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Found 14 papers, 6 papers with code
Dropout Concrete Autoencoder for Band Selection on HSI Scenes
Deep learning-based informative band selection methods on hyperspectral images (HSI) recently have gained intense attention to eliminate spectral correlation and redundancies.
Operational Support Estimator Networks
In this work, we propose a novel approach called Operational Support Estimator Networks (OSENs) for the support estimation task.
Hyperspectral Image Analysis with Subspace Learning-based One-Class Classification
Considering the imbalanced labels of the LULC classification problem and rich spectral information (high number of dimensions), the proposed classification approach is well-suited for HSI data.
Improved Active Fire Detection using Operational U-Nets
As a consequence of global warming and climate change, the risk and extent of wildfires have been increasing in many areas worldwide.
R2C-GAN: Restore-to-Classify GANs for Blind X-Ray Restoration and COVID-19 Classification
The proposed restoration approach achieves over 90% F1-Score which is significantly higher than the performance of any deep model.
SRL-SOA: Self-Representation Learning with Sparse 1D-Operational Autoencoder for Hyperspectral Image Band Selection
The band selection in the hyperspectral image (HSI) data processing is an important task considering its effect on the computational complexity and accuracy.
Representation Based Regression for Object Distance Estimation
To the best of our knowledge, this is the first representation-based method proposed for performing a regression task by utilizing the modified CSENs; and hence, we name this novel approach as Representation-based Regression (RbR).
Reliable COVID-19 Detection Using Chest X-ray Images
To address this need, in this study, we propose a reliable COVID-19 detection network: ReCovNet, which can discriminate COVID-19 pneumonia from 14 different thoracic diseases and healthy subjects.
Classification of Polarimetric SAR Images Using Compact Convolutional Neural Networks
In this work, to address the limitations of traditional ML and deep CNN based methods, a novel and systematic classification framework is proposed for the classification of PolSAR images, based on a compact and adaptive implementation of CNNs using a sliding-window classification approach.
COVID-19 Infection Map Generation and Detection from Chest X-Ray Images
To accomplish this, we have compiled the largest dataset with 119, 316 CXR images including 2951 COVID-19 samples, where the annotation of the ground-truth segmentation masks is performed on CXRs by a novel collaborative human-machine approach.
Advance Warning Methodologies for COVID-19 using Chest X-Ray Images
The detection of COVID-19 in early stages is not a straightforward task from chest X-ray images according to expert medical doctors because the traces of the infection are visible only when the disease has progressed to a moderate or severe stage.
Convolutional Sparse Support Estimator Based Covid-19 Recognition from X-ray Images
Any technological tool that can be provided to healthcare practitioners to save time, effort, and possibly lives has crucial importance.
Convolutional Sparse Support Estimator Network (CSEN) From energy efficient support estimation to learning-aided Compressive Sensing
Indeed, a vast majority of them use sparse signal recovery techniques to obtain support sets instead of directly mapping the non-zero locations from denser measurements (e. g., Compressively Sensed Measurements).
Reversible Privacy Preservation using Multi-level Encryption and Compressive Sensing
Security monitoring via ubiquitous cameras and their more extended in intelligent buildings stand to gain from advances in signal processing and machine learning.
