Morpho-Spectral Recognition of Dense Urban Objects by Hyperspectral Imagery

Gadal, Sébastien; Ouerghemmi, Walid

doi:10.5194/isprsarchives-xl-3-w3-433-2015

Cited by 4 publications

(3 citation statements)

References 22 publications

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“…(3) the biotic variations between vegetation, bare soil or water with, for the same object type, intraclass and interclass variabilities due to species, ageing, height. Further urban morphology is also problematic as buildings and infrastructures exhibit very different shapes and sizes (Gadal et al, 2015; with various volumes inducing an important spatial variability (Heiden, 2012). Rules on urban planning, construction procedures, geographical area affect the city morphology, the vegetal biodiversity and therefore may compromise any processing transfer from one town to another.…”

Section: Imaging Spectroscopy and Urban Areasmentioning

confidence: 99%

Imagerie Hyperspectrale Et Zones Urbaines: Résultats Du Projet HYEP

Weber

Briottet²,

HOUET³

et al. 2022

RFPT

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Le projet HYEP (ANR HYEP 14-CE22-0016-01 HYperspectral imagery for Environmental urban Planning - Programme Mobilité et systèmes urbains - 2014) a permis de confirmer l’intérêt d’une approche globale du milieu urbain par télédétection et notamment en utilisant l’imagerie hyperspectrale (IH). L’intérêt des images hyperspectrales réside dans la gamme d’informations fournies sur des longueurs d’onde de 0,4 à 4 μm ; elles fournissent ainsi un accès aux grandeurs spectrales d'intérêt et aux paramètres chimiques ou biophysiques de la surface. HYEP avait pour objectif de préciser ceci et de proposer un panel de méthodes et de traitements en tenant compte des caractéristiques des autres capteurs existants afin d’en comparer les performances. Les développements réalisés ont été appliqués et évalués sur des images aéroportées hyperspectrales acquises sur Toulouse (France) et Kaunas (Lituania), utilisées également pour synthétiser des systèmes spatiaux : Sentinel-2, Hypxim/Biodiversity et Pleiades. Parmi les verrous identifiés, ceux liés à l'amélioration des capacités spatiales des capteurs et aux changements d’échelle spatiale ont été en partie levés par des approches de fusion, qui se sont avérées concluantes. Des méthodes d’extraction, de fusion ou de classification ont été appliquées à différentes résolutions spatiales afin de qualifier les gains et la complémentarité de l’IH par rapport à d’autres capteurs. Ces évaluations ont ainsi porté sur des « objets » d’intérêt pour les gestionnaires et aménageurs du milieu urbain : les surfaces imperméables, la végétation ou encore la détection de panneaux solaires.

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Section: Imaging Spectroscopy and Urban Areasmentioning

confidence: 99%

Imagerie Hyperspectrale Et Zones Urbaines: Résultats Du Projet HYEP

Weber

Briottet²,

HOUET³

et al. 2022

RFPT

View full text Add to dashboard Cite

show abstract

“…Land cover information is also essential for modelling the dynamics of the urban environment and for the feasible improvement and effective resource and asset management [4]. The urban context is full of complexity because of the heterogeneous and highly fragmented environment [5]. Spatial and spectral information is essential for the recognition of urban objects from the remote sensing perspective.…”

Section: Introductionmentioning

confidence: 99%

Performance of Sentinel-2A Remote Sensing System for Urban Area Mapping in Malaysia via Pixel-Based and OBIA Methods

2021

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Sentinel-2A remote sensing satellite system was recently launched, providing free global remote sensing data similar to Landsat systems. Although the mission enables the acquisition of 10 m spatial resolution global data, the assessment of Sentinel-2A data performance for mapping in Malaysia is still limited. This study aimed to investigate and assess the capability of Sentinel-2A imagery in mapping urban areas in Malaysia by comparing its performance against the established Landsat-8 data as well as the fusion datasets from combining Landsat-8 and Sentinel-2A datasets and using Wavelet transform (WT), Brovey transform (BT) and principal component analysis. Pixel-based and object-based image analysis (OBIA) classification approaches combined with support vector machine (SVM) and decision tree (DT) algorithms were utilized in this assessment, and the accuracy generated was analysed. The Sentinel-2A data provided superior urban mapping output over the use of Landsat-8 alone, and the fusion datasets do not yield advantages for single-scene urban mapping. The highest overall accuracy (OA) for pixel-based classification of Sentinel-2A images is 84.77 % by SVM, followed by 65.27 % using DT. BT produced the highest OA for the fusion images of 78.40 % with SVM and 52.21 % with DT. For the object-based classification of Sentinel-2A images, the highest OA is 71.33 % by SVM, followed by 76.38 % using DT. Similarly, the highest OA of fusion images is obtained by BT of 50.35 % with SVM, followed by 65.66 % with DT. From the analysis, the use of SVM pixel-based classification for medium spatial resolution Sentinel-2A data is effective for urban mapping in Malaysia and useful for future long-term mapping applications. HIGHLIGHTS An accurate mapping of urban land is still challenging and requires high image quality of spectral and spatial aspects to identify features Single and fusion image analysis conducted in order to investigate and assess the most performing interpretation result by grouping out the features classes Statistical performance and image classification comparison is relevant to prove the most effective result among the images GRAPHICAL ABSTRACT

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“…Post-Soviet urban areas like Yakutsk are a mixture of Soviet blocks, post-modern buildings, wooden buildings, and individual houses mostly constructed from wood, where the roofing panels are mostly made of metal, fiber cement, and bitumen. An object-oriented morphometric approach combining the calculation of geometric attributes (e.g., area, elongation, convexity, circularity) for each urban object detected, and morphometric rules enable us to extract object urban sprawl, built-up areas categories, and their associated socioeconomic uses [23,24]. There is a growing interest in morphological image processing due to the increasing availability of high-resolution imagery.…”

Section: Introductionmentioning

confidence: 99%

Multi-Level Morphometric Characterization of Built-up Areas and Change Detection in Siberian Sub-Arctic Urban Area: Yakutsk

Gadal¹,

Ouerghemmi²

2019

IJGI

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Recognition and characterization of built-up areas in the Siberian sub-Arctic urban territories of Yakutsk are dependent on two main factors: (1) the season (snow and ice from October to the end of April, the flooding period in May, and the summertime), which influences the accuracy of urban object detection, and (2) the urban structure, which influences the morphological recognition and characterization of built-up areas. In this study, high repetitiveness remote sensing Sentinel-2A and SPOT 6 high-resolution satellite images were combined to characterize and detect urban built-up areas over the city of Yakutsk. High temporal resolution of Sentinel-2A allows land use change detection and metric spatial resolution of SPOT 6 allows the characterization of built-up areas’ socioeconomic functions and uses.

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Morpho-Spectral Recognition of Dense Urban Objects by Hyperspectral Imagery

Cited by 4 publications

References 22 publications

Imagerie Hyperspectrale Et Zones Urbaines: Résultats Du Projet HYEP

Imagerie Hyperspectrale Et Zones Urbaines: Résultats Du Projet HYEP

Performance of Sentinel-2A Remote Sensing System for Urban Area Mapping in Malaysia via Pixel-Based and OBIA Methods

Multi-Level Morphometric Characterization of Built-up Areas and Change Detection in Siberian Sub-Arctic Urban Area: Yakutsk

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