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Hyperspectral Remote Sensing

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A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: closed (31 October 2011) | Viewed by 62918

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Prof. Dr. Eyal Ben-Dor

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Remote Sensing Laboratory, Geography Department, Porter School of the Environment and Earth Sciences, Faculty of Exact Sciences, Tel Aviv University, Tel Aviv 699780, Israel
Interests: imaging spectroscopy of soil; spectral libraries; soil proxy; hyperspectral RS of soils rocks and minerals; sensor calibration
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Dear Colleagues,

Hyperspectral remote sensing (HRS), or imaging spectroscopy (IS), is a technology that can provide detailed spectral information from every pixel in an image. Whereas HRS refers mostly to remote-sensing means (usually from far distances), the emerging IS technology covers all spatial-spectral domains, from microscopic to telescopic. In general, being a technology that provides spatial and spectral information simultaneously, HRS-IS improves our understanding of the remote environment. It enables accurate identification of both targets and phenomena as the spectral information is presented on a spatial rather than point (pixel) basis. Furthermore, it provides a new capability—to quantitatively assess chemical and physical aspects of the pixel(s) in question. The IS-HRS technology is well accepted in the remote-sensing arena as an innovative tool for many applications, such as in geology, ecology, soil, limnology, pedology, plant biology and atmospheric sciences, especially for cases in which other remote-sensing means have failed or are incapable of obtaining additional information. Whereas innovative approaches have been developed over the past 10 years, mostly by scientists, the power of the IS-HRS technology is still unknown to many potential end-users, such as decision-makers, farmers, environmental watchers in both the private and governmental sectors, city planners, stock holders and others. This is mainly because the use of HRS-IS sensors still relies on the relatively high cost of its final products and on the need for professional manpower to operate the instrument and process the data. Nonetheless, today, in addition to the growing number of scientific papers and conferences focusing on this technology, the HRS-IS discipline is very active: commercial sensors are being built and sold, orbital sensors are in advanced planning phases, people are becoming more educated on the topic, national and international funds are being directed toward studying and using this technology from all domains (ground air and space) and interest from the private sector is on the rise. The aim of this special issue is to gather innovative papers dealing with this technology from all aspects giving special emphasis to remote sensing of the Earth.

Prof. Dr. Eyal Ben-Dor
Guest Editor

Keywords

imaging spectroscopy
hyperspectral remote sensing
data analysis
applications
data fusion
Cal/Val
commercialization
existing and future development
new sensors
reflectance and emittance spectroscopy

Published Papers (6 papers)

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Research

3184 KiB

Open AccessArticle

Two Linear Unmixing Algorithms to Recognize Targets Using Supervised Classification and Orthogonal Rotation in Airborne Hyperspectral Images

by Amir Averbuch and Michael Zheludev

Remote Sens. 2012, 4(2), 532-560; https://doi.org/10.3390/rs4020532 - 21 Feb 2012

Cited by 13 | Viewed by 7706

Abstract

The goal of the paper is to detect pixels that contain targets of known spectra. The target can be present in a sub- or above pixel. Pixels without targets are classified as background pixels. Each pixel is treated via the content of its neighborhood. A pixel whose spectrum is different from its neighborhood is classified as a “suspicious point”. In each suspicious point there is a mix of target(s) and background. The main objective in a supervised detection (also called “target detection”) is to search for a specific given spectral material (target) in hyperspectral imaging (HSI) where the spectral signature of the target is known a priori from laboratory measurements. In addition, the fractional abundance of the target is computed. To achieve this we present two linear unmixing algorithms that recognize targets with known (given) spectral signatures. The CLUN is based on automatic feature extraction from the target’s spectrum. These features separate the target from the background. The ROTU algorithm is based on embedding the spectra space into a special space by random orthogonal transformation and on the statistical properties of the embedded result. Experimental results demonstrate that the targets’ locations were extracted correctly and these algorithms are robust and efficient. Full article

(This article belongs to the Special Issue Hyperspectral Remote Sensing)

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856 KiB

Open AccessArticle

Use of Variogram Parameters in Analysis of Hyperspectral Imaging Data Acquired from Dual-Stressed Crop Leaves

by Christian Nansen

Remote Sens. 2012, 4(1), 180-193; https://doi.org/10.3390/rs4010180 - 11 Jan 2012

