Titlebook: Dimensionality Reduction of Hyperspectral Imagery; Arati Paul,Nabendu Chaki Book 2024 The Editor(s) (if applicable) and The Author(s), und

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Performance Assessment and Dataset Description,ication is a supervised task, it requires ground truth information or the labelled samples to perform training. Hence, in this chapter, detailed descriptions of datasets and corresponding ground truth classes are provided. The same set of data is used for conducting all the experiments that are mentioned in subsequent chapters.

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Ranking-Based Band Selection Using Correlation and Variance Measure,nsidered similar, and the one with higher variance is accepted as being more discriminating. Finally, the selected bands are classified, and overall accuracy (OA) is calculated. This method is compared with four other existing state-of-the-art methods in the similar field in terms of OA and execution time for evaluating the performance.

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Performance Assessment and Dataset Description, analysis. Classification is one of the important tasks in remote sensing where hyperspectral images are used. Hence, to assess the performance of DR methods, spectrally reduced datasets are further classified, and the overall classification accuracies are measured. Other than overall accuracy, othe

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Spectral Feature Extraction Using Pooling,e image. Hence, dimensionality reduction is applied as an essential pre-processing step in hyperspectral data analysis. Pooling is a technique of reducing spatial dimension and is successfully applied in intermediate layers of convolutional neural networks for spatial feature extraction. There are v

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Dimensionality Reduction Using Band Optimisation, optimisation-based band selection approaches are discussed using genetic algorithms (GAs) and particle swam optimisation (PSO). In contrast to exhaustive search algorithms, optimisation-based approach employs fast search measures to find a better solution in a large solution space. The key strength

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Data-Driven Approach for Hyperspectral Band Selection,agery (HSI) to improve classification accuracy. In most of the DR methods, the required number of selected/extracted bands is given by the user. However, in reality, it is difficult to perceive the required number of bands before the analysis starts. A particular number of (selected or extracted) fe

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