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Hyperspectral Imagery for Urban Environment

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Urban Remote Sensing".

Deadline for manuscript submissions: closed (31 July 2019) | Viewed by 26937

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Special Issue Editors

Dr. Christiane Weber

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Guest Editor

DR CNRS, TETIS Research Unit, AgroParisTech, CIRAD, CNRS, Irstea, Maison de la Télédétection, 500 rue Jean-François Breton, 34000 Montpellier, France
Interests: urban environment; urban multifunctionality; remote sensing
Special Issues, Collections and Topics in MDPI journals

Dr. Xavier Briottet

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Guest Editor

Optics and Associated Techniques Department, ONERA, 2 Avenue Edouard Belin, 31005 Toulouse, France
Interests: high-spatial-resolution sensor; remote sensing signal and image processing; urban environment
Special Issues, Collections and Topics in MDPI journals

Dr. Clement Mallet

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Guest Editor

Univ. Paris-Est, LASTIG MATIS, IGN, ENSG, 73 avenue de Paris, F-94160 Saint-Mandé, France
Interests: satellite image processing and classification; urban area

Special Issue Information

Dear Colleagues,

Urban areas face many recent and, most likely, future challenges and require monitoring and mapping. Urban growth steers urban planning decisions, with new projects adding new urban surfaces, volumes and forms and consequences, such as urban heat island, runoff water management challenges, and high degrees of sealed surfaces. Urban sprawl leads to disruptions in landscape, fragmentation of natural habitats, biodiversity erosion, loss of agricultural or forestry resources, and multifunctionality of natural areas. Urban areas studies require strong cross-disciplinary relationships in order to provide relevant information for decision making or for territorial prospective modelling.

To face these challenges, it is mandatory to gather the most efficient information allowing the considering of LU/LC evolution, biodiversity erosion, urban environment elements… under demographic and socioeconomic pressures over decades. Hyperspectral airborne sensors open the way to an innovative carrier for discrimination of man-made materials or to monitoring of vegetation biodiversity.

Due to the specificity of urban areas, the estimation of accurate physical properties dedicated to end-user applications require new sensors developments by taking into account the 3D shape of the city, shadow effects in atmospheric correction, geo-referencing, optical properties and surface temperature retrieval, multitemporal analysis, urban land cover mapping and its monitoring. Further, this range of remote sensing techniques enables the user to extract complementary information thus improving our understanding of complex urban structures. As one of many consequences data fusion methods need to be extended to account for multiscale images, cross-sensor fusion, spectral unmixing, bottom-up and top-down data integration methods beyond including RS-GIS integrated methods.

We particularly seek for contributions of recent methodological and theoretical developments gathering cross-disciplinary visions able to cope with the challenges related to the “21st urban century”.

Dr. Christiane Weber
Dr. Xavier Briottet
Dr. Clement Mallet
Guest Editors

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Keywords

Hyperspectral sensors
Urban environment
Big data
Methodological enhancement

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Published Papers (6 papers)

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Research

19 pages, 3739 KiB

Open AccessArticle

TCANet for Domain Adaptation of Hyperspectral Images

by Alberto S. Garea, Dora B. Heras and Francisco Argüello

Remote Sens. 2019, 11(19), 2289; https://doi.org/10.3390/rs11192289 - 30 Sep 2019

Cited by 6 | Viewed by 3231

Abstract

The use of Convolutional Neural Networks (CNNs) to solve Domain Adaptation (DA) image classification problems in the context of remote sensing has proven to provide good results but at high computational cost. To avoid this problem, a deep learning network for DA in remote sensing hyperspectral images called TCANet is proposed. As a standard CNN, TCANet consists of several stages built based on convolutional filters that operate on patches of the hyperspectral image. Unlike the former, the coefficients of the filter are obtained through Transfer Component Analysis (TCA). This approach has two advantages: firstly, TCANet does not require training based on backpropagation, since TCA is itself a learning method that obtains the filter coefficients directly from the input data. Second, DA is performed on the fly since TCA, in addition to performing dimensional reduction, obtains components that minimize the difference in distributions of data in the different domains corresponding to the source and target images. To build an operating scheme, TCANet includes an initial stage that exploits the spatial information by providing patches around each sample as input data to the network. An output stage performing feature extraction that introduces sufficient invariance and robustness in the final features is also included. Since TCA is sensitive to normalization, to reduce the difference between source and target domains, a previous unsupervised domain shift minimization algorithm consisting of applying conditional correlation alignment (CCA) is conditionally applied. The results of a classification scheme based on CCA and TCANet show that the DA technique proposed outperforms other more complex DA techniques. Full article

(This article belongs to the Special Issue Hyperspectral Imagery for Urban Environment)

