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Local interpretation of machine learning models in remote sensing with SHAP : the case of global climate constraints on photosynthesis phenology / Descals, Adrià (Centre de Recerca Ecològica i d'Aplicacions Forestals) ; Verger, Aleixandre (Centre de Recerca Ecològica i d'Aplicacions Forestals) ; Yin, Gaofei (Southwest Jiaotong University. Faculty of Geosciences and Environmental Engineering) ; Filella, Iolanda (Centre de Recerca Ecològica i d'Aplicacions Forestals) ; Peñuelas, Josep (Centre de Recerca Ecològica i d'Aplicacions Forestals)
Data-driven models using machine learning have been widely used in remote-sensing applications such as the retrieval of biophysical variables and land cover classification. However, these models behave as a 'black box', meaning that the relationships between the input and predicted variables are hard to interpret. [...]
2023 - 10.1080/01431161.2023.2217982
International Journal of Remote Sensing, Vol. 44, issue 10 (2023) , p. 3160-3173  
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15 p, 567.3 KB Almost fixed quality rate-allocation under unequal scaling factors for on-board remote-sensing data compression / Blanes Garcia, Ian (Universitat Autònoma de Barcelona. Departament d'Enginyeria de la Informació i de les Comunicacions) ; Albinet, Mathieu (Centre national d'études spatiales (França)) ; Camarero, Roberto (Centre national d'études spatiales (França)) ; Serra Sagristà, Joan (Universitat Autònoma de Barcelona. Departament d'Enginyeria de la Informació i de les Comunicacions)
This article extends a rate-allocation method based on the near-lossless-rate (NLR) complexity that is designed to operate on-board spacecrafts, to include support for distortion scaling factors, such as those that are needed to code multi- and hyperspectral image when a spectral transform is employed. [...]
2017 - 10.1080/01431161.2017.1334137
International journal of remote sensing, Vol. 39, issue 7 (April 2018)  

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