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An improved approach based on terrain-dependent mathematical models for georeferencing pushbroom satellite images / Behrooz Moradi in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 1 (January 2021)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 87 n° 1 (January 2021) . - pp 53 - 69 Titre : An improved approach based on terrain-dependent mathematical models for georeferencing pushbroom satellite images Type de document : Article/Communication Auteurs : Behrooz Moradi, Auteur ; Mohammad Javad Valadan Zoej, Auteur ; Sayad Yaghoobi, Auteur ; Somayeh Yavari, Auteur Année de publication : 2021 Article en page(s) : pp 53 - 69 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] géoréférencement [Termes descripteurs IGN] image à haute résolution [Termes descripteurs IGN] image Geoeye [Termes descripteurs IGN] image Ikonos [Termes descripteurs IGN] Iran [Termes descripteurs IGN] modèle géométrique de prise de vue [Termes descripteurs IGN] modèle par fonctions rationnelles [Termes descripteurs IGN] modélisation 3D Résumé : (Auteur) Recently, linear features in remotely sensed imagery have gained much attention because of their unique characteristics compared to other control features. For georeferencing high-resolution satellite images, the observations in the mathematical equations (slope and y-intercept) of the corresponding control lines in the two spaces are considered the same based on recent studies. However, the use of such assumptions causes error and reduces the accuracy of registration. The aim of this article is to present a methodology based on a quasi-observation assumption in the mathematical equations in the process of georeferencing. Experimental results for IKONOS and GeoEye images over two different cities of Iran indicate that the quasi-observation assumption can increase the average registration accuracy up to 48.96% and 24.77% using 3D-affine and rational function models, respectively. This improvement in accuracy increases the processing time by 31.48% over traditional approaches; however, the proposed methodology can be regarded as an efficient solution. Numéro de notice : A2021-057 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.87.1.53 date de publication en ligne : 01/01/2021 En ligne : https://doi.org/10.14358/PERS.87.1.53 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96768 [article]

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A review of image fusion techniques for pan-sharpening of high-resolution satellite imagery / Farzaneh Dadrass Javan in ISPRS Journal of photogrammetry and remote sensing, vol 171 (January 2021)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 171 (January 2021) . - pp 101 - 117 Titre : A review of image fusion techniques for pan-sharpening of high-resolution satellite imagery Type de document : Article/Communication Auteurs : Farzaneh Dadrass Javan, Auteur ; Farhad Samadzadegan, Auteur ; Soroosh Mehravar, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 101 - 117 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] affinage d'image [Termes descripteurs IGN] analyse de variance [Termes descripteurs IGN] fusion d'images [Termes descripteurs IGN] image Kompsat [Termes descripteurs IGN] image à haute résolution [Termes descripteurs IGN] image Geoeye [Termes descripteurs IGN] image Ikonos [Termes descripteurs IGN] image multibande [Termes descripteurs IGN] image panchromatique [Termes descripteurs IGN] image Pléiades-HR [Termes descripteurs IGN] image Quickbird [Termes descripteurs IGN] image Worldview [Termes descripteurs IGN] netteté [Termes descripteurs IGN] pansharpening (fusion d'images) [Termes descripteurs IGN] pouvoir de résolution spectrale Résumé : (auteur) Pan-sharpening methods are commonly used to synthesize multispectral and panchromatic images. Selecting an appropriate algorithm that maintains the spectral and spatial information content of input images is a challenging task. This review paper investigates a wide range of algorithms, including 41 methods. For this purpose, the methods were categorized as Component Substitution (CS-based), Multi-Resolution Analysis (MRA), Variational Optimization-based (VO), and Hybrid and were tested on a collection of 21 case studies. These include images from WorldView-2, 3 & 4, GeoEye-1, QuickBird, IKONOS, KompSat-2, KompSat-3A, TripleSat, Pleiades-1, Pleiades with the aerial platform, and Deimos-2. Neural network-based methods were excluded due to their substantial computational requirements for operational mapping purposes. The methods were evaluated based on four Spectral and three Spatial quality metrics. An Analysis Of Variance (ANOVA) was used to statistically compare the pan-sharpening categories. Results indicate that MRA-based methods performed better in terms of spectral quality, whereas most Hybrid-based methods had the highest spatial quality and CS-based methods had the lowest results both spectrally and spatially. The revisited version of the Additive Wavelet Luminance Proportional Pan-sharpening method had the highest spectral quality, whereas Generalized IHS with Best Trade-off Parameter with Additive Weights showed the highest spatial quality. CS-based methods generally had the fastest run-time, whereas the majority of methods belonging to MRA and VO categories had relatively long run times. Numéro de notice : A2021-014 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2020.11.001 date de publication en ligne : 21/11/2020 En ligne : https://doi.org/10.1016/j.isprsjprs.2020.11.001 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96418 [article]

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A framework for unsupervised wildfire damage assessment using VHR satellite images with PlanetScope data / Minkyung Chung in Remote sensing, vol 12 n° 22 (December 2020)  

