70 research outputs found
Multisource and Multitemporal Data Fusion in Remote Sensing
The sharp and recent increase in the availability of data captured by
different sensors combined with their considerably heterogeneous natures poses
a serious challenge for the effective and efficient processing of remotely
sensed data. Such an increase in remote sensing and ancillary datasets,
however, opens up the possibility of utilizing multimodal datasets in a joint
manner to further improve the performance of the processing approaches with
respect to the application at hand. Multisource data fusion has, therefore,
received enormous attention from researchers worldwide for a wide variety of
applications. Moreover, thanks to the revisit capability of several spaceborne
sensors, the integration of the temporal information with the spatial and/or
spectral/backscattering information of the remotely sensed data is possible and
helps to move from a representation of 2D/3D data to 4D data structures, where
the time variable adds new information as well as challenges for the
information extraction algorithms. There are a huge number of research works
dedicated to multisource and multitemporal data fusion, but the methods for the
fusion of different modalities have expanded in different paths according to
each research community. This paper brings together the advances of multisource
and multitemporal data fusion approaches with respect to different research
communities and provides a thorough and discipline-specific starting point for
researchers at different levels (i.e., students, researchers, and senior
researchers) willing to conduct novel investigations on this challenging topic
by supplying sufficient detail and references
Multisource and multitemporal data fusion in remote sensing:A comprehensive review of the state of the art
The recent, sharp increase in the availability of data captured by different sensors, combined with their considerable heterogeneity, poses a serious challenge for the effective and efficient processing of remotely sensed data. Such an increase in remote sensing and ancillary data sets, however, opens up the possibility of utilizing multimodal data sets in a joint manner to further improve the performance of the processing approaches with respect to applications at hand. Multisource data fusion has, therefore, received enormous attention from researchers worldwide for a wide variety of applications. Moreover, thanks to the revisit capability of several
Efficient Algorithms for Clustering and Interpolation of Large Spatial Data Sets
Categorizing, analyzing, and integrating large spatial data sets are of great importance in various areas such as image processing, pattern recognition, remote sensing, and life sciences. For example, NASA alone is faced with huge data sets gathered from around the globe on a daily basis to help scientists better understand our planet. Many approaches for accurately clustering, interpolating, and integrating these data sets are very computationally expensive.
The focus of my PhD thesis is on the development of efficient implementations of data clustering and interpolation methods for large spatial data sets, and the application of these methods to geostatistics and remote sensing. In particular, I have developed fast implementations of ISODATA clustering and kriging interpolation algorithms. These implementations derive their efficiency through the use of both exact and approximate computational techniques from computational geometry and scientific computing.
My work on the ISODATA clustering algorithm employs the kd-tree data structure and the filtering algorithm to speed up distance and nearest neighbor calculations. In the case of kriging interpolation, I applied techniques from scientific computing including iterative methods, tapering, fast multipole methods, and nearest neighbor searching techniques. I also present an application of kriging interpolation method to the problem of data fusion of remotely sensed data
R package for Nearest Neighbor Gaussian Process models
This paper describes and illustrates functionality of the spNNGP R package.
The package provides a suite of spatial regression models for Gaussian and
non-Gaussian point-referenced outcomes that are spatially indexed. The package
implements several Markov chain Monte Carlo (MCMC) and MCMC-free Nearest
Neighbor Gaussian Process (NNGP) models for inference about large spatial data.
Non-Gaussian outcomes are modeled using a NNGP Polya-Gamma latent variable.
OpenMP parallelization options are provided to take advantage of multiprocessor
systems. Package features are illustrated using simulated and real data sets
The application of deep learning for remote sensing of soil organic carbon stocks distribution in South Africa.
Doctoral Degree. University of KwaZulu-Natal, Pietermaritzburg.Soil organic carbon (SOC) is a vital measure for ecosystem health and offers opportunities to
understand carbon fluxes and associated implications. However, unprecedented anthropogenic
disturbances have significantly altered SOC distribution across the globe, leading to
considerable carbon losses. In addition, reliable SOC estimates, particularly over large spatial
extents remain a major challenge due to among others limited sample points, quality of
simulation data and suitable algorithms. Remote sensing (RS) approaches have emerged as a
suitable alternative to field and laboratory SOC determination, especially at large spatial extent.
