Jocelyn Chanussot

TL;DR: This letter presents an effective implementation of sparse representation (SR) theory to the fusion of multispectral and panchromatic images, and proposes an algorithm exploiting the details self-similarity through the scales and compares it with classical and recent pansharpening methods, both at reduced and full resolution.

TL;DR: This paper designs a new detail injection based convolutional neural network (DiCNN) framework for pansharpening with the MS details being directly formulated in end-to-end manners, where the first detail injections based CNN mines MS details through the PAN image and the MS image, and the second one utilizes only the PAN picture.

TL;DR: Experimental results on both simulated and real hyperspectral data verify the effectiveness of the RS ensemble methods for the classification of both spectral and spatial information (EMAPs) and the key parameters in RS ensembles and the computational complexity are investigated.

TL;DR: An interpretation of the derivative morphological profile (DMP) obtained with a granulometric approach is presented in terms of a fuzzy measurement of the characteristic size and contrast of each structure.

TL;DR: This work exploits temporal information in fine image sequences and solves the spatiotemporal fusion problem with a two-stream convolutional neural network called StfNet with a temporal constraint aiming to guarantee the uniqueness of the fusion result and encourages temporal consistent predictions in learning and thus leads to more realistic final results.