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22 pages, 7217 KiB

Open AccessArticle

Partial Linear NMF-Based Unmixing Methods for Detection and Area Estimation of Photovoltaic Panels in Urban Hyperspectral Remote Sensing Data

by Moussa Sofiane Karoui, Fatima Zohra Benhalouche, Yannick Deville, Khelifa Djerriri, Xavier Briottet, Thomas Houet, Arnaud Le Bris and Christiane Weber

Remote Sens. 2019, 11(18), 2164; https://doi.org/10.3390/rs11182164 - 17 Sep 2019

Cited by 41 | Viewed by 4377

Abstract

High-spectral-resolution hyperspectral data are acquired by sensors that gather images from hundreds of narrow and contiguous bands of the electromagnetic spectrum. These data offer unique opportunities for characterization and precise land surface recognition in urban areas. So far, few studies have been conducted with these data to automatically detect and estimate areas of photovoltaic panels, which currently constitute an important part of renewable energy systems in urban areas of developed countries. In this paper, two hyperspectral-unmixing-based methods are proposed to detect and to estimate surfaces of photovoltaic panels. These approaches, related to linear spectral unmixing (LSU) techniques, are based on new nonnegative matrix factorization (NMF) algorithms that exploit known panel spectra, which makes them partial NMF methods. The first approach, called Grd-Part-NMF, is a gradient-based method, whereas the second one, called Multi-Part-NMF, uses multiplicative update rules. To evaluate the performance of these approaches, experiments are conducted on realistic synthetic and real airborne hyperspectral data acquired over an urban region. For the synthetic data, obtained results show that the proposed methods yield much better overall performance than NMF-unmixing-based methods from the literature. For the real data, the obtained detection and area estimation results are first confirmed by using very high-spatial-resolution ortho-images of the same regions. These results are also compared with those obtained by standard NMF-unmixing-based methods and by a one-class-classification-based approach. This comparison shows that the proposed approaches are superior to those considered from the literature. Full article

(This article belongs to the Special Issue Hyperspectral Imagery for Urban Environment)

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28 pages, 16647 KiB

Open AccessArticle

KLUM: An Urban VNIR and SWIR Spectral Library Consisting of Building Materials

by Rebecca Ilehag, Andreas Schenk, Yilin Huang and Stefan Hinz

Remote Sens. 2019, 11(18), 2149; https://doi.org/10.3390/rs11182149 - 15 Sep 2019

Cited by 22 | Viewed by 5730

Abstract

Knowledge about the existing materials in urban areas has, in recent times, increased in importance. With the use of imaging spectroscopy and hyperspectral remote sensing techniques, it is possible to measure and collect the spectra of urban materials. Most spectral libraries consist of either spectra acquired indoors in a controlled lab environment or of spectra from afar using airborne systems accompanied with in situ measurements. Furthermore, most publicly available spectral libraries have, so far, not focused on facade materials but on roofing materials, roads, and pavements. In this study, we present an urban spectral library consisting of collected in situ material spectra with imaging spectroscopy techniques in the visible and near-infrared (VNIR) and short-wave infrared (SWIR) spectral range, with particular focus on facade materials and material variation. The spectral library consists of building materials, such as facade and roofing materials, in addition to surrounding ground material, but with a focus on facades. This novelty is beneficial to the community as there is a shift to oblique-viewed Unmanned Aerial Vehicle (UAV)-based remote sensing and thus, there is a need for new types of spectral libraries. The post-processing consists partly of an intra-set solar irradiance correction and recalculation of reference spectra caused by signal clipping. Furthermore, the clustering of the acquired spectra was performed and evaluated using spectral measures, including Spectral Angle and a modified Spectral Gradient Angle. To confirm and compare the material classes, we used samples from publicly available spectral libraries. The final material classification scheme is based on a hierarchy with subclasses, which enables a spectral library with a larger material variation and offers the possibility to perform a more refined material analysis. The analysis reveals that the color and the surface structure, texture or coating of a material plays a significantly larger role than what has been presented so far. The samples and their corresponding detailed metadata can be found in the Karlsruhe Library of Urban Materials (KLUM) archive. Full article

(This article belongs to the Special Issue Hyperspectral Imagery for Urban Environment)

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19 pages, 6081 KiB

Open AccessArticle

Comparison of Hyperspectral Techniques for Urban Tree Diversity Classification

by Charlotte Brabant, Emilien Alvarez-Vanhard, Achour Laribi, Gwénaël Morin, Kim Thanh Nguyen, Alban Thomas and Thomas Houet

Remote Sens. 2019, 11(11), 1269; https://doi.org/10.3390/rs11111269 - 28 May 2019

