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A geographic information-driven method and a new large scale dataset for remote sensing cloud/snow detection / Xi Wu in ISPRS Journal of photogrammetry and remote sensing, vol 174 (April 2021)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 174 (April 2021) . - pp 87 - 104 Titre : A geographic information-driven method and a new large scale dataset for remote sensing cloud/snow detection Type de document : Article/Communication Auteurs : Xi Wu, Auteur ; Zhenwei Shi, Auteur ; Zhengxia Zou, Auteur Année de publication : 2021 Article en page(s) : pp 87 - 104 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] altitude [Termes IGN] apprentissage profond [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] détection des nuages [Termes IGN] extraction de traits caractéristiques [Termes IGN] fusion de données [Termes IGN] image Gaofen [Termes IGN] information géographique [Termes IGN] latitude [Termes IGN] longitude [Termes IGN] modèle statistique [Termes IGN] neige [Termes IGN] Normalized Difference Snow Index Résumé : (auteur) Geographic information such as the altitude, latitude, and longitude are common but fundamental meta-records in remote sensing image products. In this paper, it is shown that such a group of records provides important priors for cloud and snow detection in remote sensing imagery. The intuition comes from some common geographical knowledge, where many of them are important but are often overlooked. For example, it is generally known that snow is less likely to exist in low-latitude or low-altitude areas, and clouds in different geographic may have various visual appearances. Previous cloud and snow detection methods simply ignore the use of such information, and perform detection solely based on the image data (band reflectance). Due to the neglect of such priors, most of these methods are difficult to obtain satisfactory performance in complex scenarios (e.g., cloud-snow coexistence). In this paper, a novel neural network called “Geographic Information-driven Network (GeoInfoNet)” is proposed for cloud and snow detection. In addition to the use of the image data, the model integrates the geographic information at both training and detection phases. A “geographic information encoder” is specially designed, which encodes the altitude, latitude, and longitude of imagery to a set of auxiliary maps and then feeds them to the detection network. The proposed network can be trained in an end-to-end fashion with dense robust features extracted and fused. A new dataset called “Levir_CS” for cloud and snow detection is built, which contains 4,168 Gaofen-1 satellite images and corresponding geographical records, and is over 20× larger than other datasets in this field. On “Levir_CS”, experiments show that the method achieves 90.74% intersection over union of cloud and 78.26% intersection over union of snow. It outperforms other state of the art cloud and snow detection methods with a large margin. Feature visualizations also show that the method learns some important priors which is close to the common sense. The proposed dataset and the code of GeoInfoNet are available in https://github.com/permanentCH5/GeoInfoNet. Numéro de notice : A2021-209 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2021.01.023 Date de publication en ligne : 22/02/2021 En ligne : https://doi.org/10.1016/j.isprsjprs.2021.01.023 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97187 [article]

Exemplaires(3)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
081-2021041	SL	Revue	Centre de documentation	Revues en salle	Disponible
081-2021043	DEP-RECP	Revue	LASTIG	Dépôt en unité	Exclu du prêt
081-2021042	DEP-RECF	Revue	Nancy	Dépôt en unité	Exclu du prêt

Can a machine generate humanlike language descriptions for a remote sensing image? / Zhenwei Shi in IEEE Transactions on geoscience and remote sensing, vol 55 n° 6 (June 2017)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 55 n° 6 (June 2017) . - pp 3623 - 3634 Titre : Can a machine generate humanlike language descriptions for a remote sensing image? Type de document : Article/Communication Auteurs : Zhenwei Shi, Auteur ; Zhengxia Zou, Auteur Année de publication : 2017 Article en page(s) : pp 3623 - 3634 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Télédétection [Termes IGN] descripteur [Termes IGN] image à haute résolution [Termes IGN] intelligence artificielle [Termes IGN] interface en langage naturel Résumé : (Auteur) This paper investigates an intriguing question in the remote sensing field: “can a machine generate humanlike language descriptions for a remote sensing image?” The automatic description of a remote sensing image (namely, remote sensing image captioning) is an important but rarely studied task for artificial intelligence. It is more challenging as the description must not only capture the ground elements of different scales, but also express their attributes as well as how these elements interact with each other. Despite the difficulties, we have proposed a remote sensing image captioning framework by leveraging the techniques of the recent fast development of deep learning and fully convolutional networks. The experimental results on a set of high-resolution optical images including Google Earth images and GaoFen-2 satellite images demonstrate that the proposed method is able to generate robust and comprehensive sentence description with desirable speed performance. Numéro de notice : A2017-479 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2017.2677464 En ligne : http://dx.doi.org/10.1109/TGRS.2017.2677464 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=86406 [article]

