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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 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 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)

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 82 n° 3 (March 2016) . - pp 213 - 222 Titre : A feature selection approach for segmentation of very high-resolution satellite images Type de document : Article/Communication Auteurs : Ahmad Izadipour, Auteur ; Behzad Akbari, Auteur ; Barat Mojaradi, Auteur Année de publication : 2016 Article en page(s) : pp 213 - 222 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] extraction de traits caractéristiques [Termes descripteurs IGN] image Geoeye [Termes descripteurs IGN] image Quickbird [Termes descripteurs IGN] résolution globale (imagerie) [Termes descripteurs IGN] segmentation d'image Résumé : (auteur) Most of the feature selection (FS) methods in the literature determine features that are appropriate only for a given dataset. In contrast, in this paper a FS method that is not dependent to a specific dataset is proposed. In this regard, the effective feature types based on reasonable facts are predefined and appropriate candidate features for each feature type are selected. In proposed method, the features selected from a single labeled image can be used in segmentation of images captured by different satellites with similar spatial resolution. The selected feature types contain spatial and spectral features. The selected features are applied for segmentation of the images captured by QuickBird and GeoEye satellites and obtained results of proposed method are compared with well-known FS methods. Using different evaluation measures, our comparison shows the efficiency of the proposed method in providing better segmentation compared to other FS methods that are presented in this paper. Numéro de notice : A2016-178 Thématique : IMAGERIE Nature : Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=80519 [article]

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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)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 108 (October 2015) . - pp 62 – 71 Titre : Distinctive order based self-similarity descriptor for multi-sensor remote sensing image matching Type de document : Article/Communication Auteurs : Amin Sedaghat, Auteur ; Hamid Ebadi, Auteur Année de publication : 2015 Article en page(s) : pp 62 – 71 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] appariement d'images [Termes descripteurs IGN] extraction automatique [Termes descripteurs IGN] image Geoeye [Termes descripteurs IGN] image IRS [Termes descripteurs IGN] image Landsat-ETM+ [Termes descripteurs IGN] image multicapteur [Termes descripteurs IGN] image Quickbird [Termes descripteurs IGN] image SPOT 4 [Termes descripteurs IGN] image SPOT 5 [Termes descripteurs IGN] image SPOT 6 [Termes descripteurs IGN] image Terra-ASTER [Termes descripteurs IGN] image Worldview [Termes descripteurs IGN] invariant [Termes descripteurs IGN] SIFT (algorithme) [Termes descripteurs IGN] transformation projective Résumé : (auteur) Robust, well-distributed and accurate feature matching in multi-sensor remote sensing image is a difficult task duo to significant geometric and illumination differences. In this paper, a robust and effective image matching approach is presented for multi-sensor remote sensing images. The proposed approach consists of three main steps. In the first step, UR-SIFT (Uniform robust scale invariant feature transform) algorithm is applied for uniform and dense local feature extraction. In the second step, a novel descriptor namely Distinctive Order Based Self Similarity descriptor, DOBSS descriptor, is computed for each extracted feature. Finally, a cross matching process followed by a consistency check in the projective transformation model is performed for feature correspondence and mismatch elimination. The proposed method was successfully applied for matching various multi-sensor satellite images as: ETM+, SPOT 4, SPOT 5, ASTER, IRS, SPOT 6, QuickBird, GeoEye and Worldview sensors, and the results demonstrate its robustness and capability compared to common image matching techniques such as SIFT, PIIFD, GLOH, LIOP and LSS. Numéro de notice : A2015-852 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern En ligne : http://dx.doi.org/10.1016/j.isprsjprs.2015.06.003 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=79222 [article]

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)

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 53 n° 6 (June 2015) . - pp 3124 - 3145 Titre : MTF-adjusted pansharpening approach based on coupled multiresolution decompositions Type de document : Article/Communication Auteurs : Abdelaziz Kallel, Auteur Année de publication : 2015 Article en page(s) : pp 3124 - 3145 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] analyse multirésolution [Termes descripteurs IGN] décomposition d'image [Termes descripteurs IGN] fonction de transfert de modulation [Termes descripteurs IGN] image Geoeye [Termes descripteurs IGN] image multibande [Termes descripteurs IGN] image Pléiades-HR [Termes descripteurs IGN] image SPOT 6 [Termes descripteurs IGN] pansharpening (fusion d'images) [Termes descripteurs IGN] transformation en ondelettes Résumé : (Auteur) Among others, the wavelet-based pansharpening approach tries to enhance the resolution of the multispectral (MS) image by injection of spatial details extracted from the high-resolution panchromatic (PAN) image. The problem is presented as follows, the inputs are a coarse-resolution MS image and a high-resolution detail image provided from the PAN image; therefore, one would think that the wavelet reconstruction allows combining approximations and details to construct the high-resolution MS image. However, the wavelet transform (WT) assumes that details and approximations are calculated using the same wavelet decomposition. Now, in the pansharpening case, the MS low-resolution image is assumed to be aliased and blurred due to the imaging system modulation transfer function (MTF) that is approximated as a specific low-pass filter. Meanwhile, there are no constraints about details that can be extracted from PAN using discrete WT (DWT). Approximation and details are not any more orthogonal as needed in the reconstruct of the MS high-resolution image based on DWT. For that, we propose in this paper a new fusion schema [coupled multiresolution decomposition model (CMD)] allowing the reconstruction of a high-resolution MS given its approximation and details obtained by MTF-tailored downsampling and wavelet decomposition, respectively. For validation, CMD is applied to Pléiades, GeoEye-1, and SPOT 6 images. Compared to other approaches [i.e., Gram-Schmidt (GS) adaptive, GS mode 2 (GS2), “À trous' WT (AWT), generalized Laplacian pyramid (GLP), DWT, and PCI Geomatics software algorithm], our method performs generally better. Numéro de notice : A2015-282 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=76399 [article]

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Radiometric and geometric evaluation of GeoEye-1, WorldView-2 and Pléiades-1A stereo images for 3D information extraction / D. 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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A sparse image fusion algorithm with application to pan-sharpening / Xiao Xiang Zhu in IEEE Transactions on geoscience and remote sensing, vol 51 n° 5 Tome 1 (May 2013)

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Automated detection of arbitrarily shaped buildings in complex environments from monocular VHR optical satellite imagery / Ali Ozgun Ok in IEEE Transactions on geoscience and remote sensing, vol 51 n° 3 Tome 2 (March 2013)

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Utilisation d'image satellitaire et d'un modèle numérique d'altitude pour la cartographie des zones à risque d'inondation sur le littoral méditerranéen de Saïdia (Nord-Est du Maroc) / M. Mouzouri in Revue Française de Photogrammétrie et de Télédétection, n° 201 (Janvier 2013)

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