Deep learning (DL) algorithms have seen a massive rise in popularity for remote-sensing
image analysis over the past few years. In this study, the major DL concepts pertinent to …

Big Data and machine learning (ML) technologies have the potential to impact many facets
of environment and water management (EWM). Big Data are information assets …

With the rapid development of deep learning technology and improvement in computing
capability, deep learning has been widely used in the field of hyperspectral image (HSI) …

In the last five years, deep learning has been introduced to tackle the hyperspectral image
(HSI) classification and demonstrated good performance. In particular, the convolutional …

Most machine learning tasks can be categorized into classification or regression problems.
Regression and classification models are normally used to extract useful geographic …

With rapid advancements in remote sensing image registration algorithms, comprehensive
imaging applications are no longer limited to single-modal remote sensing images. Instead …

Deep neural networks (DNNs) show impressive performance for hyperspectral image (HSI)
classification when abundant labeled samples are available. The problem is that HSI …

Deep learning-based methods based on convolutional neural networks (CNNs) have
demonstrated remarkable performance in hyperspectral image (HSI) classification. Most of …

Efficient and automated data acquisition techniques, as well as intelligent and accurate data
processing and analysis techniques, are essential for the advancement of precision …

In this paper, we propose an unsupervised method for hyperspectral remote sensing image
segmentation. The method exploits the mean-shift clustering algorithm that takes as input a …