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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)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 59 n° 4 (April 2021) . - pp 2863 - 2876 Titre : Spectral–spatial-aware unsupervised change detection with stochastic distances and support vector machines Type de document : Article/Communication Auteurs : Rogério Galante Negri, Auteur ; Alejandro C. Frery, Auteur ; Wallace Casaca, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 2863 - 2876 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte [Termes descripteurs IGN] analyse de sensibilité [Termes descripteurs IGN] classification non dirigée [Termes descripteurs IGN] classification par séparateurs à vaste marge [Termes descripteurs IGN] détection d'ombre [Termes descripteurs IGN] détection de changement [Termes descripteurs IGN] détection des nuages [Termes descripteurs IGN] image ALOS-PALSAR [Termes descripteurs IGN] image Landsat-OLI [Termes descripteurs IGN] image Sentinel-MSI [Termes descripteurs IGN] processus stochastique [Termes descripteurs IGN] zone homogène Résumé : (auteur) Change detection is a topic of great interest in remote sensing. A good similarity metric to compute the variations among the images is the key to high-quality change detection. However, most existing approaches rely on the fixed threshold values or the user-provided ground truth in order to be effective. The inability to deal with artificial objects such as clouds and shadows is a significant difficulty for many change-detection methods. We propose a new unsupervised change-detection framework to address those critical points. The notion of homogeneous regions is introduced together with a set of geometric operations and statistic-based criteria to characterize and distinguish formally the change and nonchange areas in a pair of remote sensing images. Moreover, a robust and statistically well-posed family of stochastic distances is also proposed, which allows comparing the probability distributions of different regions/objects in the images. These stochastic measures are then used to train a support-vector-machine-based approach in order to detect the change/nonchange areas. Three study cases using the images acquired with different sensors are given in order to compare the proposed method with other well-known unsupervised methods. Numéro de notice : A2021-282 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.3009483 date de publication en ligne : 24/07/2020 En ligne : https://doi.org/10.1109/TGRS.2020.3009483 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97389 [article]

Impact of atmospheric correction on spatial heterogeneity relations between land surface temperature and biophysical compositions / Xin-Ming Zhu in IEEE Transactions on geoscience and remote sensing, Vol 59 n° 3 (March 2021)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > Vol 59 n° 3 (March 2021) . - pp 2680 - 2697 Titre : Impact of atmospheric correction on spatial heterogeneity relations between land surface temperature and biophysical compositions Type de document : Article/Communication Auteurs : Xin-Ming Zhu, Auteur ; Xiao-Ning Song, Auteur ; Pei Leng, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 2680 - 2697 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] Chine [Termes descripteurs IGN] correction atmosphérique [Termes descripteurs IGN] hétérogénéité spatiale [Termes descripteurs IGN] image Landsat-8 [Termes descripteurs IGN] Normalized Difference Vegetation Index [Termes descripteurs IGN] régression [Termes descripteurs IGN] température au sol [Termes descripteurs IGN] variable biophysique (végétation) Résumé : (Auteur) Investigating the relations between land surface temperature (LST) and biophysical compositions can help the understanding of the surface biophysical process. However, there are still uncertainties in determining the impacts of biophysical compositions on LST due to the atmospheric effects. In this article, four atmospheric correction algorithms were used to correct 12 Landsat 8 images in Xi’an, Beijing, Wuhan, and Guangzhou, China, including the Atmospheric Correction for Flat Terrain (ATCOR2), Quick Atmospheric Correction (QUAC), Fast Line-of-sight Atmospheric Analysis of Spectral Hypercube (FLAASH), and Second Simulation of Satellite Signal in the Solar Spectrum (6S). Then, geodetector was used to investigate the atmospheric correction differences in the spatial heterogeneity relationships between LST and normalized difference vegetation index (NDVI), normalized difference built-up index (NDBI), and bare soil index (BSI). Results indicate that the selected composition factors were greatly improved after atmospheric correction, and the relations between LST and three factors were characterized by obvious atmospheric correction differences in four study areas. On the whole, the 6S algorithm performed the best in improving the factor values and impacting the spatial heterogeneity relations between LST and biophysical compositions, followed by FLAASH, QUAC, and ATCOR2 algorithms. Except for Wuhan, 6S, FLAASH, and QUAC algorithms significantly enhanced the correlation between LST and NDVI. However, all algorithms weakened the correlations between LST, NDVI, and BSI, except Guangzhou. These findings have been verified using the regression analysis. In addition, with geodetector, combinations of any two composition factors all had strongly enhanced impacts on LST, and a combination between NDVI and NDBI performed the strongest in most cases. Numéro de notice : A2021-219 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.3002821 date de publication en ligne : 26/06/2020 En ligne : https://doi.org/10.1109/TGRS.2020.3002821 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97211 [article]

