Spectra of a shallow sea—unmixing for class identification and monitoring of coastal waters

Hommersom, Annelies; Wernand, Marcel Robert; Peters, Steef; Eleveld, M.A.; Woerd, H.J. van der; Boer, Jacob de

doi:10.1007/s10236-010-0373-4

Cited by 13 publications

(8 citation statements)

References 42 publications

(59 reference statements)

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“…Satellite applications of coastal ocean optical typology for monitoring coastal water quality [106] or improving ocean color product inversion [107,108] are also still relatively scarce. A recent study [109] performed from an in situ data set gathered in contrasted coastal waters (i.e.…”

Section: Bio-optical Algorithms: the Classification Approach Vs Regimentioning

confidence: 99%

Challenges and New Advances in Ocean Color Remote Sensing of Coastal Waters

Loisel¹,

Vantrepotte²,

Jamet³

et al. 2013

Topics in Oceanography

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Section: Bio-optical Algorithms: the Classification Approach Vs Regimentioning

confidence: 99%

Challenges and New Advances in Ocean Color Remote Sensing of Coastal Waters

Loisel¹,

Vantrepotte²,

Jamet³

et al. 2013

Topics in Oceanography

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“…The reflection spectrum of each satellite pixel has a certain probability of belonging to each of the 8 clusters. Another classification method that can be tuned to local properties is proposed by Hommersom et al (2011). In this work we go back to use the oldest classification of 21 pre-defined scales and use the relative colour difference (colour comparator scale) instead of absolute remote sensing reflectance to classify each pixel to only one representative FU number.…”

Section: Introductionmentioning

confidence: 99%

MERIS-based ocean colour classification with the discrete Forel–Ule scale

2013

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Abstract. Multispectral information from satellite borne ocean colour sensors is at present used to characterize natural waters via the retrieval of concentrations of the three dominant optical constituents; pigments of phytoplankton, non-algal particles and coloured dissolved organic matter. A limitation of this approach is that accurate retrieval of these constituents requires detailed local knowledge of the specific absorption and scattering properties. In addition, the retrieval algorithms generally use only a limited part of the collected spectral information. In this paper we present an additional new algorithm that has the merit of using the full spectral information in the visible domain to characterize natural waters in a simple and globally valid way. This Forel-Ule MERIS (FUME) algorithm converts the normalized multiband reflectance information into a discrete set of numbers using uniform colourimetric functions. The Forel-Ule (FU) scale is a sea colour comparator scale that has been developed to cover all possible natural sea colours, ranging from indigo blue (the open ocean) to brownish-green (coastal water) and even brown (humic-acid dominated) waters. Data using this scale have been collected since the late nineteenth century, and therefore, this algorithm creates the possibility to compare historic ocean colour data with present-day satellite ocean colour observations. The FUME algorithm was tested by transforming a number of MERIS satellite images into Forel-Ule colour index images and comparing in situ observed FU numbers with FU numbers modelled from in situ radiometer measurements. Similar patterns and FU numbers were observed when comparing MERIS ocean colour distribution maps with ground truth Forel-Ule observations.The FU numbers modelled from in situ radiometer measurements showed a good correlation with observed FU numbers (R 2 = 0.81 when full spectra are used and R 2 = 0.71 when MERIS bands are used).

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“…Thus, in a multi/hyperspectral image, water appears in a darker tone in the IR bands and can be easily differentiated from the dry land surfaces. To date, various water body extraction algorithms for optical imagery have been developed, and they can be categorized into four basic types: 4 (a) thematic classification; [5][6][7][8][9][10][11][12][13][14][15] (b) spectral-unmixing; [16][17][18][19] (c) single-band thresholding; 17,[20][21][22] and (d) the spectral water index methods. [23][24][25][26][27][28][29][30][31][32][33][34] Among these methods, the spectral water index methods are the most commonly used water body extraction methods, because of the ease of use and low computational cost.…”

Section: Introductionmentioning

confidence: 99%

New hyperspectral difference water index for the extraction of urban water bodies by the use of airborne hyperspectral images

Xie

Luo

et al. 2014

J. Appl. Remote Sens

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Abstract. Extracting surface land-cover types and analyzing changes are among the most common applications of remote sensing. One of the most basic tasks is to identify and map surface water boundaries. Spectral water indexes have been successfully used in the extraction of water bodies in multispectral images. However, directly applying a water index method to hyperspectral images disregards the abundant spectral information and involves difficulty in selecting appropriate spectral bands. It is also a challenge for a spectral water index to distinguish water from shadowed regions. The purpose of this study is therefore to develop an index that is suitable for water extraction by the use of hyperspectral images, and with the capability to mitigate the effects of shadow and low-albedo surfaces, especially in urban areas. Thus, we introduce a new hyperspectral difference water index (HDWI) to improve the water classification accuracy in areas that include shadow over water, shadow over other ground surfaces, and low-albedo ground surfaces. We tested the new method using PHI-2, HyMAP, and ROSIS hyperspectral images of Shanghai, Munich, and Pavia. The performance of the water index was compared with the normalized difference water index (NDWI) and the Mahalanobis distance classifier (MDC). With all three test images, the accuracy of HDWI was significantly higher than that of NDWI and MDC. Therefore, HDWI can be used for extracting water with a high degree of accuracy, especially in urban areas, where shadow caused by high buildings is an important source of classification error. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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Spectra of a shallow sea—unmixing for class identification and monitoring of coastal waters

Cited by 13 publications

References 42 publications

Challenges and New Advances in Ocean Color Remote Sensing of Coastal Waters

Challenges and New Advances in Ocean Color Remote Sensing of Coastal Waters

MERIS-based ocean colour classification with the discrete Forel–Ule scale

New hyperspectral difference water index for the extraction of urban water bodies by the use of airborne hyperspectral images

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