標(biāo)題: Titlebook: Dimensionality Reduction of Hyperspectral Imagery; Arati Paul,Nabendu Chaki Book 2024 The Editor(s) (if applicable) and The Author(s), und [打印本頁(yè)] 作者: Croching 時(shí)間: 2025-3-21 17:55
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書(shū)目名稱Dimensionality Reduction of Hyperspectral Imagery讀者反饋學(xué)科排名
作者: 善辯 時(shí)間: 2025-3-21 20:41
Dimensionality Reduction: State of the Art,most discriminating characteristics, and therefore, the physical relevance of the selected bands is maintained. This chapter discusses the state-of-the-art methods of dimensionality reduction of HSI. Specific gap areas are also analysed, and accordingly, improved methodologies are given in subsequen作者: Noisome 時(shí)間: 2025-3-22 04:18 作者: tympanometry 時(shí)間: 2025-3-22 08:22 作者: 準(zhǔn)則 時(shí)間: 2025-3-22 09:39
Data-Driven Approach for Hyperspectral Band Selection,en band selection (BS) approach employs multi-featured analysis and signal-to-noise-ratio (SNR)-based band prioritisation for selecting discriminating bands. The signal quantisation process is used in the supervised data-driven approach for distinctly identifying each class signature pattern using a作者: archenemy 時(shí)間: 2025-3-22 14:25
Concluding Remarks and Way Forward,ion time. The effect of noise is also analysed for optimisation and ranking-based band selection (BS) methods. The data-driven approaches for band selection show significant advantage as they do not depend on user perception to select the required number of discriminating bands from the data. At the作者: archenemy 時(shí)間: 2025-3-22 18:28 作者: Commonwealth 時(shí)間: 2025-3-22 22:28 作者: grovel 時(shí)間: 2025-3-23 01:37
Jochen Seemann,Jürgen Wolff von Gudenbergmost discriminating characteristics, and therefore, the physical relevance of the selected bands is maintained. This chapter discusses the state-of-the-art methods of dimensionality reduction of HSI. Specific gap areas are also analysed, and accordingly, improved methodologies are given in subsequen作者: Adenocarcinoma 時(shí)間: 2025-3-23 06:47 作者: Ingrained 時(shí)間: 2025-3-23 13:11
Zusammenfassende Darstellung der Ergebnisse,on accuracies and improved the speed of convergence. The reduction of spatial dimension also contributed in improving the computation time. The presented methods also reduced the effect of noise in hyperspectral imagery for efficient band selection.作者: 沒(méi)有希望 時(shí)間: 2025-3-23 16:07 作者: opinionated 時(shí)間: 2025-3-23 20:53 作者: 以煙熏消毒 時(shí)間: 2025-3-23 22:51 作者: 軍火 時(shí)間: 2025-3-24 05:20
978-3-031-42669-8The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl作者: 失眠癥 時(shí)間: 2025-3-24 09:16
Jochen Seemann,Jürgen Wolff von Gudenbergd the associated dataset, this chapter introduces the background of remote sensing (RS), which includes a brief discussion on electromagnetic spectrum, atmospheric transmission window, atmospheric scattering, surface reflection, reflectance curve and RS data characteristics. This chapter also includ作者: 橡子 時(shí)間: 2025-3-24 12:49 作者: 一罵死割除 時(shí)間: 2025-3-24 18:23 作者: Debrief 時(shí)間: 2025-3-24 22:19
https://doi.org/10.1007/3-540-30950-0e 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作者: 使?jié)M足 時(shí)間: 2025-3-25 03:08
Jochen Seemann,Jürgen Wolff Gudenbergr, a minimum redundancy– and maximum variance–based unsupervised band selection method is presented. Since ranking-based band selection methods are iterative in nature, the huge spatial dimension of the hyperspectral image increases the computation time of the dimensionality reduction (DR) method. H作者: 使增至最大 時(shí)間: 2025-3-25 04:35
Zusammenfassende Darstellung der Ergebnisse, 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作者: 錫箔紙 時(shí)間: 2025-3-25 09:48
https://doi.org/10.1007/978-3-658-15902-3agery (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作者: GULF 時(shí)間: 2025-3-25 15:30 作者: Assignment 時(shí)間: 2025-3-25 18:50 作者: IST 時(shí)間: 2025-3-25 20:33 作者: 遺產(chǎn) 時(shí)間: 2025-3-26 03:29 作者: Meander 時(shí)間: 2025-3-26 06:37
Book 2024. The authors first explain how hyperspectral imagery (HSI) plays an important role in remote sensing due to its high spectral resolution that enables better identification of different materials on?the earth’s?surface. The authors go on to describe potential challenges due to HSI being acquired in 作者: 寬宏大量 時(shí)間: 2025-3-26 12:27 作者: 噴油井 時(shí)間: 2025-3-26 15:22 作者: 輕浮女 時(shí)間: 2025-3-26 17:04
Jochen Seemann,Jürgen Wolff Gudenbergnsidered 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.作者: CAPE 時(shí)間: 2025-3-26 22:39
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.作者: Nibble 時(shí)間: 2025-3-27 01:21
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.作者: GUILE 時(shí)間: 2025-3-27 06:30 作者: acetylcholine 時(shí)間: 2025-3-27 11:53 作者: Inveterate 時(shí)間: 2025-3-27 13:54
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作者: 他很靈活 時(shí)間: 2025-3-27 19:44
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作者: Indelible 時(shí)間: 2025-3-27 23:14 作者: 繁殖 時(shí)間: 2025-3-28 05:54
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作者: 衰弱的心 時(shí)間: 2025-3-28 07:35
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作者: Barter 時(shí)間: 2025-3-28 11:19 作者: 桉樹(shù) 時(shí)間: 2025-3-28 18:36 作者: 野蠻 時(shí)間: 2025-3-28 21:59 作者: recede 時(shí)間: 2025-3-29 01:55
