74 research outputs found
A Comprehensive Survey of Deep Learning in Remote Sensing: Theories, Tools and Challenges for the Community
In recent years, deep learning (DL), a re-branding of neural networks (NNs),
has risen to the top in numerous areas, namely computer vision (CV), speech
recognition, natural language processing, etc. Whereas remote sensing (RS)
possesses a number of unique challenges, primarily related to sensors and
applications, inevitably RS draws from many of the same theories as CV; e.g.,
statistics, fusion, and machine learning, to name a few. This means that the RS
community should be aware of, if not at the leading edge of, of advancements
like DL. Herein, we provide the most comprehensive survey of state-of-the-art
RS DL research. We also review recent new developments in the DL field that can
be used in DL for RS. Namely, we focus on theories, tools and challenges for
the RS community. Specifically, we focus on unsolved challenges and
opportunities as it relates to (i) inadequate data sets, (ii)
human-understandable solutions for modelling physical phenomena, (iii) Big
Data, (iv) non-traditional heterogeneous data sources, (v) DL architectures and
learning algorithms for spectral, spatial and temporal data, (vi) transfer
learning, (vii) an improved theoretical understanding of DL systems, (viii)
high barriers to entry, and (ix) training and optimizing the DL.Comment: 64 pages, 411 references. To appear in Journal of Applied Remote
Sensin
Detection And Classification Of Buried Radioactive Materials
This dissertation develops new approaches for detection and classification of buried radioactive materials. Different spectral transformation methods are proposed to effectively suppress noise and to better distinguish signal features in the transformed space. The contributions of this dissertation are detailed as follows. 1) Propose an unsupervised method for buried radioactive material detection. In the experiments, the original Reed-Xiaoli (RX) algorithm performs similarly as the gross count (GC) method; however, the constrained energy minimization (CEM) method performs better if using feature vectors selected from the RX output. Thus, an unsupervised method is developed by combining the RX and CEM methods, which can efficiently suppress the background noise when applied to the dimensionality-reduced data from principle component analysis (PCA). 2) Propose an approach for buried target detection and classification, which applies spectral transformation followed by noisejusted PCA (NAPCA). To meet the requirement of practical survey mapping, we focus on the circumstance when sensor dwell time is very short. The results show that spectral transformation can alleviate the effects from spectral noisy variation and background clutters, while NAPCA, a better choice than PCA, can extract key features for the following detection and classification. 3) Propose a particle swarm optimization (PSO)-based system to automatically determine the optimal partition for spectral transformation. Two PSOs are incorporated in the system with the outer one being responsible for selecting the optimal number of bins and the inner one for optimal bin-widths. The experimental results demonstrate that using variable bin-widths is better than a fixed bin-width, and PSO can provide better results than the traditional Powell’s method. 4) Develop parallel implementation schemes for the PSO-based spectral partition algorithm. Both cluster and graphics processing units (GPU) implementation are designed. The computational burden of serial version has been greatly reduced. The experimental results also show that GPU algorithm has similar speedup as cluster-based algorithm
Hyperspectral Unmixing on Multicore DSPs: Trading Off Performance for Energy
Wider coverage of observation missions will increase
onboard power restrictions while, at the same time, pose higher
demands from the perspective of processing time, thus asking for
the exploration of novel high-performance and low-power processing
architectures. In this paper, we analyze the acceleration
of spectral unmixing, a key technique to process hyperspectral
images, on multicore architectures. To meet onboard processing
restrictions, we employ a low-power Digital Signal Processor
(DSP), comparing processing time and energy consumption with
those of a representative set of commodity architectures. We
demonstrate that DSPs offer a fair balance between ease of
programming, performance, and energy consumption, resulting
in a highly appealing platform to meet the restrictions of current
missions if onboard processing is required
A comprehensive approach for the efficient acquisition and processing of hyperspectral images and sequence
Programa Oficial de Doctorado en Computación. 5009P01[Abstract]
Despite the scientific and technological developments achieved during the last
two decades in the hyperspectral field, some methodological, operational and
conceptual issues have restricted the progress, promotion and popular dissemination
of this technology. These shortcomings include the specialized knowledge
required for the acquisition of hyperspectral images, the shortage of publicly accessible
hyperspectral image repositories with reliable ground truth images or
the lack of methodologies that allow for the adaptation of algorithms to particular
user or application processing needs.
The work presented here has the objective of contributing to the hyperspectral
field with procedures for the automatic acquisition of hyperspectral scenes,
including the hardware adaptation of our own imagers and the development
of methods for the calibration and correction of the hyperspectral datacubes,
the creation of a publicly available hyperspectral repository of well categorized
and labeled images and the design and implementation of novel computational
intelligence based processing techniques that solve typical issues related to the
segmentation and denoising of hyperspectral images as well as sequences of them
taking into account their temporal evolution.[Resumen]
A pesar de los desarrollos tecnológicos y científicos logrados en el campo hiperespectral
durante las dos últimas décadas, alg\mas limitaciones de tipo metodológico,
operacional y conceptual han restringido el progreso, difusión y popularización
de esta tecnología, entre ellas, el conocimiento especializado requerido
en la adquisición de imágenes hiperespectrales, la carencia de repositorios de
imágenes hiperespectrales con etiquetados fiables y de acceso público o la falta
de metodologías que posibiliten la adaptación de algoritmos a usuarios o necesidades
de procesamiento concretas.
Este trabajo doctoral tiene el objetivo de contribuir al campo hiperespectral
con procedimientos para la adquisición automática de escenas hiperespectrales,
incluyendo la adaptación hardware de cámaras hiperespectrales propias
y el desarrollo de métodos para la calibración y corrección de cubos de datos
hiperespectrales; la creación de un repositorio hiperespectral de acceso público
con imágenes categorizadas y con verdades de terreno fiables; y el diseño e
implementación de técnicas de procesamiento basadas en inteligencia computacional
para la resolución de problemas típicamente relacionados con las tareas
de segmentación y eliminación de ruido en imágenes estáticas y secuencias de
imágenes hiperespectrales teniendo en consideración su evolución temporal.[Resumo]
A pesar dos desenvolvementos tecnolóxicos e científicos logrados no campo
hiperespectral durante as dúas últimas décadas, algunhas lirrútacións de tipo
metodolóxico¡ operacional e conceptual restrinxiron o progreso) difusión e popularización
desta tecnoloxía, entre elas, o coñecemento especializado requirido
na adquisición de imaxes hiperespectrales¡ a carencia de repositorios de irnaxes
hiperespectrales con etiquetaxes fiables e de acceso público ou a falta de metodoloxías
que posibiliten a adaptación de algoritmos a usuarios ou necesidades de
procesamento concretas.
Este traballo doutoral ten o obxectívo de contribuir ao campo hiperespectral
con procedementos para a adquisición automática de eicenas hiperespectrais,
incluíndo a adaptación hardware de cámaras hiperespectrales propias e o desenvolvemento
de métodos para a calibración e corrección de cubos de datos hiperespectrais;
a creación dun repositorio hiperespectral de acceso público con imaxes
categorizadas e con verdades de terreo fiables; e o deseño e implementación de
técnicas de procesamento baseadas en intelixencia computacional para a resolución
de problemas tipicamente relacionado~ coas tarefas de segmentación e
eliminación de ruído en imaxes estáticas e secuencias de imaxes hiperespectrai~
tendo en consideración a súa evolución temporal
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