Recently, unmanned aerial vehicles (UAVs) or drones have emerged as a ubiquitous and
integral part of our society. They appear in great diversity in a multiplicity of applications for …

Recently, unmanned aerial vehicles (UAVs), also known as drones, have come in a great
diversity of several applications such as military, construction, image and video map** …

Deep learning (DL) based methods represented by convolutional neural networks (CNNs)
are widely used in hyperspectral image classification (HSIC). Some of these methods have …

Abstract Purpose of the Review Many LiDAR remote sensing studies over the past decade
promised data fusion as a potential avenue to increase accuracy, spatial-temporal …

Despite success in the same-scene hyperspectral image classification (HSIC), for the cross-
scene classification, samples between source and target scenes are not drawn from the …

Abstract Three-dimensional (3D) image map** of real-world scenarios has a great
potential to provide the user with a more accurate scene understanding. This will enable …

In recent years, convolutional neural networks (CNNs)-based methods achieve cracking
performance on hyperspectral image (HSI) classification tasks, due to its hierarchical …

In the absence of effective and scalable human intervention, up to 95% of the world's
ecosystems will be affected by anthropogenic degradation by 2050. Therefore, immediate …

Monitoring and assessing wetland diversity is crucial for its accurate preservation.
Hyperspectral satellites have been proven effective for detailed investigations of plant …

Abundant spectral information endows unique advantages of hyperspectral remote sensing
images in target location and recognition. Target detection techniques locate materials or …