[HTML][HTML] Advances in Earth observation and machine learning for quantifying blue carbon

TD Pham, NT Ha, N Saintilan, A Skidmore… - Earth-Science …, 2023 - Elsevier
Blue carbon ecosystems (mangroves, seagrasses and saltmarshes) are highly productive
coastal habitats, and are considered some of the most carbon-dense ecosystems on the …

Recent developments in parallel and distributed computing for remotely sensed big data processing

Z Wu, J Sun, Y Zhang, Z Wei… - Proceedings of the …, 2021 - ieeexplore.ieee.org
This article gives a survey of state-of-the-art methods for processing remotely sensed big
data and thoroughly investigates existing parallel implementations on diverse popular high …

Real-time big data analytical architecture for remote sensing application

MMU Rathore, A Paul, A Ahmad… - IEEE journal of …, 2015 - ieeexplore.ieee.org
The assets of remote senses digital world daily generate massive volume of real-time data
(mainly referred to the term “Big Data”), where insight information has a potential …

Stable mean-shift algorithm and its application to the segmentation of arbitrarily large remote sensing images

J Michel, D Youssefi, M Grizonnet - IEEE Transactions on …, 2014 - ieeexplore.ieee.org
Segmentation of real-world remote sensing images is challenging because of the large size
of those data, particularly for very high resolution imagery. However, a lot of high-level …

[HTML][HTML] Fine-scale characterization of irrigated and rainfed croplands at national scale using multi-source data, random forest, and deep learning algorithms

KS Mpakairi, T Dube, M Sibanda, O Mutanga - ISPRS Journal of …, 2023 - Elsevier
Abstract Knowledge of the extent and distribution of irrigated and rainfed croplands is critical
in providing the necessary baseline data for enhancing agricultural efficiency and making …

Monitoring land-cover changes: A machine-learning perspective

A Karpatne, Z Jiang, RR Vatsavai… - … and Remote Sensing …, 2016 - ieeexplore.ieee.org
Monitoring land-cover changes is of prime importance for the effective planning and
management of critical, natural and man-made resources. The growing availability of remote …

A survey machine learning based object detections in an image

S Mohmmad, R Dadi, D Kothandaraman… - AIP Conference …, 2022 - pubs.aip.org
One of the research emergence as per studied problem on the image processing based
computer vision is that object detection in a image with bounding boxes. This complicated …

In-memory parallel processing of massive remotely sensed data using an apache spark on hadoop yarn model

W Huang, L Meng, D Zhang… - IEEE Journal of Selected …, 2016 - ieeexplore.ieee.org
MapReduce has been widely used in Hadoop for parallel processing larger-scale data for
the last decade. However, remote-sensing (RS) algorithms based on the programming …

Mapping annual forest cover by fusing PALSAR/PALSAR-2 and MODIS NDVI during 2007–2016

Y Zhang, F Ling, GM Foody, Y Ge, DS Boyd, X Li… - Remote Sensing of …, 2019 - Elsevier
Abstract Advanced Land Observing Satellite (ALOS) Phased Arrayed L-band Synthetic
Aperture Radar (PALSAR) HH and HV polarization data were used previously to produce …

GPU implementation of an automatic target detection and classification algorithm for hyperspectral image analysis

S Bernabe, S López, A Plaza… - IEEE Geoscience and …, 2012 - ieeexplore.ieee.org
The detection of (moving or static) targets in remotely sensed hyperspectral images often
requires real-time responses for swift decisions that depend upon high computing …