Multi-objective based spectral unmixing for hyperspectral images / Xia Xu in ISPRS Journal of photogrammetry and remote sensing, vol 124 (February 2017)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 124 (February 2017) . - pp 54 - 69 Titre : Multi-objective based spectral unmixing for hyperspectral images Type de document : Article/Communication Auteurs : Xia Xu, Auteur ; Zhenwei Shi, Auteur Année de publication : 2017 Article en page(s) : pp 54 - 69 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse des mélanges spectraux [Termes IGN] données clairsemées [Termes IGN] image hyperspectrale [Termes IGN] optimisation (mathématiques) Résumé : (Auteur) Sparse hyperspectral unmixing assumes that each observed pixel can be expressed by a linear combination of several pure spectra in a priori library. Sparse unmixing is challenging, since it is usually transformed to a NP-hard l0l0 norm based optimization problem. Existing methods usually utilize a relaxation to the original l0l0 norm. However, the relaxation may bring in sensitive weighted parameters and additional calculation error. In this paper, we propose a novel multi-objective based algorithm to solve the sparse unmixing problem without any relaxation. We transform sparse unmixing to a multi-objective optimization problem, which contains two correlative objectives: minimizing the reconstruction error and controlling the endmember sparsity. To improve the efficiency of multi-objective optimization, a population-based randomly flipping strategy is designed. Moreover, we theoretically prove that the proposed method is able to recover a guaranteed approximate solution from the spectral library within limited iterations. The proposed method can directly deal with l0l0 norm via binary coding for the spectral signatures in the library. Experiments on both synthetic and real hyperspectral datasets demonstrate the effectiveness of the proposed method. Numéro de notice : A2017-071 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2016.12.010 En ligne : http://dx.doi.org/10.1016/j.isprsjprs.2016.12.010 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=84306 [article]

Exemplaires(3)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
081-2017021	RAB	Revue	Centre de documentation	En réserve L003	Disponible
081-2017023	DEP-EXM	Revue	LASTIG	Dépôt en unité	Exclu du prêt
081-2017022	DEP-EAF	Revue	Nancy	Dépôt en unité	Exclu du prêt

Sparse unmixing of hyperspectral data using spectral a priori information / Wei Tang in IEEE Transactions on geoscience and remote sensing, vol 53 n° 2 (February 2015)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 53 n° 2 (February 2015) . - pp 770 - 783 Titre : Sparse unmixing of hyperspectral data using spectral a priori information Type de document : Article/Communication Auteurs : Wei Tang, Auteur ; Zhenwei Shi, Auteur ; Ying Wu, Auteur ; Changshui Zhang, Auteur Année de publication : 2015 Article en page(s) : pp 770 - 783 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse des mélanges spectraux [Termes IGN] classification spectrale [Termes IGN] image hyperspectrale Résumé : (Auteur) Given a spectral library, sparse unmixing aims at finding the optimal subset of endmembers from it to model each pixel in the hyperspectral scene. However, sparse unmixing still remains a challenging task due to the usually high mutual coherence of the spectral library. In this paper, we exploit the spectral a priori information in the hyperspectral image to alleviate this difficulty. It assumes that some materials in the spectral library are known to exist in the scene. Such information can be obtained via field investigation or hyperspectral data analysis. Then, we propose a novel model to incorporate the spectral a priori information into sparse unmixing. Based on the alternating direction method of multipliers, we present a new algorithm, which is termed sparse unmixing using spectral a priori information (SUnSPI), to solve the model. Experimental results on both synthetic and real data demonstrate that the spectral a priori information is beneficial to sparse unmixing and that SUnSPI can exploit this information effectively to improve the abundance estimation. Numéro de notice : A2015-104 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2014.2328336 En ligne : https://doi.org/10.1109/TGRS.2014.2328336 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=75622 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
065-2015021	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Regularized simultaneous forward–backward greedy algorithm for sparse unmixing of hyperspectral data / Wei Tang in IEEE Transactions on geoscience and remote sensing, vol 52 n° 9 Tome 1 (September 2014)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 52 n° 9 Tome 1 (September 2014) . - pp 5271 - 5288 Titre : Regularized simultaneous forward–backward greedy algorithm for sparse unmixing of hyperspectral data Type de document : Article/Communication Auteurs : Wei Tang, Auteur ; Zhenwei Shi, Auteur ; Y. Wu, Auteur Année de publication : 2014 Article en page(s) : pp 5271 - 5288 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse des mélanges spectraux [Termes IGN] image hyperspectrale [Termes IGN] optimisation (mathématiques) Résumé : (Auteur) Sparse unmixing assumes that each observed signature of a hyperspectral image is a linear combination of only a few spectra (endmembers) in an available spectral library. It then estimates the fractional abundances of these endmembers in the scene. The sparse unmixing problem still remains a great difficulty due to the usually high correlation of the spectral library. Under such circumstances, this paper presents a novel algorithm termed as the regularized simultaneous forward-backward greedy algorithm (RSFoBa) for sparse unmixing of hyperspectral data. The RSFoBa has low computational complexity of getting an approximate solution for the l0 problem directly and can exploit the joint sparsity among all the pixels in the hyperspectral data. In addition, the combination of the forward greedy step and the backward greedy step makes the RSFoBa more stable and less likely to be trapped into the local optimum than the conventional greedy algorithms. Furthermore, when updating the solution in each iteration, a regularizer that enforces the spatial-contextual coherence within the hyperspectral image is considered to make the algorithm more effective. We also show that the sublibrary obtained by the RSFoBa can serve as input for any other sparse unmixing algorithms to make them more accurate and time efficient. Experimental results on both synthetic and real data demonstrate the effectiveness of the proposed algorithm. Numéro de notice : A2014-442 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2013.2287795 En ligne : https://doi.org/10.1109/TGRS.2013.2287795 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=73979 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
065-2014091A	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Subspace matching pursuit for sparse unmixing of hyperspectral data / Zhenwei Shi in IEEE Transactions on geoscience and remote sensing, vol 52 n° 6 Tome 1 (June 2014)  

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