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Unsupervised denoising for satellite imagery using wavelet directional cycleGAN / Shaoyang Kong in IEEE Transactions on geoscience and remote sensing, vol 59 n° 8 (August 2021)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 59 n° 8 (August 2021) . - pp 6573 - 6585 Titre : Unsupervised denoising for satellite imagery using wavelet directional cycleGAN Type de document : Article/Communication Auteurs : Shaoyang Kong, Auteur ; Cheng Hu, Auteur ; Rui Wang, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 6573 - 6585 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes IGN] apprentissage non-dirigé [Termes IGN] apprentissage profond [Termes IGN] classification non dirigée [Termes IGN] filtrage du bruit [Termes IGN] image radar [Termes IGN] Insecta [Termes IGN] polarimétrie radar [Termes IGN] réseau antagoniste génératif [Termes IGN] transformation en ondelettes Résumé : (auteur) The measurement of insect radar cross section (RCS) is a prerequisite for the studies such as the quantitative estimation of insect population density and the identification of insects using entomological radar. In this article, we established a multiband polarimetric RCS measurement system in the microwave anechoic chamber. The targets’ range profile at different frequencies can be obtained based on the step frequency continuous wave, and meanwhile the clutter elimination and polarimetric calibration were applied to reduce the measuring error. The multifrequency (X-/Ku-/Ka-bands) polarimetric RCSs of 169 insects belonging to 21 species were measured and reported, which is the first time to systematically present the multifrequency polarimetric RCSs of insects. The mass of all specimens range from 25.6 to 964 mg, and their ventral-aspect RCSs range from −57.47 to −32.17 dBsm at X-band, from −48.27 to −33.87 dBsm at Ku-band and from −69.76 to −36.40 dBsm at Ka-band. For small insects less than 300 mg, the HH polarization RCS increases rapidly with frequency at X-band and fluctuates with the frequency at Ku-band, while the VV polarization RCS increases monotonically with frequency at X- and Ku-band. For larger insects, the HH polarization RCS decreased slowly with frequency at X-band and fluctuates with the frequency at Ku-band, while the VV polarization RCS increases with the frequency, then reaches the maximum, finally fluctuates with the frequency. At Ka-band, the measured polarization RCS versus frequency curves are smooth and all show similar variation. The measurement results verify the effectiveness and accuracy of the established system. Numéro de notice : A2021-631 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.3025601 date de publication en ligne : 08/10/2020 En ligne : https://doi.org/10.1109/TGRS.2020.3025601 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98281 [article]

Comparison of classification methods for urban green space extraction using very high resolution worldview-3 imagery / S. Vigneshwaran in Geocarto international, vol 36 n° 13 ([15/07/2021])  

Public [article]  inGeocarto international > vol 36 n° 13 [15/07/2021] . - pp 1429 - 1442 Titre : Comparison of classification methods for urban green space extraction using very high resolution worldview-3 imagery Type de document : Article/Communication Auteurs : S. Vigneshwaran, Auteur ; S. Vasantha Kumar, Auteur Année de publication : 2021 Article en page(s) : pp 1429 - 1442 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] carte de la végétation [Termes IGN] classification dirigée [Termes IGN] classification non dirigée [Termes IGN] classification orientée objet [Termes IGN] espace vert [Termes IGN] flore urbaine [Termes IGN] image à très haute résolution [Termes IGN] image Worldview [Termes IGN] New Delhi (Inde ; ville) [Termes IGN] Normalized Difference Vegetation Index [Termes IGN] urbanisme Résumé : (auteur) Urban green space (UGS) plays a vital role in maintaining the ecological balance of a city and in ensuring healthy living of the city inhabitants. It is generally suggested that one-third of the city should be covered by green and to ensure this, the city administrators must have an accurate map of the existing UGS. Such a map would be useful to visualize the distribution of the existing green cover and also to find out the areas that can possibly be converted to UGS. Reported studies on UGS mapping have mostly used medium and high resolution images such as Landsat-TM, ETM+, Sentinel-2A, IKONOS, etc. However, studies on the use of very high resolution images for UGS extraction are very limited. The present study is a first attempt in utilizing the very high resolution Worldview-3 image for UGS extraction. Performance of different classification methods such as unsupervised, supervised, object based and normalized difference vegetation index (NDVI) were compared using the pan sharpened Worldview-3 image covering part of New Delhi in India. It was found that the unsupervised classification followed by manual recoding method showed superior performance with overall accuracy (OA) of 99% and κ coefficient of 0.98. Also, the OA achieved in the present study is the highest when compared to other reported studies on UGS extraction. The map of UGS revealed that almost 40% of the study area is covered by green which is more than the recommended value of 33% (one-third). In order to check the universality of the unsupervised classification approach in extracting UGS, Worldview-3 image covering Rio in Brazil was tested. It was found that an OA of 98% and κ coefficient of 0.95 were obtained which clearly indicate that the proposed approach would work very well in extracting UGS from any Worldview-3 imagery. Numéro de notice : A2021-553 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2019.1665714 date de publication en ligne : 18/09/2019 En ligne : https://doi.org/10.1080/10106049.2019.1665714 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98104 [article]

