment comprises a set of structured scenes and each scene has multiple 3D objects/meshes. Therefore the granular level of the block in a virtual environment is the object. In a virtual environment, it is required to give user interactions for every 3D object and at any point of time, it is enough if the system streams and brings in only the visible portion of the object from the server to the client by utilizing the limited network bandwidth and the limited client memory space. This streaming would reduce the time to present the rendered object to the requested clients. Further to reduce the time and effectively utilize the bandwidth and memory space, in the proposed study, an attempt is made to exploit the user interaction on 3D object and built a predictive agent which would minimize the latency in the rendering of the 3D mesh that is being streamed. The experiment result shows that the rendering time and cache miss rates are significantly reduced with the predictive agent

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