Cited by 15 | Viewed by 4923

Abstract

This research aims to assess the capabilities of Very High Spatial Resolution (VHSR) hyperspectral satellite data in order to discriminate urban tree diversity. Four dimension reduction methods and two classifiers are tested, using two learning methods and applied with four in situ sample datasets. An airborne HySpex image (408 bands/2 m) was acquired in July 2015 from which prototypal spaceborne hyperspectral images (named HYPXIM) at 4 m and 8 m and a multispectral Sentinel2 image at 10 m have been simulated for the purpose of this study. A comparison is made using these methods and datasets. The influence of dimension reduction methods is assessed on hyperspectral (HySpex and HYPXIM) and Sentinel2 datasets. The influence of conventional classifiers (Support Vector Machine –SVM– and Random Forest –RF–) and learning methods is evaluated on all image datasets (reduced and non-reduced hyperspectral and Sentinel2 datasets). Results show that HYPXIM 4 m and HySpex 2 m reduced by Minimum Noise Fraction (MNF) provide the greatest classification of 14 species using the SVM with an overall accuracy of 78.4% (±1.5) and a kappa index of agreement of 0.7. More generally, the learning methods have a stronger influence than classifiers, or even than dimensional reduction methods, on urban tree diversity classification. Prototypal HYPXIM images appear to present a great compromise (192 spectral bands/4 m resolution) for urban vegetation applications compared to HySpex or Sentinel2 images. Full article

(This article belongs to the Special Issue Hyperspectral Imagery for Urban Environment)

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19 pages, 3978 KiB

Open AccessArticle

Extraction of Urban Objects in Cloud Shadows on the basis of Fusion of Airborne LiDAR and Hyperspectral Data

by Qixia Man and Pinliang Dong

Remote Sens. 2019, 11(6), 713; https://doi.org/10.3390/rs11060713 - 25 Mar 2019

Cited by 5 | Viewed by 3606

Abstract

Feature extraction in cloud shadows is a difficult problem in the field of optical remote sensing. The key to solving this problem is to improve the accuracy of classification algorithms by fusing multi-source remotely sensed data. Hyperspectral data have rich spectral information but highly suffer from cloud shadows, whereas light detection and ranging (LiDAR) data can be acquired from beneath clouds to provide accurate height information. In this study, fused airborne LiDAR and hyperspectral data were used to extract urban objects in cloud shadows using the following steps: (1) a series of LiDAR and hyperspectral metrics were extracted and selected; (2) cloud shadows were extracted; (3) the new proposed approach was used by combining a pixel-based support vector machine (SVM) and object-based classifiers to extract urban objects in cloud shadows; (4) a pixel-based SVM classifier was used for the classification of the whole study area with the selected metrics; (5) a decision-fusion strategy was employed to get the final results for the whole study area; (6) accuracy assessment was conducted. Compared with the SVM classification results, the decision-fusion results of the combined SVM and object-based classifiers show that the overall classification accuracy is improved by 5.00% (from 87.30% to 92.30%). The experimental results confirm that the proposed method is very effective for urban object extraction in cloud shadows and thus improve urban applications such as urban green land management, land use analysis, and impervious surface assessment. Full article

(This article belongs to the Special Issue Hyperspectral Imagery for Urban Environment)

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24 pages, 6193 KiB

Open AccessArticle

An Improved Model Based Detection of Urban Impervious Surfaces Using Multiple Features Extracted from ROSIS-3 Hyperspectral Images

by Yuliang Wang, Huiyi Su and Mingshi Li

Remote Sens. 2019, 11(2), 136; https://doi.org/10.3390/rs11020136 - 11 Jan 2019

Cited by 9 | Viewed by 3299

Abstract

Hyperspectral images (HSIs) provide unique capabilities for urban impervious surfaces (UIS) extraction. This paper proposes a multi-feature extraction model (MFEM) for UIS detection from HSIs. The model is based on a nonlinear dimensionality reduction technique, t-distributed stochastic neighbor embedding (t-SNE), and the deep learning method convolutional deep belief networks (CDBNs). We improved the two methods to create a novel MFEM consisting of improved t-SNE, deep compression CDBNs (d-CDBNs), and a logistic regression classifier. The improved t-SNE method provides dimensionality reduction and spectral feature extraction from the original HSIs and the d-CDBNs algorithm extracts spatial feature and edges using the reduced dimensional datasets. Finally, the extracted features are combined into multi-feature for the impervious surface detection using the logistic regression classifier. After comparing with the commonly used methods, the current experimental results demonstrate that the proposed MFEM model provides better performance for UIS extraction and detection from HSIs. Full article

(This article belongs to the Special Issue Hyperspectral Imagery for Urban Environment)

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