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An incremental isomap method for hyperspectral dimensionality reduction and classification / Yi Ma in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 6 (June 2021)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 87 n° 6 (June 2021) . - pp 445 - 455 Titre : An incremental isomap method for hyperspectral dimensionality reduction and classification Type de document : Article/Communication Auteurs : Yi Ma, Auteur ; Zezhong Zheng, Auteur ; Yutang Ma, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 445 - 455 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] algorithme d'apprentissage [Termes IGN] classification barycentrique [Termes IGN] classification par séparateurs à vaste marge [Termes IGN] échantillonnage de données [Termes IGN] image AVIRIS [Termes IGN] image hyperspectrale [Termes IGN] squelettisation [Termes IGN] utilisation du sol Résumé : (Auteur) Many manifold learning algorithms conduct an eigen vector analysis on a data-similarity matrix with a size of N×N, where N is the number of data points. Thus, the memory complexity of the analysis is no less than O(N2). We present in this article an incremental manifold learning approach to handle large hyperspectral data sets for land use identification. In our method, the number of dimensions for the high-dimensional hyperspectral-image data set is obtained with the training data set. A local curvature variation algorithm is utilized to sample a subset of data points as landmarks. Then a manifold skeleton is identified based on the landmarks. Our method is validated on three AVIRIS hyperspectral data sets, outperforming the comparison algorithms with a k–nearest-neighbor classifier and achieving the second best performance with support vector machine. Numéro de notice : A2021-375 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.87.7.445 date de publication en ligne : 01/06/2021 En ligne : https://doi.org/10.14358/PERS.87.7.445 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97829 [article]

Evaluating the performance of hyperspectral leaf reflectance to detect water stress and estimation of photosynthetic capacities / Jingjing Zhou in Remote sensing, vol 13 n° 11 (June-1 2021)  

Public [article]  inRemote sensing > vol 13 n° 11 (June-1 2021) . - n° 2160 Titre : Evaluating the performance of hyperspectral leaf reflectance to detect water stress and estimation of photosynthetic capacities Type de document : Article/Communication Auteurs : Jingjing Zhou, Auteur ; Ya-Hao Zhang, Auteur ; Ze-Min Han, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 2160 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] apprentissage automatique [Termes IGN] Chine [Termes IGN] Citrus (genre) [Termes IGN] classification par forêts aléatoires [Termes IGN] classification par séparateurs à vaste marge [Termes IGN] feuille (végétation) [Termes IGN] image hyperspectrale [Termes IGN] photosynthèse [Termes IGN] réflectance végétale [Termes IGN] rendement agricole [Termes IGN] stress hydrique [Termes IGN] surveillance de la végétation Résumé : (auteur) Advanced techniques capable of early, rapid, and nondestructive detection of the impacts of drought on fruit tree and the measurement of the underlying photosynthetic traits on a large scale are necessary to meet the challenges of precision farming and full prediction of yield increases. We tested the application of hyperspectral reflectance as a high-throughput phenotyping approach for early identification of water stress and rapid assessment of leaf photosynthetic traits in citrus trees by conducting a greenhouse experiment. To this end, photosynthetic CO2 assimilation rate (Pn), stomatal conductance (Cond) and transpiration rate (Trmmol) were measured with gas-exchange approaches alongside measurements of leaf hyperspectral reflectance from citrus grown across a gradient of soil drought levels six times, during 20 days of stress induction and 13 days of rewatering. Water stress caused Pn, Cond, and Trmmol rapid and continuous decline throughout the entire drought period. The upper layer was more sensitive to drought than middle and lower layers. Water stress could also bring continuous and dynamic changes of the mean spectral reflectance and absorptance over time. After trees were rewatered, these differences were not obvious. The original reflectance spectra of the four water stresses were surprisingly of low diversity and could not track drought responses, whereas specific hyperspectral spectral vegetation indices (SVIs) and absorption features or wavelength position variables presented great potential. The following machine-learning algorithms: random forest (RF), support vector machine (SVM), gradient boost (GDboost), and adaptive boosting (Adaboost) were used to develop a measure of photosynthesis from leaf reflectance spectra. The performance of four machine-learning algorithms were assessed, and RF algorithm yielded the highest predictive power for predicting photosynthetic parameters (R2 was 0.92, 0.89, and 0.88 for Pn, Cond, and Trmmol, respectively). Our results indicated that leaf hyperspectral reflectance is a reliable and stable method for monitoring water stress and yield increase, with great potential to be applied in large-scale orchards. Numéro de notice : A2021-440 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.3390/rs13112160 date de publication en ligne : 31/05/2021 En ligne : https://doi.org/10.3390/rs13112160 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97826 [article]