Nevertheless, reliable determination of SOC distribution using RS data requires robust
analytical approaches. Compared to linear and classical machine learning (ML) models, deep
learning (DL) models offer a considerable improvement in data analysis due to their ability to
extract more representative features and identify complex spatial patterns associated with big
data. Hence, advancements in remote sensing, proliferation of big data, and deep learning
architecture offer great potential for large-scale SOC mapping. However, there is paucity in
literature on the application of DL-based remote sensing approaches for SOC prediction. To
this end, this study is aimed at exploring DL-based approaches for the remote sensing of SOC
stocks distribution across South Africa. The first objective sought to provide a synopsis of the
use of traditional neural network (TNN) and DL-based remote sensing of SOC with emphasis
on basic concepts, differences, similarities and limitations, while the second objective provided
an in-depth review of the history, utility, challenges, and prospects of DL-based remote sensing
approaches for mapping SOC. A quantitative evaluation between the use of TNN and DL
frameworks was also conducted. Findings show that majority of published literature were
conducted in the Northern Hemisphere while Africa have only four publications. Results also
reveal that most studies adopted hyperspectral data, particularly spectrometers as compared to
multispectral data. In comparison to DL (10%), TNN (90%) models were more commonly
utilized in the literature; yet, DL models produced higher median accuracy (93%) than TNN
(85%) models. The review concludes by highlighting future opportunities for retrieving SOC
from remotely sensed data using DL frameworks.
The third objective compared the accuracy of DL—deep neural network (DNN) model and a
TNN—artificial neural network (ANN), as well as other popular classical ML models that
include random forest (RF) and support vector machine (SVM), for national scale SOC
mapping using Sentinel-3 data. With a root mean square error (RMSE) of 10.35 t/ha, the DNN
model produced the best results, followed by RF (11.2 t/ha), ANN (11.6 t/ha), and SVM (13.6 t/ha). The DNN's analytical abilities, combined with its capacity to handle large amounts of
data is a key advantage over other classical ML models. Having established the superiority of
DL models over TNN and other classical models, the fourth objective focused on investigating
SOC stocks distribution across South Africa’s major land uses, using Deep Neural Networks
(DNN) and Sentinel-3 satellite data. Findings show that grasslands contributed the most to
overall SOC stocks (31.36 %), while urban vegetation contributed the least (0.04%). Results
also show that commercial (46.06 t/h) and natural (44.34 t/h) forests had better carbon
sequestration capacity than other classes. These findings provide an important guideline for
managing SOC stocks in South Africa, useful in climate change mitigation by promoting
sustainable land-use practices.
The fifth objective sought to determine the distribution of SOC within South Africa’s major
biomes using remotely sensed-topo-climatic data and Concrete Autoencoder-Deep Neural
Networks (CAE-DNN). Findings show that the CAE-DNN model (built from 26 selected
variables) had the best accuracy of the DNNs examined, with an RMSE of 7.91 t/h. Soil organic
carbon stock was also shown to be related to biome coverage, with the grassland (32.38%) and
savanna (31.28%) biomes contributing the most to the overall SOC pool in South Africa.
forests (44.12 t/h) and the Indian ocean coastal belt (43.05 t/h) biomes, despite having smaller
footprints, have the highest SOC sequestration capacity. To increase SOC storage, it is
recommended that degraded biomes be restored; however, a balance must be maintained
between carbon sequestration capability, biodiversity health, and adequate provision of
ecosystem services. The sixth objective sought to project the present SOC stocks in South
Africa into the future (i.e. 2050). Soil organic carbon variations generated by projected climate
change and land cover were mapped and analysed using a digital soil mapping (DSM)
technique combined with space-for-time substitution (SFTS) procedures over South Africa
through 2050. The potential SOC stocks variations across South Africa's major land uses were
also assessed from current (2021) to future (2050). The first part of the study uses a Deep
Neural Network (DNN) to estimate current SOC content (2021), while the second phase uses
an average of five WorldClim General Circulation Models to project SOC to the future (2050)
under four Shared Socio-economic Pathways (SSPs). Results show a general decline in
projected future SOC stocks by 2050, ranging from 4.97 to 5.38 Pg, compared to estimated
current stocks of 5.64 Pg. The findings are critical for government and policymakers in
assessing the efficacy of current management systems in South Africa. Overall, this study provides a cost-effective framework for national scale mapping of SOC
stocks, which is the largest terrestrial carbon pool using advanced DL-based remote sensing
approach. These findings are valuable for designing appropriate management strategies to
promote carbon uptake, soil quality, and measuring terrestrial ecosystem responses and
feedbacks to climate change. This study is also the first DL-based remote sensing of SOC
stocks distribution in South Africa
Development of an unsupervised remote sensing methodology of detect surface leakage from terrestrial CO2 storage sites
Imperial Users onl
Remote Sensing Monitoring of Land Surface Temperature (LST)
This book is a collection of recent developments, methodologies, calibration and validation techniques, and applications of thermal remote sensing data and derived products from UAV-based, aerial, and satellite remote sensing. A set of 15 papers written by a total of 70 authors was selected for this book. The published papers cover a wide range of topics, which can be classified in five groups: algorithms, calibration and validation techniques, improvements in long-term consistency in satellite LST, downscaling of LST, and LST applications and land surface emissivity research
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