Review of spectral indices for urban remote sensing / Akib Javed in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 7 (July 2021)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 87 n° 7 (July 2021) . - pp 513 - 524 Titre : Review of spectral indices for urban remote sensing Type de document : Article/Communication Auteurs : Akib Javed, Auteur ; Qimin Cheng, Auteur ; Hao Peng, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 513 - 524 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] bande spectrale [Termes IGN] classification non dirigée [Termes IGN] détection du bâti [Termes IGN] indice de détection [Termes IGN] milieu urbain [Termes IGN] occupation du sol [Termes IGN] surface imperméable Résumé : (Auteur) Urban spectral indices have made promising improvements in the last two decades in urban land use land cover studies through mapping, estimation, change detection, time-series analyzing, urban dynamics, monitoring, modeling, and so on. Remote sensing spectral indices are unsupervised, unbiased, rapid, scalable, and quantitative in information extraction. Hence, we aimed to summarize the most relevant urban spectral indices by focusing on multispectral, thermal, and nighttime lights indices. We use the search terms "urban index", "built-up index", "normalized difference built-up area (NDBI )", "impervious surface index", and "spectral urban index" to collect relevant literature from the "Web of Science Core Collection" database. We found that all urban spectral indices developed since 2003, except NDBI. This review will help understand the applications of urban spectral indices, the selection of indices based on available spectral bands, and their merits and demerits. Numéro de notice : A2021-572 Affiliation des auteurs : non IGN Thématique : IMAGERIE/URBANISME Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.87.7.513 date de publication en ligne : 01/07/2021 En ligne : https://doi.org/10.14358/PERS.87.7.513 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98167 [article]

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Semantic unsupervised change detection of natural land cover with multitemporal object-based analysis on SAR images / Donato Amitrano in IEEE Transactions on geoscience and remote sensing, Vol 59 n° 7 (July 2021)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > Vol 59 n° 7 (July 2021) . - pp 5494 - 5514 Titre : Semantic unsupervised change detection of natural land cover with multitemporal object-based analysis on SAR images Type de document : Article/Communication Auteurs : Donato Amitrano, Auteur ; Raffaella Guida, Auteur ; Pasquale Lervolino, Auteur Année de publication : 2021 Article en page(s) : pp 5494 - 5514 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] analyse d'image orientée objet [Termes IGN] biomasse forestière [Termes IGN] canopée [Termes IGN] changement d'occupation du sol [Termes IGN] classification floue [Termes IGN] classification non dirigée [Termes IGN] déboisement [Termes IGN] détection de changement [Termes IGN] image multitemporelle [Termes IGN] image radar moirée [Termes IGN] image RVB [Termes IGN] image Sentinel-SAR [Termes IGN] Normalized Difference Vegetation Index [Termes IGN] segmentation d'image [Termes IGN] seuillage d'image [Termes IGN] texture d'image Résumé : (auteur) Change detection is one of the most addressed topics in the remote sensing community. When performed on synthetic aperture radar images, the most critical issues are as follows: 1) the labeling of the identified changing patterns and 2) the scarce robustness of classic pixel-based approaches based on threshold segmentation of an appropriate change index, which tend to fail when multiple changes are present in the study area. In this work, a new methodology for unsupervised change detection in vegetation canopy is presented. It overcomes these limitations by exploiting multitemporal geographical object-based image analysis with the aim to make the intrinsic semantic of data emerge and direct the processing toward the identification of precise classes of changes through dictionary-based preclassification and fuzzy combination of class-specific information layers. The proposed methodology has been tested in ten different experiments covering agriculture and clear-cut deforestation applications. The results, validated against literature methods, highlighted the superiority of the proposed approach, which was quantitatively assessed in terms of standard classification quality parameters. On agriculture experiments, it allowed for an average increase in the detection accuracy of about 11% with respect to the best performing literature method, with an increment of the false alarm rate in the order of 0.5%. In case of deforestation, the registered detection accuracy was comparable to that achieved by the literature, while the most significant benefit was the reduction, of more than one-third, of the number of detected false deforestation patterns. Overall, the main characteristics of the proposed architecture are the robustness and the lack of any supervision, which makes it very well-suited for operational scenarios. Numéro de notice : A2021-528 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.3029841 date de publication en ligne : 22/10/2020 En ligne : https://doi.org/10.1109/TGRS.2020.3029841 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97978 [article]