Performance evaluation of artificial neural networks for natural terrain classification / Perpetual Hope Akwensi in Applied geomatics, vol 13 n° 1 (March 2021)  

Public [article]  inApplied geomatics > vol 13 n° 1 (March 2021) Titre : Performance evaluation of artificial neural networks for natural terrain classification Type de document : Article/Communication Auteurs : Perpetual Hope Akwensi, Auteur ; Eric Thompson Brantson, Auteur ; Johanna Ngula Niipele, Auteur ; et al., Auteur Année de publication : 2021 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] Afrique occidentale [Termes descripteurs IGN] classification par nuées dynamiques [Termes descripteurs IGN] échantillonnage [Termes descripteurs IGN] fonction de base radiale [Termes descripteurs IGN] image Landsat-OLI [Termes descripteurs IGN] image multibande [Termes descripteurs IGN] inventaire de la végétation [Termes descripteurs IGN] réalité de terrain [Termes descripteurs IGN] regroupement de données [Termes descripteurs IGN] réseau neuronal artificiel [Termes descripteurs IGN] segmentation d'image Résumé : (auteur) Remotely sensed image segmentation and classification form a very important part of remote sensing which involves geo-data processing and analysis. Artificial neural networks (ANNs) are powerful machine learning approaches that have been successfully implemented in numerous fields of study. There exist many kinds of neural networks and there is no single efficient approach for resolving all geospatial problems. Therefore, this research aims at investigating and evaluating the efficiency of three ANN approaches, namely, backpropagation neural network (BPNN), radial basis function neural network (RBFNN), and Elman backpropagation recurrent neural network (EBPRNN) using multi-spectral satellite images for terrain feature classification. Additionally, there has been close to no application of EBPRNN in modeling multi-spectral satellite images even though they also contain patterns. The efficiency of the three tested approaches is presented using the kappa coefficient, user’s accuracy, producer’s accuracy, overall accuracy, classification error, and computational simulation time. The study demonstrated that all the three ANN models achieved the aim of pattern identification, segmentation, and classification. This paper also discusses the observations of increasing sample sizes as inputs in the various ANN models. It was concluded that RBFNN’s computational time increases with increasing sample size and consequently increasing the number of hidden neurons; BPNN on overall attained the highest accuracy compared to the other models; EBPRNN’s accuracy increases with increasing sample size, hence a promising and perhaps an alternative choice to BPNN and RBFNN if very large datasets are involved. Based on the performance metrics used in this study, BPNN is the best model out of the three evaluated ANN models. Numéro de notice : A2021-223 Affiliation des auteurs : non IGN Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s12518-021-00360-9 date de publication en ligne : 13/02/2021 En ligne : https://doi.org/10.1007/s12518-021-00360-9 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97194 [article]

Assessing spatial-temporal evolution processes and driving forces of karst rocky desertification / Fei Chen in Geocarto international, vol 36 n° 3 ([15/02/2021])  