Multiscale context-aware ensemble deep KELM for efficient hyperspectral image classification / Bobo Xi in IEEE Transactions on geoscience and remote sensing, vol 59 n° 6 (June 2021)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 59 n° 6 (June 2021) . - pp 5114 - 5130 Titre : Multiscale context-aware ensemble deep KELM for efficient hyperspectral image classification Type de document : Article/Communication Auteurs : Bobo Xi, Auteur ; Jiaojiao Li, Auteur ; Yunsong Li, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 5114 - 5130 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] apprentissage profond [Termes IGN] image hyperspectrale [Termes IGN] méthode fondée sur le noyau [Termes IGN] segmentation multi-échelle [Termes IGN] superpixel Résumé : (auteur) Recently, multiscale spatial features have been widely utilized to improve the hyperspectral image (HSI) classification performance. However, fixed-size neighborhood involving the contextual information probably leads to misclassifications, especially for the boundary pixels. Additionally, it has been demonstrated that deep neural network (DNN) is practical to extract representative features for the classification tasks. Nevertheless, under the condition of high dimensionality versus small sample sizes, DNN tends to be over-fitting and it is generally time-consuming due to the deep-level feature learning process. To alleviate the aforementioned issues, we propose a multiscale context-aware ensemble deep kernel extreme learning machine (MSC-EDKELM) for efficient HSI classification. First, the scene of the HSI data set is over-segmented in multiscale via using the adaptive superpixel segmentation technique. Second, superpixel pattern (SP) and attentional neighboring superpixel pattern (ANSP) are generated by leveraging the superpixel maps, which can automatically comprise local and global contextual information, respectively. Afterward, an ensemble deep kernel extreme learning machine (EDKELM) is presented to investigate the deep-level characteristics in the SP and ANSP. Finally, the category of each pixel is accurately determined by the decision fusion and weighted output layer fusion strategy. Experimental results on four real-world HSI data sets demonstrate that the proposed frameworks outperform some classic and state-of-the-art methods with high computational efficiency, which can be employed to serve real-time applications. Numéro de notice : A2021-426 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueNat DOI : 10.1109/TGRS.2020.3022029 date de publication en ligne : 22/09/2020 En ligne : https://doi.org/10.1109/TGRS.2020.3022029 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97782 [article]

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

Public [article]  inRemote sensing > vol 13 n° 8 (April-2 2021) . - n° 1602 Titre : Unsupervised multi-level feature extraction for improvement of hyperspectral classification Type de document : Article/Communication Auteurs : Qiaoqiao Sun, Auteur ; Xuefeng Liu, Auteur ; Salah Bourennane, Auteur Année de publication : 2021 Article en page(s) : n° 1602 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] apprentissage profond [Termes IGN] classification non dirigée [Termes IGN] codage [Termes IGN] convolution (signal) [Termes IGN] déconvolution [Termes IGN] échantillonnage d'image [Termes IGN] extraction de traits caractéristiques [Termes IGN] image hyperspectrale [Termes IGN] observation multiniveaux Résumé : (auteur) Deep learning models have strong abilities in learning features and they have been successfully applied in hyperspectral images (HSIs). However, the training of most deep learning models requires labeled samples and the collection of labeled samples are labor-consuming in HSI. In addition, single-level features from a single layer are usually considered, which may result in the loss of some important information. Using multiple networks to obtain multi-level features is a solution, but at the cost of longer training time and computational complexity. To solve these problems, a novel unsupervised multi-level feature extraction framework that is based on a three dimensional convolutional autoencoder (3D-CAE) is proposed in this paper. The designed 3D-CAE is stacked by fully 3D convolutional layers and 3D deconvolutional layers, which allows for the spectral-spatial information of targets to be mined simultaneously. Besides, the 3D-CAE can be trained in an unsupervised way without involving labeled samples. Moreover, the multi-level features are directly obtained from the encoded layers with different scales and resolutions, which is more efficient than using multiple networks to get them. The effectiveness of the proposed multi-level features is verified on two hyperspectral data sets. The results demonstrate that the proposed method has great promise in unsupervised feature learning and can help us to further improve the hyperspectral classification when compared with single-level features. Numéro de notice : A2021-380 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/rs13081602 date de publication en ligne : 20/04/2021 En ligne : https://doi.org/10.3390/rs13081602 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97628 [article]

Automatic atmospheric correction for shortwave hyperspectral remote sensing data using a time-dependent deep neural network / Jian Sun 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 117 - 131 Titre : Automatic atmospheric correction for shortwave hyperspectral remote sensing data using a time-dependent deep neural network Type de document : Article/Communication Auteurs : Jian Sun, Auteur ; Fangcao Xu, Auteur ; Guido Cervone, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 117 - 131 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] apprentissage profond [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] correction atmosphérique [Termes IGN] détection de cible [Termes IGN] image hyperspectrale [Termes IGN] modèle de transfert radiatif [Termes IGN] rayonnement solaire [Termes IGN] réflectivité Résumé : (auteur) Atmospheric correction is an essential step in hyperspectral imaging and target detection from spectrometer remote sensing data. State-of-the-art atmospheric correction approaches either require extensive filed experiments or prior knowledge of atmospheric characteristics to improve the predicted accuracy, which are computational expensive and unsuitable for real time application. To take full advantages of remote sensing observation in quickly and reliably acquiring data for a large area, an automatic and efficient processing tool is required for atmospheric correction. In this paper, we propose a time-dependent neural network for automatic atmospheric correction and target detection using multi-scan hyperspectral data under different elevation angles. In addition to the total radiance, the collection day and time are also incorporated to improve the time-dependency of the network and represent the seasonal and diurnal characteristics of atmosphere and solar radiation. Results show that the proposed network has the capacity to accurately provide atmospheric characteristics and estimate precise reflectivity spectra with 95,72% averaged accuracy for different materials, including vegetation, sea ice, and ocean. Additional experiments are designed to investigate the network’s temporal dependency and performance on missing data. The error analysis confirms that our proposed network is capable of estimating atmospheric characteristics under both seasonally and diurnally varying environments and handling the influence of missing data. Both the predicted results and error analysis are promising and demonstrate that our network has the ability of providing accurate atmospheric correction and target detection in real time. Numéro de notice : A2021-208 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2021.02.007 date de publication en ligne : 24/02/2021 En ligne : https://doi.org/10.1016/j.isprsjprs.2021.02.007 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97186 [article]

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