Gustau Camps-Valls

TL;DR: It is argued that contextual cues should be used as part of deep learning to gain further process understanding of Earth system science problems, improving the predictive ability of seasonal forecasting and modelling of long-range spatial connections across multiple timescales.

TL;DR: A tutorial/overview cross section of some relevant hyperspectral data analysis methods and algorithms, organized in six main topics: data fusion, unmixing, classification, target detection, physical parameter retrieval, and fast computing.

TL;DR: This paper assesses performance of regularized radial basis function neural networks (Reg-RBFNN), standard support vector machines (SVMs), kernel Fisher discriminant (KFD) analysis, and regularized AdaBoost (reg-AB) in the context of hyperspectral image classification.

TL;DR: This framework of composite kernels demonstrates enhanced classification accuracy as compared to traditional approaches that take into account the spectral information only, flexibility to balance between the spatial and spectral information in the classifier, and computational efficiency.

TL;DR: An efficient version of the RLDA recently presented by Ye to cope with critical ill-posed hyperspectral image classification problems is introduced in the remote sensing community and several LDA-based classifiers are compared theoretically and experimentally with the standard LDA and theRLDA.