A novel unsupervised change detection method from remotely sensed imagery based on an improved thresholding algorithm / Sara Khanbani in Applied geomatics, vol 13 n° 1 (May 2021)  

Public [article]  inApplied geomatics > vol 13 n° 1 (May 2021) . - pp 89 - 105 Titre : A novel unsupervised change detection method from remotely sensed imagery based on an improved thresholding algorithm Type de document : Article/Communication Auteurs : Sara Khanbani, Auteur ; Ali Mohammadzadeh, Auteur ; Milad Janalipour, Auteur Année de publication : 2021 Article en page(s) : pp 89 - 105 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] Alaska (Etats-Unis) [Termes IGN] algorithme génétique [Termes IGN] changement temporel [Termes IGN] classification floue [Termes IGN] classification non dirigée [Termes IGN] classification par nuées dynamiques [Termes IGN] coût [Termes IGN] détection de changement [Termes IGN] seuillage Résumé : (auteur) Change Detection (CD) problem from remotely sensed images is a popular topic among researchers. Because of the diversity in the problem of change detection and the complexity of the study areas it cannot be claimed that there is an appropriate and prevalent algorithm which is more effective for different types of the case study. As a fundamental investigation, it is critical to recognize the weaknesses of the state of artworks in change detection. Also, those examined weaknesses have to be improved aptly to develop a new strong method. This paper presents a thresholding algorithm improved by the Genetic Algorithm (GA) in CD problems, which focuses on minimizing a novel cost function. The suggested cost function can be adopted for local and global change variations in difference images without any prior assumptions. The presented algorithm was tested on two data sets (i.e., Alaska region and Uremia Lake) to validate its effectiveness. Experimental results demonstrated that the proposed algorithm in this work has improved the accuracy of change detection (changed pixel accuracy term) in the Alaska region about 8%–12% and also in Uremia Lake approximately between 8%–13% in comparison with other conventional methods including Fuzzy C- Means (FCM), Otsu thresholding, K-Means, and K-Medoid. Numéro de notice : A2021-237 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1007/s12518-020-00323-6 date de publication en ligne : 22/06/2020 En ligne : https://doi.org/10.1007/s12518-020-00323-6 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97246 [article]

Unsupervised multi-level feature extraction for improvement of hyperspectral classification / Qiaoqiao Sun in Remote sensing, vol 13 n° 8 (April-2 2021)  

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Extraction of sea ice cover by Sentinel-1 SAR based on support vector machine with unsupervised generation of training data / Xiao-Ming Li in IEEE Transactions on geoscience and remote sensing, vol 59 n° 4 (April 2021)  

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Spectral–spatial-aware unsupervised change detection with stochastic distances and support vector machines / Rogério Galante Negri in IEEE Transactions on geoscience and remote sensing, vol 59 n° 4 (April 2021)  

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Unsupervised pansharpening based on self-attention mechanism / Ying Qu in IEEE Transactions on geoscience and remote sensing, vol 59 n° 4 (April 2021)  

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Improving the unsupervised mapping of riparian bugweed in commercial forest plantations using hyperspectral data and LiDAR / Kabir Peerbhay in Geocarto international, vol 36 n° 4 ([01/03/2021])  

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Robust unsupervised small area change detection from SAR imagery using deep learning / Xinzheng Zhang in ISPRS Journal of photogrammetry and remote sensing, vol 173 (March 2021)  

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Unsupervised deep representation learning for real-time tracking / Ning Wang in International journal of computer vision, vol 129 n° 2 (February 2021)  

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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)  

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Unsupervised deep joint segmentation of multitemporal high-resolution images / Sudipan Saha in IEEE Transactions on geoscience and remote sensing, Vol 58 n° 12 (December 2020)  

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A fractal projection and Markovian segmentation-based approach for multimodal change detection / Max Mignotte in IEEE Transactions on geoscience and remote sensing, vol 58 n° 11 (November 2020)  

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