2 citations · 2 across the 1 of their papers we have counts for
8 papers
MixerSENet: A Lightweight Framework for Efficient Hyperspectral Image Classification
Mohammed Q. Alkhatib, Swalpa Kumar Roy, Ali Jamali
In this paper, a novel framework, MixerSENet, is introduced for hyperspectral image (HSI) classification, designed to address the challenges of computational efficiency and limited…
PolSAR Image Classification using a Hybrid Complex-Valued Network (HybridCVNet)
Mohammed Q. Alkhatib
Recently, convolutional neural networks (CNNs) have become popular for image classification due to their effectiveness in computer vision tasks. Now, researchers are exploring the…
Hyperspectral Image Classification using Spectral-Spatial Mixer Network
Mohammed Q. Alkhatib
This paper introduces SS-MixNet, a lightweight and effective deep learning model for hyperspectral image (HSI) classification. The architecture integrates 3D convolutional layers f…
SDF2Net: Shallow to Deep Feature Fusion Network for PolSAR Image Classification
Mohammed Q. Alkhatib, M. Sami Zitouni, Mina Al-Saad +2
Polarimetric synthetic aperture radar (PolSAR) images encompass valuable information that can facilitate extensive land cover interpretation and generate diverse output products. E…
MixerCA: An Efficient and Accurate Model for High-Performance Hyperspectral Image Classification
Mohammed Q. Alkhatib, Ali Jamali
Over the past decade, hyperspectral image (HSI) classification has drawn considerable interest due to HSIs' ability to effectively distinguish terrestrial objects by capturing deta…
ConvVitMamba: Efficient Multiscale Convolution, Transformer, and Mamba-Based Sequence modelling for Hyperspectral Image Classification
Mohammed Q. Alkhatib
Hyperspectral image (HSI) classification remains challenging due to high spectral dimensionality, redundancy, and limited labeled data. Although convolutional neural networks (CNNs…