Cited by 23 | Viewed by 8496

Abstract

A detailed introduction to variogram analysis of reflectance data is provided, and variogram parameters (nugget, sill, and range values) were examined as possible indicators of abiotic (irrigation regime) and biotic (spider mite infestation) stressors. Reflectance data was acquired from 2 maize hybrids (Zea mays L.) at multiple time points in 2 data sets (229 hyperspectral images), and data from 160 individual spectral bands in the spectrum from 405 to 907 nm were analyzed. Based on 480 analyses of variance (160 spectral bands × 3 variogram parameters), it was seen that most of the combinations of spectral bands and variogram parameters were unsuitable as stress indicators mainly because of significant difference between the 2 data sets. However, several combinations of spectral bands and variogram parameters (especially nugget values) could be considered unique indicators of either abiotic or biotic stress. Furthermore, nugget values at 683 and 775 nm responded significantly to abiotic stress, and nugget values at 731 nm and range values at 715 nm responded significantly to biotic stress. Based on qualitative characterization of actual hyperspectral images, it was seen that even subtle changes in spatial patterns of reflectance values can elicit several-fold changes in variogram parameters despite non-significant changes in average and median reflectance values and in width of 95% confidence limits. Such scattered stress expression is in accordance with documented within-leaf variation in both mineral content and chlorophyll concentration and therefore supports the need for reflectance-based stress detection at a high spatial resolution (many hyperspectral reflectance profiles acquired from a single leaf) and may be used to explain or characterize within-leaf foraging patterns of herbivorous arthropods. Full article

(This article belongs to the Special Issue Hyperspectral Remote Sensing)

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1441 KiB

Open AccessArticle

Hyperspectral Data for Mangrove Species Mapping: A Comparison of Pixel-Based and Object-Based Approach

by Muhammad Kamal and Stuart Phinn

Remote Sens. 2011, 3(10), 2222-2242; https://doi.org/10.3390/rs3102222 - 20 Oct 2011

Cited by 164 | Viewed by 15498

Abstract

Visual image interpretation and digital image classification have been used to map and monitor mangrove extent and composition for decades. The presence of a high-spatial resolution hyperspectral sensor can potentially improve our ability to differentiate mangrove species. However, little research has explored the use of pixel-based and object-based approaches on high-spatial hyperspectral datasets for this purpose. This study assessed the ability of CASI-2 data for mangrove species mapping using pixel-based and object-based approaches at the mouth of the Brisbane River area, southeast Queensland, Australia. Three mapping techniques used in this study: spectral angle mapper (SAM) and linear spectral unmixing (LSU) for the pixel-based approaches, and multi-scale segmentation for the object-based image analysis (OBIA). The endmembers for the pixel-based approach were collected based on existing vegetation community map. Nine targeted classes were mapped in the study area from each approach, including three mangrove species: Avicennia marina, Rhizophora stylosa, and Ceriops australis. The mapping results showed that SAM produced accurate class polygons with only few unclassified pixels (overall accuracy 69%, Kappa 0.57), the LSU resulted in a patchy polygon pattern with many unclassified pixels (overall accuracy 56%, Kappa 0.41), and the object-based mapping produced the most accurate results (overall accuracy 76%, Kappa 0.67). Our results demonstrated that the object-based approach, which combined a rule-based and nearest-neighbor classification method, was the best classifier to map mangrove species and its adjacent environments. Full article

(This article belongs to the Special Issue Hyperspectral Remote Sensing)

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1237 KiB

Open AccessArticle

Monitoring the Extent of Contamination from Acid Mine Drainage in the Iberian Pyrite Belt (SW Spain) Using Hyperspectral Imagery

by Asuncion Riaza, Jorge Buzzi, Eduardo García-Meléndez, Veronique Carrère and Andreas Müller

Remote Sens. 2011, 3(10), 2166-2186; https://doi.org/10.3390/rs3102166 - 14 Oct 2011

