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Multimodal Probabilistic Latent Semantic Analysis for Sentinel-1 and Sentinel-2 Image Fusion
(IEEE, 2018-09)
Probabilistic topic models have recently shown a great potential in the remote sensing image fusion field, which is particularly helpful in land-cover categorization tasks. This letter first studies the application of ...
Depth and All-in-Focus Image Estimation in Synthetic Aperture Integral Imaging Under Partial Occlusions
(IEEE, 2018-12)
A common assumption in the integral imaging reconstruction is that a pixel will be photo-consistent if all viewpoints observed by the different cameras converge at a single point when focusing at the proper depth. However, ...
Remotely Sensed Data Fusion for Spatiotemporal Geostatistical Analysis of Forest Fire Hazard
(MDPI, 2020)
Forest fires are a natural phenomenon which might have severe implications on naturaland anthropogenic ecosystems. Future projections predict that, under a climate change environment,the fire season would be lengthier with ...
Depth estimation improvement in 3D integral imaging using an edge removal approach
(Springer, 2018)
A new depth estimation method for 3D reconstruction in a synthetic aperture integral imaging framework is presented. This
method removes the edges of the objects in the elemental images when the objects are in focus. This ...
Cocaine-Induced Preference Conditioning: a Machine Vision Perspective
(Springer, 2018)
Existing work on drug-induced synaptic changes has shown that the expression of perineuronal nets (PNNs) at the cerebellar
cortex can be regulated by cocaine-related memory. However, these studies on animals have mostly ...
On Hyperspectral Remote Sensing of Leaf Biophysical Constituents: Decoupling Vegetation Structure and Leaf Optics Using CHRIS–PROBA Data Over Crops in Barrax
(IEEE, 2014)
Scattering from a leaf responds differently at different
wavelengths to changes in leaf properties such as pigment
concentrations, chemical constituents, internal structure,
and leaf-surface properties. Radiation scattered ...
A New Deep Generative Network for Unsupervised Remote Sensing Single-Image Super-Resolution
(IEEE, 2018-11)
Super-resolution (SR) brings an excellent opportunity to improve a wide range of different remote sensing applications. SR techniques are concerned about increasing the image resolution while providing finer spatial details ...
Capsule Networks for Hyperspectral Image Classification
(IEEE, 2018-10)
Convolutional neural networks (CNNs) have recently exhibited an excellent performance in hyperspectral image classification tasks. However, the straightforward CNN-based network architecture still finds obstacles when ...
Deep Pyramidal Residual Networks for Spectral-Spatial Hyperspectral Image Classification
(IEEE, 2018-08)
Convolutional neural networks (CNNs) exhibit good performance in image processing tasks, pointing themselves as the current state-of-the-art of deep learning methods. However, the intrinsic complexity of remotely sensed ...
Hyperspectral Unmixing Based on Dual-Depth Sparse Probabilistic Latent Semantic Analysis
(IEEE, 2018-06)
This paper presents a novel approach for spectral unmixing of remotely sensed hyperspectral data. It exploits probabilistic latent topics in order to take advantage of the semantics pervading the latent topic space when ...