Public [article]  inRemote sensing > vol 12 n° 22 (December 2020) . - n° 3835 Titre : A framework for unsupervised wildfire damage assessment using VHR satellite images with PlanetScope data Type de document : Article/Communication Auteurs : Minkyung Chung, Auteur ; Youkyung Han, Auteur ; Yongil Kim, Auteur Année de publication : 2020 Article en page(s) : n° 3835 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] aide à la décision [Termes descripteurs IGN] classification non dirigée [Termes descripteurs IGN] classification par forêts aléatoires [Termes descripteurs IGN] classification par séparateurs à vaste marge [Termes descripteurs IGN] Corée du sud [Termes descripteurs IGN] détection de changement [Termes descripteurs IGN] dommage [Termes descripteurs IGN] estimation par noyau [Termes descripteurs IGN] flou [Termes descripteurs IGN] gestion des risques [Termes descripteurs IGN] image à très haute résolution [Termes descripteurs IGN] image Geoeye [Termes descripteurs IGN] image multibande [Termes descripteurs IGN] image PlanetScope [Termes descripteurs IGN] incendie de forêt [Termes descripteurs IGN] Normalized Difference Vegetation Index Résumé : (auteur) The application of remote sensing techniques for disaster management often requires rapid damage assessment to support decision-making for post-treatment activities. As the on-demand acquisition of pre-event very high-resolution (VHR) images is typically limited, PlanetScope (PS) offers daily images of global coverage, thereby providing favorable opportunities to obtain high-resolution pre-event images. In this study, we propose an unsupervised change detection framework that uses post-fire VHR images with pre-fire PS data to facilitate the assessment of wildfire damage. To minimize the time and cost of human intervention, the entire process was executed in an unsupervised manner from image selection to change detection. First, to select clear pre-fire PS images, a blur kernel was adopted for the blind and automatic evaluation of local image quality. Subsequently, pseudo-training data were automatically generated from contextual features regardless of the statistical distribution of the data, whereas spectral and textural features were employed in the change detection procedure to fully exploit the properties of different features. The proposed method was validated in a case study of the 2019 Gangwon wildfire in South Korea, using post-fire GeoEye-1 (GE-1) and pre-fire PS images. The experimental results verified the effectiveness of the proposed change detection method, achieving an overall accuracy of over 99% with low false alarm rate (FAR), which is comparable to the accuracy level of the supervised approach. The proposed unsupervised framework accomplished efficient wildfire damage assessment without any prior information by utilizing the multiple features from multi-sensor bi-temporal images. Numéro de notice : A2020-793 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/rs12223835 date de publication en ligne : 22/11/2020 En ligne : https://doi.org/10.3390/rs12223835 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96570 [article]

Unsupervised-restricted deconvolutional neural network for very high resolution remote-sensing image classification / Yiting Tao in IEEE Transactions on geoscience and remote sensing, vol 55 n° 12 (December 2017)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 55 n° 12 (December 2017) . - pp 6805 - 6823 Titre : Unsupervised-restricted deconvolutional neural network for very high resolution remote-sensing image classification Type de document : Article/Communication Auteurs : Yiting Tao, Auteur ; Miaozhong Xu, Auteur ; Fan Zhang, Auteur ; Bo Du, Auteur ; Liangpei Zhang, Auteur Année de publication : 2017 Article en page(s) : pp 6805 - 6823 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] apprentissage non-dirigé [Termes descripteurs IGN] classification pixellaire [Termes descripteurs IGN] déconvolution [Termes descripteurs IGN] image Geoeye [Termes descripteurs IGN] image Quickbird [Termes descripteurs IGN] méthode fondée sur le noyau [Termes descripteurs IGN] réseau neuronal convolutif Résumé : (Auteur) As the acquisition of very high resolution (VHR) satellite images becomes easier owing to technological advancements, ever more stringent requirements are being imposed on automatic image interpretation. Moreover, per-pixel classification has become the focus of research interests in this regard. However, the efficient and effective processing and the interpretation of VHR satellite images remain a critical task. Convolutional neural networks (CNNs) have recently been applied to VHR satellite images with considerable success. However, the prevalent CNN models accept input data of fixed sizes and train the classifier using features extracted directly from the convolutional stages or the fully connected layers, which cannot yield pixel-to-pixel classifications. Moreover, training a CNN model requires large amounts of labeled reference data. These are challenging to obtain because per-pixel labeled VHR satellite images are not open access. In this paper, we propose a framework called the unsupervised-restricted deconvolutional neural network (URDNN). It can solve these problems by learning an end-to-end and pixel-to-pixel classification and handling a VHR classification using a fully convolutional network and a small number of labeled pixels. In URDNN, supervised learning is always under the restriction of unsupervised learning, which serves to constrain and aid supervised training in learning more generalized and abstract feature. To some degree, it will try to reduce the problems of overfitting and undertraining, which arise from the scarcity of labeled training data, and to gain better classification results using fewer training samples. It improves the generality of the classification model. We tested the proposed URDNN on images from the Geoeye and Quickbird sensors and obtained satisfactory results with the highest overall accuracy (OA) achieved as 0.977 and 0.989, respectively. Experiments showed that the combined effects of additional kernels and stages may have produced better results, and two-stage URDNN consistently produced a more stable result. We compared URDNN with four methods and found that with a small ratio of selected labeled data items, it yielded the highest and most stable results, whereas the accuracy values of the other methods quickly decreased. For some categories with fewer training pixels, accuracy for categories from other methods was considerably worse than that in URDNN, with the largest difference reaching almost 10%. Hence, the proposed URDNN can successfully handle the VHR image classification using a small number of labeled pixels. Furthermore, it is more effective than state-of-the-art methods. Numéro de notice : A2017-766 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2017.2734697 En ligne : https://doi.org/10.1109/TGRS.2017.2734697 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=88803 [article]

