Spectral variability and bidirectional reflectance behaviour of urban materials at a 20 cm spatial resolution in the visible and near‐infrared wavelengths. A case study over Toulouse (France)

Lachérade, Sophie; Miesch, Christophe; Briottet, Xavier; Men, Hervé Le

doi:10.1080/01431160500177414

Cited by 23 publications

(17 citation statements)

References 5 publications

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“…The representation of urban surface materials in hyperspectral image data is also characterized by a high within-class variability (Lacherade et al, 2005;Heiden et al, 2005). This is due to several factors, such as color, coating, degradation and illumination of the material as well as preprocessing of the image data.…”

Section: Introductionmentioning

confidence: 99%

Determination of robust spectral features for identification of urban surface materials in hyperspectral remote sensing data

Heiden

Segl

Roessner

et al. 2007

Remote Sensing of Environment

180

115

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Hyperspectral remote sensing data open up new opportunities for analyzing urban areas characterized by a large variety of spectrally distinct surface materials. Spectroscopic analysis using diagnostic spectral features yields the potential for automated identification and mapping of these materials. This study proposes a new approach for the determination and evaluation of such spectral features that are robust against spectral overlap between material classes and within class variability. Analysis is based on comprehensive field and image spectral libraries of more than 21.000 spectra of surface materials widely-used in German cities. The robustness of the interactively defined spectral features is evaluated by a separability analysis. This method is performed based on confusion matrices for each material computed from classification results. For comparison this analysis is also performed for material-specific gray values of selected bands. The obtained commission and omission errors show superiority of the spectral features compared to gray values for most of the investigated materials. The results indicate that robust spectral features yield the potential for unsupervised detection of endmembers in hyperspectral image data.

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Section: Introductionmentioning

confidence: 99%

Determination of robust spectral features for identification of urban surface materials in hyperspectral remote sensing data

Heiden

Segl

Roessner

et al. 2007

Remote Sensing of Environment

180

115

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“…Therefore, Rogge et al [140] integrate spatial-spectral information into a fully automated EM extraction tool and have proven to detect EM in the context of geological applications. Although images of urban areas comprise a high number of spectrally distinct endmembers [4], they are additionally characterized by a large within-class variability [4,118] as the result of aging, BRDF effects, and shadow as described above. In order to deal with the per pixel EM variability, the Multiple Endmember Spectral Mixture Analysis (MESMA) was developed to allow the types and number of EM to vary for each pixel [141].…”

Section: Hyperspectral Approachesmentioning

confidence: 99%

“…Additionally, the Sun illumination angle, the orientation of the urban objects, and the viewing geometry of the sensor can have significant effects on the spectral signature. This is described by the bidirectional reflectance distribution function (BRDF) [118][119][120]. Another major factor is shadow, which reduces the albedo of the surface material and thus, can hide the spectral reflectance characteristics making the detection of the material challenging.…”

Section: Hyperspectral Approachesmentioning

confidence: 99%

Can the Future EnMAP Mission Contribute to Urban Applications? A Literature Survey

et al. 2011

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With urban populations and their footprints growing globally, the need to assess the dynamics of the urban environment increases. Remote sensing is one approach that can analyze these developments quantitatively with respect to spatially and temporally large scale changes. With the 2015 launch of the spaceborne EnMAP mission, a new hyperspectral sensor with high signal-to-noise ratio at medium spatial resolution, and a 21 day global revisit capability will become available. This paper presents the results of a literature survey on existing applications and image analysis techniques in the context of urban remote sensing in order to identify and outline potential contributions of the future EnMAP mission. Regarding urban applications, four frequently addressed topics have been identified: urban development and planning, urban growth assessment, risk and vulnerability assessment and urban climate. The requirements of four application fields and associated image processing techniques used to retrieve desired parameters and create geo-information products have been reviewed. As a result, we identified promising research directions enabling the use of EnMAP for urban studies. First and foremost, research is required to analyze the spectral information content of an EnMAP pixel used to support material-based land cover mapping approaches. This information can subsequently be used to improve urban indicators, such as imperviousness. Second, we identified the global monitoring of urban areas as a promising field of investigation taking advantage of EnMAP's spatial coverage and revisit capability.Remote Sens. 2011, 3 1818However, owing to the limitations of EnMAPs spatial resolution for urban applications, research should also focus on hyperspectral resolution enhancement to enable retrieving material information on sub-pixel level.

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“…Since no existing map contains such information, airborne remote sensing techniques appear to be convenient for obtaining such a map at a large scale. However, remote sensing of urban environments from airborne acquisitions namely still remains a major issue, since on one hand, urban areas are characterised by a high variety of materials which can appear very similar on images, and on the other hand, by a strong intra-class variability due for instance to material aging and uses (Lacherade et al, 2005). Thus results provided by most traditional processing methods based on usual red-green-blue-near infrared multispectral images remain limited for such applications.…”

Section: Some Needs For Urban Materials Mapsmentioning

confidence: 99%

“…For each generated synthetic spectrum, the multiplicative factor was randomly selected between 0.8 and 1.2, according to the standard deviations of the classes for which a sufficient amount of spectra was available. Finer quantitative analyses are available in (Lacherade et al, 2005).…”

Section: Generate New Synthetic Spectra From the Data Basementioning

confidence: 99%

Spectral Band Selection for Urban Material Classification Using Hyperspectral Libraries

Bris¹,

Laroui²,

Briottet³

et al. 2016

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:In urban areas, information concerning very high resolution land cover and especially material maps are necessary for several city modelling or monitoring applications. That is to say, knowledge concerning the roofing materials or the different kinds of ground areas is required. Airborne remote sensing techniques appear to be convenient for providing such information at a large scale. However, results obtained using most traditional processing methods based on usual red-green-blue-near infrared multispectral images remain limited for such applications. A possible way to improve classification results is to enhance the imagery spectral resolution using superspectral or hyperspectral sensors. In this study, it is intended to design a superspectral sensor dedicated to urban materials classification and this work particularly focused on the selection of the optimal spectral band subsets for such sensor. First, reflectance spectral signatures of urban materials were collected from 7 spectral libraires. Then, spectral optimization was performed using this data set. The band selection workflow included two steps, optimising first the number of spectral bands using an incremental method and then examining several possible optimised band subsets using a stochastic algorithm. The same wrapper relevance criterion relying on a confidence measure of Random Forests classifier was used at both steps. To cope with the limited number of available spectra for several classes, additional synthetic spectra were generated from the collection of reference spectra: intra-class variability was simulated by multiplying reference spectra by a random coefficient. At the end, selected band subsets were evaluated considering the classification quality reached using a rbf svm classifier. It was confirmed that a limited band subset was sufficient to classify common urban materials. The important contribution of bands from the Short Wave Infra-Red (SWIR) spectral domain (1000 -2400 nm) to material classification was also shown.

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Spectral variability and bidirectional reflectance behaviour of urban materials at a 20 cm spatial resolution in the visible and near‐infrared wavelengths. A case study over Toulouse (France)

Cited by 23 publications

References 5 publications

Determination of robust spectral features for identification of urban surface materials in hyperspectral remote sensing data

Determination of robust spectral features for identification of urban surface materials in hyperspectral remote sensing data

Can the Future EnMAP Mission Contribute to Urban Applications? A Literature Survey

Spectral Band Selection for Urban Material Classification Using Hyperspectral Libraries

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