Cited by 31 | Viewed by 10107

Abstract

Monitoring mine waste from sulfide deposits by hyperspectral remote sensing can be used to predict surface water quality by quantitatively estimating acid drainage and metal contamination on a yearly basis. In addition, analysis of the mineralogy of surface crusts rich in soluble salts can provide a record of annual humidity and temperature. In fact, temporal monitoring of salt efflorescence from mine wastes at a mine site in the Iberian Pyrite Belt (Huelva, Spain) has been achieved using hyperspectral airborne Hymap data. Furthermore, climate variability estimates are possible based on oxidation stages derived from well-known sequences of minerals, by tracing sulfide oxidation intensity using archive spectral libraries. Thus, airborne and spaceborne hyperspectral remote sensing data can be used to provide a short-term record of climate change, and represent a useful set of tools for assessing environmental geoindicators in semi-arid areas. Spectral and geomorphological indicators can be monitored on a regular basis through image processing, supported by field and laboratory spectral data. In fact, hyperspectral image analysis is one of the methods selected by the Joint Research Centre of the European Community (Ispra, Italy) to study abandoned mine sites, in order to assess the enforcement of the European Mine Waste Directive (2006/21/EC of the European Parliament and of the Council 15 March 2006) on the management of waste from extractive industries (Official Journal of the European Union, 11 April 2006). The pyrite belt in Andalucia has been selected as one of the core mission test sites for the PECOMINES II program (Cracow, November 2005), using imaging spectroscopy; and this technique is expected to be implemented as a monitoring tool by the Environmental Net of Andalucía (REDIAM, Junta de Andalucía, Spain). Full article

(This article belongs to the Special Issue Hyperspectral Remote Sensing)

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2794 KiB

Open AccessArticle

AMARTIS v2: 3D Radiative Transfer Code in the [0.4; 2.5 µm] Spectral Domain Dedicated to Urban Areas

by Colin Thomas, Stéphanie Doz, Xavier Briottet and Sophie Lachérade

Remote Sens. 2011, 3(9), 1914-1942; https://doi.org/10.3390/rs3091914 - 31 Aug 2011

Cited by 13 | Viewed by 7076

Abstract

The availability of new very high spatial resolution sensors has for the past few years allowed a precise description of urban areas, and thus the settlement of specific ground or atmosphere characterization methods. However, in order to develop such techniques, a radiative transfer tool dedicated to such an area is necessary. AMARTIS v2 is a new radiative transfer code derived from the radiative transfer code AMARTIS specifically dedicated to urban areas. It allows to simulate airborne and spaceborne multiangular observations of 3D scenes in the [0.4; 2.5µm] domain with the ground’s geometry, urban materials optical properties, atmospheric modeling and sensor characteristics entirely defined by the user. After a general presentation of AMARTIS v2 and a description of the performed calculations, results of radiometric intercomparisons with other radiative transfer codes are presented and the new offered potentials are illustrated with four realistic examples, representative of current issues in urban areas remote sensing. Full article

(This article belongs to the Special Issue Hyperspectral Remote Sensing)

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1861 KiB

Open AccessArticle

Effect of Reduced Spatial Resolution on Mineral Mapping Using Imaging Spectrometry—Examples Using Hyperspectral Infrared Imager (HyspIRI)-Simulated Data

by Fred A. Kruse, James V. Taranik, Mark Coolbaugh, Joshua Michaels, Elizabeth F. Littlefield, Wendy M. Calvin and Brigette A. Martini

Remote Sens. 2011, 3(8), 1584-1602; https://doi.org/10.3390/rs3081584 - 25 Jul 2011

Cited by 34 | Viewed by 11804

Abstract

The Hyperspectral Infrared Imager (HyspIRI) is a proposed NASA satellite remote sensing system combining a visible to shortwave infrared (VSWIR) imaging spectrometer with over 200 spectral bands between 0.38 and 2.5 μm and an 8-band thermal infrared (TIR) multispectral imager, both at 60 m spatial resolution. Short Wave Infrared (SWIR) (2.0–2.5 μm) simulation results are described here using Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data in preparation for the future launch. The simulated data were used to assess the effect of the HyspIRI 60 m spatial resolution on the ability to identify and map minerals at hydrothermally altered and geothermal areas. Mineral maps produced using these data successfully detected and mapped a wide variety of characteristic minerals, including jarosite, alunite, kaolinite, dickite, muscovite-illite, montmorillonite, pyrophyllite, calcite, buddingtonite, and hydrothermal silica. Confusion matrix analysis of the datasets showed overall classification accuracy ranging from 70 to 92% for the 60 m HyspIRI simulated data relative to 15 m spatial resolution data. Classification accuracy was lower for similar minerals and smaller areas, which were not mapped well by the simulated 60 m HyspIRI data due to blending of similar signatures and spectral mixing with adjacent pixels. The simulations demonstrate that HyspIRI SWIR data, while somewhat limited by their relatively coarse spatial resolution, should still be useful for mapping hydrothermal/geothermal systems, and for many other geologic applications requiring mineral mapping. Full article

(This article belongs to the Special Issue Hyperspectral Remote Sensing)

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