RPC-based coregistration of VHR imagery for urban change detection / Shabnam Jabari in Photogrammetric Engineering & Remote Sensing, PERS, vol 82 n° 7 (juillet 2016)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 82 n° 7 (juillet 2016) . - pp 521 - 534 Titre : RPC-based coregistration of VHR imagery for urban change detection Type de document : Article/Communication Auteurs : Shabnam Jabari, Auteur ; Yun Zhang, Auteur Année de publication : 2016 Article en page(s) : pp 521 - 534 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] angle de visée [Termes descripteurs IGN] coefficient de corrélation [Termes descripteurs IGN] détection de changement [Termes descripteurs IGN] image à très haute résolution [Termes descripteurs IGN] image Geoeye [Termes descripteurs IGN] image Ikonos [Termes descripteurs IGN] image multitemporelle [Termes descripteurs IGN] image Worldview [Termes descripteurs IGN] milieu urbain [Termes descripteurs IGN] modèle numérique de surface [Termes descripteurs IGN] modèle par fonctions rationnelles [Termes descripteurs IGN] points homologues Résumé : (Auteur) In urban change detection, coregistration between bi-temporal Very High Resolution (VHR) images taken from different viewing angles, especially from high off-nadir angles, is very challenging. The relief displacements of elevated objects in such images usually lead to significant misregistration that negatively affects the accuracy of change detection. This paper presents a novel solution, called Patch-Wise CoRegistration (PWCR), that can overcome the misregistration problem caused by viewing angle difference and accordingly improve the accuracy of urban change detection. The PWCR method utilizes a Digital Surface Model (DSM) and the Rational Polynomial Coefficients (RPCs) of the images to find corresponding points in a bi-temporal image set. The corresponding points are then used to generate corresponding patches in the image set. To prove that the PWCR method can overcome the misregistration problem and help achieving accurate change detection, two change detection criteria are tested and incorporated into a change detection framework. Experiments on four bi-temporal image sets acquired by Ikonos, GeoEye-1, and Worldview-2 satellites from different viewing angles show that the PWCR method can achieve highly accurate image patch coregistration (up to 80 percent higher than traditional coregistration for elevated objects), so that the change detection framework can produce accurate urban change detection results (over 90 percent). Numéro de notice : A2016-514 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article En ligne : http://dx.doi.org/10.14358/PERS.82.7.521 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=81585 [article]

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A feature selection approach for segmentation of very high-resolution satellite images / Ahmad Izadipour in Photogrammetric Engineering & Remote Sensing, PERS, vol 82 n° 3 (March 2016)

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Distinctive order based self-similarity descriptor for multi-sensor remote sensing image matching / Amin Sedaghat in ISPRS Journal of photogrammetry and remote sensing, vol 108 (October 2015)  

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MTF-adjusted pansharpening approach based on coupled multiresolution decompositions / Abdelaziz Kallel in IEEE Transactions on geoscience and remote sensing, vol 53 n° 6 (June 2015)  

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Radiometric and geometric evaluation of GeoEye-1, WorldView-2 and Pléiades-1A stereo images for 3D information extraction / Daniela Poli in ISPRS Journal of photogrammetry and remote sensing, vol 100 (February 2015)  

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Estimating fractional land cover in semi-arid central Kalahari: the impact of mapping method (spectral unmixing vs. object-based image analysis) and vegetation morphology / Niti B. Mishra in Geocarto international, vol 29 n° 7 - 8 (November - December 2014)  

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Archéologie minière et métallurgique en Bolivie : l'apport de l'image satellite aux opérations de prospection en milieu aride / Florian Tereygeol in Photo interpretation, European journal of applied remote sensing, vol 50 n° 1 (mars 2014)

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Generation and quality assessment of stereo-extracted DSM from Geoeye-1 and Worldview-2 imagery / Manuel Angel Aguilar in IEEE Transactions on geoscience and remote sensing, vol 52 n° 2 (February 2014)  

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Agricultural field delimitation using active learning and random forests margin / Karim Ghariani (2014)  

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Automated detection of buildings from single VHR multispectral images using shadow information and graph cuts / Ali Ozgun Ok in ISPRS Journal of photogrammetry and remote sensing, vol 86 (December 2013)

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Cartographie et suivi de la densité des arbres de l'arganeraie (Sud-Ouest du Maroc) à partir d'images de télédétection à haute résolution spatiale / Mbark Aouragh in Revue Française de Photogrammétrie et de Télédétection, n° 203 (Juillet 2013)

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