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Discriminative feature learning for unsupervised change detection in heterogeneous images based on a coupled neural network / Wei Zhao 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 7066 - 7080 Titre : Discriminative feature learning for unsupervised change detection in heterogeneous images based on a coupled neural network Type de document : Article/Communication Auteurs : Wei Zhao, Auteur ; Zhirui Wang, Auteur ; Maoguo Gong, Auteur ; Jia Liu, Auteur Année de publication : 2017 Article en page(s) : pp 7066 - 7080 Note générale : Bibliograpie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] analyse discriminante [Termes IGN] apprentissage profond [Termes IGN] classification par réseau neuronal [Termes IGN] détection de changement Résumé : (Auteur) With the application requirement, the technique for change detection based on heterogeneous remote sensing images is paid more attention. However, detecting changes between two heterogeneous images is challenging as they cannot be compared in low-dimensional space. In this paper, we construct an approximately symmetric deep neural network with two sides containing the same number of coupled layers to transform the two images into the same feature space. The two images are connected with the two sides and transformed into the same feature space, in which their features are more discriminative and the difference image can be generated by comparing paired features pixel by pixel. The network is first built by stacked restricted Boltzmann machines, and then, the parameters are updated in a special way based on clustering. The special way, motivated by that two heterogeneous images share the same reality in unchanged areas and retain respective properties in changed areas, shrinks the distance between paired features transformed from unchanged positions, and enlarges the distance between paired features extracted from changed positions. It is achieved through introducing two types of labels and updating parameters by adaptively changed learning rate. This is different from the existing methods based on deep learning that just do operations on positions predicted to be unchanged and extract only one type of labels. The whole process is completely unsupervised without any priori knowledge. Besides, the method can also be applied to homogeneous images. We test our method on heterogeneous images and homogeneous images. The proposed method achieves quite high accuracy. Numéro de notice : A2017-768 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2017.2739800 En ligne : https://doi.org/10.1109/TGRS.2017.2739800 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=88807 [article]

Modeling canopy reflectance over sloping terrain based on path length correction / Gaofei Yin in IEEE Transactions on geoscience and remote sensing, vol 55 n° 8 (August 2017)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 55 n° 8 (August 2017) . - pp 4597 - 4609 Titre : Modeling canopy reflectance over sloping terrain based on path length correction Type de document : Article/Communication Auteurs : Gaofei Yin, Auteur ; Ainong Li, Auteur ; Wei Zhao, Auteur ; et al., Auteur Année de publication : 2017 Article en page(s) : pp 4597 - 4609 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] distorsion du signal [Termes IGN] figuré du terrain [Termes IGN] méthode de Monte-Carlo [Termes IGN] modèle de simulation [Termes IGN] modèle numérique de surface de la canopée [Termes IGN] montagne [Termes IGN] pente [Termes IGN] réflectance végétale Résumé : (Auteur) Sloping terrain induces distortion of canopy reflectance (CR), and the retrieval of biophysical variables from remote sensing data needs to account for topographic effects. We developed a 1-D model (the path length correction (PLC) based model) for simulating CR over sloping terrain. The effects of sloping terrain on single-order and diffuse scatterings are accounted for by PLC and modification of the fraction of incoming diffuse irradiance, respectively. The PLC model was validated via both Monte Carlo and remote sensing image simulations. The comparison with the Monte Carlo simulation revealed that the PLC model can capture the pattern of slope-induced reflectance distortion with high accuracy (red band: R2 = 0.88; root-mean-square error (RMSE) = 0.0045; relative RMSE (RRMSE) = 15%; near infrared response (NIR) band: R2 = 0.79; RMSE = 0.041; RRMSE = 16%). The comparison of the PLC-simulated results with remote sensing observations acquired by the Landsat8-OLI sensor revealed an accuracy similar to that with the Monte Carlo simulation (red band: R2 = 0.83; RMSE = 0.0053; RRMSE = 13%; NIR band: R2 = 0.77; RMSE = 0.023; RRMSE = 8%). To further validate the PLC model, we used it to implement topographic normalization; the results showed a large reduction in topographic effects after normalization, which implied that the PLC model captures reflectance variations caused by terrain. The PLC model provides a promising tool to improve the simulation of CR and the retrieval of biophysical variables over mountainous regions. Numéro de notice : A2017-500 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2017.2694483 En ligne : http://dx.doi.org/10.1109/TGRS.2017.2694483 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=86442 [article]