Public [article]  inGeocarto international > vol 36 n° 3 [15/02/2021] . - pp 262 - 280 Titre : Assessing spatial-temporal evolution processes and driving forces of karst rocky desertification Type de document : Article/Communication Auteurs : Fei Chen, Auteur ; Shijie Wang, Auteur ; Xiaoyong Bai, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 262 - 280 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] analyse spatio-temporelle [Termes descripteurs IGN] carte d'utilisation du sol [Termes descripteurs IGN] Chine [Termes descripteurs IGN] classification et arbre de régression [Termes descripteurs IGN] désertification [Termes descripteurs IGN] données spatiotemporelles [Termes descripteurs IGN] image Landsat-OLI [Termes descripteurs IGN] image Landsat-TM [Termes descripteurs IGN] karst [Termes descripteurs IGN] lithologie Résumé : (auteur) Karst Rocky Desertification (KRD) has become the most serious ecological disaster in Southwest China. We used the data of Thematic Mapper (TM) images from 1990, 1995, 2000, 2004, and 2011 and the 2016 Operational Land Imager (OLI) image. These sensing images were pre-processed by removing non-karst areas based on lithology and cutting away the land types impossibly generating KRD from land use maps. Then, we used a Classification And Regression Tree (CART) to classify the KRD. We want to improve the interpretation accuracy of KRD through the above steps. The results were as follows: (1) The KRD experiences the evolution process of ‘first deterioration and then improvement’. The rate is −4.94 km2.a−1 over a period of 1990 to 2004, and the rate is 36.48 km2.a−1 from 2004 to 2016; (2) The most influential factors causing KRD formation are the lithology and the resident population density, with contribution rates of 30.17% and 25.86%, respectively. Numéro de notice : A2021-140 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2019.1595175 date de publication en ligne : 18/07/2019 En ligne : https://doi.org/10.1080/10106049.2019.1595175 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97036 [article]

Characterizing the spatial and temporal variation of the land surface temperature hotspots in Wuhan from a local scale / Chen Yang in Geo-spatial Information Science, vol 23 n° 4 (December 2020)  

Public [article]  inGeo-spatial Information Science > vol 23 n° 4 (December 2020) . - pp 327 - 340 Titre : Characterizing the spatial and temporal variation of the land surface temperature hotspots in Wuhan from a local scale Type de document : Article/Communication Auteurs : Chen Yang, Auteur ; Qingming Zhan, Auteur ; Sihang Gao, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 327 - 340 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] analyse spatio-temporelle [Termes descripteurs IGN] climat urbain [Termes descripteurs IGN] géomorphologie locale [Termes descripteurs IGN] ilot thermique urbain [Termes descripteurs IGN] image Landsat-OLI [Termes descripteurs IGN] image Terra-MODIS [Termes descripteurs IGN] image thermique [Termes descripteurs IGN] morphologie urbaine [Termes descripteurs IGN] processus gaussien [Termes descripteurs IGN] regroupement de données [Termes descripteurs IGN] série temporelle [Termes descripteurs IGN] température au sol [Termes descripteurs IGN] Wuhan (Chine) [Termes descripteurs IGN] zonage (urbanisme) Résumé : (auteur) Land Surface Temperature (LST) derived from space-borne Thermal-infrared (TIR) sensors is a key parameter of urban climate studies. Current studies are inefficient to capture the spatial and temporal variations of LST for only one snapshot adopted at one time. Focusing on the characterization of the spatial and temporal of LST variations at local scales, the latent patterns, and morphological characteristics are extracted in this study. Technically, sixteen MODerate-resolution Imaging Spectroradiometer (MODIS) eight-day synthesized LST products (MYD11A2) in 2002, 2007, 2012, and 2017 are employed. First, the non-parametric Multi-Task Gaussian Process Model (MTGP) is used to extract the smooth and continuous Latent LST (LLST) patterns using one LST subset and its temporally adjacent images. Second, the Multi-Scale Shape Index (MSSI) is then applied to quantify the morphological characteristics at the optimal scale. Then, the LLST patterns and MSSI maps are clustered into multiple spatial categories. The specific clusters with the highest LLST and MSSI values are considered as local LLST hotspots. The Hotspots Weighted Mean Center (HSWMC) and standard deviation ellipse are adopted to further investigate the spatiotemporal change of hotspots orientation, direction, and trajectories. Results revealed that Impervious Surfaces (IS) composition is the most significant external forcing of local LST anomalies. The configuration factors (e.g., shape index, aggregation index) also have a noticeable local warming effect. This study represents a latent pattern and morphology-based framework for LST hotspots spatial and temporal variations characterization, catering to the zoning and grading strategies in urban planning. Numéro de notice : A2020-788 Affiliation des auteurs : non IGN Thématique : IMAGERIE/URBANISME Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10095020.2020.1834882 date de publication en ligne : 06/11/2020 En ligne : https://doi.org/10.1080/10095020.2020.1834882 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96550 [article]

Analyse de la déforestation dans la périphérie ouest de la réserve de biosphère du Dja au Cameroun, à partir d'une série multi-annuelle d'images Landsat / Eric Wilson Tegno Nguekam in Revue Française de Photogrammétrie et de Télédétection, n° 222 (novembre 2020)  

