Analytical Design of Multispectral Sensors

Wiersma, D. J.; Landgrebe, D. A.

doi:10.1109/tgrs.1980.350271

Cited by 28 publications

(11 citation statements)

References 4 publications

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“…PCA is often used for the dimensionality reduction of HSI [3,[25][26][27][28][29][30]. It is a mathematical orthogonal transformation that changes a set of observations of possibly correlated variables into a set of uncorrelated variables called principal components [31].…”

Section: Dimensionality Reduction Techniquesmentioning

confidence: 99%

Assessment of Component Selection Strategies in Hyperspectral Imagery

Ibarrola-Ulzurrun

Marcello

Gonzalo-Martín

2017

Entropy

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Hyperspectral imagery (HSI) integrates many continuous and narrow bands that cover different regions of the electromagnetic spectrum. However, the main challenge is the high dimensionality of HSI data due to the 'Hughes' phenomenon. Thus, dimensionality reduction is necessary before applying classification algorithms to obtain accurate thematic maps. We focus the study on the following feature-extraction algorithms: Principal Component Analysis (PCA), Minimum Noise Fraction (MNF), and Independent Component Analysis (ICA). After a literature survey, we have observed a lack of a comparative study on these techniques as well as accurate strategies to determine the number of components. Hence, the first objective was to compare traditional dimensionality reduction techniques (PCA, MNF, and ICA) in HSI of the Compact Airborne Spectrographic Imager (CASI) sensor and to evaluate different strategies for selecting the most suitable number of components in the transformed space. The second objective was to determine a new dimensionality reduction approach by dividing the CASI HSI regarding the spectral regions covering the electromagnetic spectrum. The components selected from the transformed space of the different spectral regions were stacked. This stacked transformed space was evaluated to see if the proposed approach improves the final classification.

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Section: Dimensionality Reduction Techniquesmentioning

confidence: 99%

Assessment of Component Selection Strategies in Hyperspectral Imagery

Ibarrola-Ulzurrun

Marcello

Gonzalo-Martín

2017

Entropy

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“…(Jensen and Solberg, 2007) merge adjacent bands decomposing some reference spectra of several classes into piece-wise constant functions. (Wiersma and Landgrebe, 1980) define optimal band subsets using an analytical model considering spectra reconstruction errors. (Serpico and Moser, 2007) propose an adaptation of his Steepest Ascent algorithm to band extraction, also optimizing a JM separability measure.…”

Section: Band Grouping and Band Extractionmentioning

confidence: 99%

Extraction of Optimal Spectral Bands Using Hierarchical Band Merging Out of Hyperspectral Data

Bris¹,

Chehata

Briottet

et al. 2015

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Spectral optimization consists in identifying the most relevant band subset for a specific application. It is a way to reduce hyperspectral data huge dimensionality and can be applied to design specific superspectral sensors dedicated to specific land cover applications. Spectral optimization includes both band selection and band extraction. On the one hand, band selection aims at selecting an optimal band subset (according to a relevance criterion) among the bands of a hyperspectral data set, using automatic feature selection algorithms. On the other hand, band extraction defines the most relevant spectral bands optimizing both their position along the spectrum and their width. The approach presented in this paper first builds a hierarchy of groups of adjacent bands, according to a relevance criterion to decide which adjacent bands must be merged. Then, band selection is performed at the different levels of this hierarchy. Two approaches were proposed to achieve this task : a greedy one and a new adaptation of an incremental feature selection algorithm to this hierarchy of merged bands.

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“…In most related papers, the relationship between the reconstruction pattern and X is identified as RX [11], [12]. This is only true for the conventional spectral sensors.…”

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confidence: 99%

Data Interpretation for Spectral Sensors with Arbitrary Spectral Responses

Wang

Tyo

2006

2006 IEEE International Symposium on Geoscience and Remote Sensing

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Remote Sensing of the Earth's resources from space-based sensors has evolved in the past forty years from a scientific experiment to a commonly used technological tool. The scientific applications and engineering aspects of remote sensing systems have been studied extensively. However, most of these studies have been aimed at spectral sensors with nonoverlapping bands, which means that the spectral responses of different bands have little between-band overlaps. Numerous conventional spectral sensors have dispersive bands which collect independent data in wavelength order [1]. Conventional spectral imaging system models are convenient for analyzing such sensors. Each band is characterized with a center wavelength and a particular bandwidth. In these models, multispectral imaging (MSI) and hyperspectral imaging (HSI) systems are often analyzed in a geometric context [2]. The output photocurrents of different bands at a scene pixel are considered as elements of a p-dimensional vector X, where p is the number of bands available for the particular imager in question. For convenience, feature space Ψ with orthogonal Cartesian coordinates is commonly used to visualize data. This geometrical view can be leveraged to define spectral data as a convex or a conical set, to describe the linear unmixing problem in terms of simplexes [3], to use subspace projection methods for spectral image processing [4], and to define target subspaces for classification [5], among other applications. Although overlaps between spectral responses of different bands are allowed, and really exist in most current MSI or HSI systems, their influences to the output data are not carefully analyzed yet because they are small enough to be ignored.A motivation for studying overlapping bands has arisen with the advent of adaptive simply-configurable sensors based on Quantum-Dot Infrared Photodetectors (QDIPs) in recent years [6], together with the purpose of better understanding of color vision principle of human eyes, the most popular spectral sensors with three bands. For QDIPs, the spectral responses change continuously with the bias voltages applied. A group of typical spectral responses are shown in Fig. 1 (a) [7]. For human eyes, their three bands are spectral responses of three types of retina cones which are shown in 1 (b) [8]. While these sensors can exhibit their spectral imaging abilities as is known, the spectral responses of these instruments are highly overlapping so that the influence of overlaps to the image processing result is significant. The conventional imaging system model cannot be applied to analyze them directly.In this paper, a generalized geometry-based model is provided for spectral sensors with arbitrary spectral responses. Spectral sensors with both overlapping and non-overlapping bands are both covered. This model starts from the mathematical description of the interaction process between sensor and the radiation from scene reaching the sensor. The radiation power is a function of wavelength. If it is considered as pattern...

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Analytical Design of Multispectral Sensors

Cited by 28 publications

References 4 publications

Assessment of Component Selection Strategies in Hyperspectral Imagery

Assessment of Component Selection Strategies in Hyperspectral Imagery

Extraction of Optimal Spectral Bands Using Hierarchical Band Merging Out of Hyperspectral Data

Data Interpretation for Spectral Sensors with Arbitrary Spectral Responses

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