Cerca
View-Dependent Tessellation and Simulation of Ocean Surfaces
(Hindawi Publishing Corporation, 2014-02)
Modeling and rendering realistic ocean scenes have been thoroughly investigated for many years. Its appearance has been studied and it is possible to find very detailed simulations where a high degree of realism is achieved. ...
Object oriented data analysis under spatial correlation
(WILEY-VCH, 2014-09)
This is a discussion of the following paper: “Overview of object oriented data analysis” by J. Steve Marron and Andre ́s M. Alonso.
Low-High-Power Consumption Architectures for Deep-Learning Models Applied to Hyperspectral Image Classification
(IEEE, 2018-11)
Convolutional neural networks have emerged as an excellent tool for remotely sensed hyperspectral image (HSI) classification. Nonetheless, the high computational complexity and energy requirements of these models typically ...
On hidden Markov models and cyclic strings for shape recognition
(Elsevier, 2014)
Shape descriptions and the corresponding matching techniques must be robust to noise and
invariant to transformations for their use in recognition tasks. Most transformations are relatively
easy to handle when contours ...
Mean curvature and compactification of surfaces in a negatively curved Cartan–Hadamard manifold
(International Press, 2014)
We state and prove a Chern–Osserman-type inequality in terms
of the volume growth for complete surfaces with controlled mean
curvature properly immersed in a Cartan–Hadamard manifold N
with sectional curvatures bounded ...
Deep Unsupervised Embedding for Remotely Sensed Images Based on Spatially Augmented Momentum Contrast
(IEEE, 2020-07-14)
Convolutional neural networks (CNNs) have achieved great success when characterizing remote sensing (RS) images. However, the lack of sufficient annotated data (together with the high complexity of the RS image domain) ...
GPU Parallel Implementation of Dual-Depth Sparse Probabilistic Latent Semantic Analysis for Hyperspectral Unmixing
(IEEE, 2019-08-22)
Hyperspectral unmixing (HU) is an important task for remotely sensed hyperspectral (HS) data exploitation. It comprises the identification of pure spectral signatures (endmembers) and their corresponding fractional abundances ...
Remote Sensing Single-Image Superresolution Based on a Deep Compendium Model
(IEEE, 2019-03)
This letter introduces a novel remote sensing single-image superresolution (SR) architecture based on a deep efficient compendium model. The current deep learning-based SR trend stands for using deeper networks to improve ...
Environment-Aware Regression for Indoor Localization based on WiFi Fingerprinting
(Institute of Electrical and Electronics EngineersIEEE, 2021-04-19)
Data enrichment through interpolation or regression is a common approach to deal with sample collection for Indoor Localization with WiFi fingerprinting. This paper provides guidelines on where to collect WiFi samples, and ...
Remote Sensing Image Superresolution Using Deep Residual Channel Attention
(IEEE, 2019-07-23)
The current trend in remote sensing image superresolution (SR) is to use supervised deep learning models to effectively enhance the spatial resolution of airborne and satellite-based optical imagery. Nonetheless, the ...