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Forêt d'arbres aléatoires et classification d'images satellites : relation entre la précision du modèle d'entraînement et la précision globale de la classification / Aurélien N.G. Matsaguim in Revue Française de Photogrammétrie et de Télédétection, n° 222 (novembre 2020)  

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Combination of Landsat 8 OLI and Sentinel-1 SAR time-series data for mapping paddy fields in parts of West and Central Java provinces, Indonesia / Sanjiwana Arjasakusuma in ISPRS International journal of geo-information, vol 9 n° 11 (November 2020)  

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Comparative analysis of index and chemometric techniques-based assessment of leaf area index (LAI) in wheat through field spectroradiometer, Landsat-8, Sentinel-2 and Hyperion bands / Bappa Das in Geocarto international, vol 35 n° 13 ([01/10/2020])  

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Spatio-temporal relationship between land cover and land surface temperature in urban areas: A case study in Geneva and Paris / Xu Ge in ISPRS International journal of geo-information, vol 9 n° 10 (October 2020)  

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Applying multi-temporal Landsat satellite data and Markov-cellular automata to predict forest cover change and forest degradation of sundarban reserve forest, Bangladesh / Mohammad Emran Hasan in Forests, vol 11 n° 9 (September 2020)  

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Comparison of tree-based classification algorithms in mapping burned forest areas / Dilek Kucuk Matci in Geodetski vestnik, vol 64 n° 3 (September - November 2020)  

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Monitoring narrow mangrove stands in Baja California Sur, Mexico using linear spectral unmixing / Jonathan B. Thayn in Marine geodesy, Vol 43 n° 5 (September 2020)  

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Accuracies of support vector machine and random forest in rice mapping with Sentinel-1A, Landsat-8 and Sentinel-2A datasets / Lamin R. Mansaray in Geocarto international, vol 35 n° 10 ([01/08/2020])  

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Development and application of a new mangrove vegetation index (MVI) for rapid and accurate mangrove mapping / Alvin B. Baloloy in ISPRS Journal of photogrammetry and remote sensing, vol 166 (August 2020)  

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Extraction of built-up areas from Landsat-8 OLI data based on spectral-textural information and feature selection using support vector machine method / Vijendra Singh Bramhe in Geocarto international, vol 35 n° 10 ([01/08/2020])  

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Landuse and land cover identification and disaggregating socio-economic data with convolutional neural network / Jingtao Yao in Geocarto international, vol 35 n° 10 ([01/08/2020])  

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Improved optical image matching time series inversion approach for monitoring dune migration in North Sinai Sand Sea: Algorithm procedure, application, and validation / Eslam Ali in ISPRS Journal of photogrammetry and remote sensing, vol 164 (June 2020)  

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A water identification method basing on grayscale Landsat 8 OLI images / Zhitian Deng in Geocarto international, vol 35 n° 7 ([15/05/2020])  

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Assessment of winter season land surface temperature in the Himalayan regions around the Kullu area in India using Landsat-8 data / Divyesh Varade in Geocarto international, vol 35 n° 6 ([01/05/2020])  

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Assessment of malaria hazard, vulnerability, and risks in Dire Dawa City Administration of eastern Ethiopia using GIS and remote sensing / Abdinasir Moha in Applied geomatics, vol 12 n° 1 (April 2020)  

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Conterminous United States land cover change patterns 2001–2016 from the 2016 National Land Cover Database / Collin Homer in ISPRS Journal of photogrammetry and remote sensing, vol 162 (April 2020)  

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A Fusion Approach for Water Area Classification Using Visible, Near Infrared and Synthetic Aperture Radar for South Asian Conditions / Shahryar K. Ahmad in IEEE Transactions on geoscience and remote sensing, vol 58 n° 4 (April 2020)  

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GIS-based modeling for selection of dam sites in the Kurdistan region, Iraq / Arsalan Ahmed Othman in ISPRS International journal of geo-information, vol 9 n° 4 (April 2020)  

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How far can we trust forestry estimates from low-density LiDAR acquisitions? The Cutfoot Sioux experimental forest (MN, USA) case study / Enrico Borgogno Mondino in International Journal of Remote Sensing IJRS, vol 41 n°12 (20 - 30 March 2020)  

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