8 research outputs found
Remote Sensing Data Compression
A huge amount of data is acquired nowadays by different remote sensing systems installed on satellites, aircrafts, and UAV. The acquired data then have to be transferred to image processing centres, stored and/or delivered to customers. In restricted scenarios, data compression is strongly desired or necessary. A wide diversity of coding methods can be used, depending on the requirements and their priority. In addition, the types and properties of images differ a lot, thus, practical implementation aspects have to be taken into account. The Special Issue paper collection taken as basis of this book touches on all of the aforementioned items to some degree, giving the reader an opportunity to learn about recent developments and research directions in the field of image compression. In particular, lossless and near-lossless compression of multi- and hyperspectral images still remains current, since such images constitute data arrays that are of extremely large size with rich information that can be retrieved from them for various applications. Another important aspect is the impact of lossless compression on image classification and segmentation, where a reasonable compromise between the characteristics of compression and the final tasks of data processing has to be achieved. The problems of data transition from UAV-based acquisition platforms, as well as the use of FPGA and neural networks, have become very important. Finally, attempts to apply compressive sensing approaches in remote sensing image processing with positive outcomes are observed. We hope that readers will find our book useful and interestin
High-Level Synthesis of a Single/Multi-Band Optical and SAR Image Compression and Encryption Hardware Accelerator
Transmitting images from earth observation satellites to ground is a major challenge, and a compression/encryption stage is actually mandatory. Development of hardware accelerators is highly recommended, both to relieve the software from such demanding task, and to improve performance, aiming at quasi-real-time data processing. To this end, we discuss the design, development, deployment and test of a FPGA-based accelerator, featuring a lossless and lossy (near-lossless) compression, including the data encryption too. Its architecture is well suited for different image types, including single- and multi-band optical and SAR images and can be fully run-time configurable. Measured performance showed a throughput of 10 Msamples/s, in agreement with related state-of-the-art works, focused on lossless compression only
Técnicas de compresión de imágenes hiperespectrales sobre hardware reconfigurable
Tesis de la Universidad Complutense de Madrid, Facultad de Informática, leída el 18-12-2020Sensors are nowadays in all aspects of human life. When possible, sensors are used remotely. This is less intrusive, avoids interferces in the measuring process, and more convenient for the scientist. One of the most recurrent concerns in the last decades has been sustainability of the planet, and how the changes it is facing can be monitored. Remote sensing of the earth has seen an explosion in activity, with satellites now being launched on a weekly basis to perform remote analysis of the earth, and planes surveying vast areas for closer analysis...Los sensores aparecen hoy en día en todos los aspectos de nuestra vida. Cuando es posible, de manera remota. Esto es menos intrusivo, evita interferencias en el proceso de medida, y además facilita el trabajo científico. Una de las preocupaciones recurrentes en las últimas décadas ha sido la sotenibilidad del planeta, y cómo menitoirzar los cambios a los que se enfrenta. Los estudios remotos de la tierra han visto un gran crecimiento, con satélites lanzados semanalmente para analizar la superficie, y aviones sobrevolando grades áreas para análisis más precisos...Fac. de InformáticaTRUEunpu
EO-ALERT: A Novel Architecture for the Next Generation of Earth Observation Satellites Supporting Rapid Civil Alerts
Satellite Earth Observation (EO) data is ubiquitously used in many applications, providing basic services to
society, such as environment monitoring, emergency management and civilian security. Due to the increasing request
of EO products by the market, the classical EO data chain generates a severe bottleneck problem, further exacerbated
in constellations. A huge amount of EO raw data generated on-board the satellite must be transferred to ground,
slowing down the EO product availability, increasing latency, and hampering the growth of applications in
accordance with the increased user demand.
This paper provides an overview of the results achieved by the EO-ALERT project (http://eo-alert-h2020.eu/), an
H2020 European Union research activity led by DEIMOS Space. EO-ALERT proposes the definition and
development of the next-generation EO data processing chain, based on a novel flight segment architecture that
moves optimised key EO data processing elements from the ground segment to on-board the satellite, with the aim of
delivering the EO products to the end user with very low latency (quasi-real-time). EO-ALERT achieves, globally,
latencies below five minutes for EO products delivery, reaching latencies below 1 minute in some scenarios.
The proposed architecture solves the above challenges through a combination of innovations in the on-board
elements of the data chain and the communications. Namely, the architecture introduces innovative technological
solutions, including on-board reconfigurable data handling, on-board image generation and processing for the
generation of alerts (EO products) using Artificial Intelligence (AI), on-board data compression and encryption using
AI, high-speed on-board avionics, and reconfigurable high data rate communication links to ground, including a
separate chain for alerts with minimum latency and global coverage.
The paper presents the proposed architecture, its performance and hardware, considering two different user
scenarios; ship detection and extreme weather observation/nowcasting. The results show that, when implemented
using COTS components and available communication links, the proposed architecture can deliver alerts to ground
with latency lower than five minutes, for both SAR and Optical missions, demonstrating the viability of the EOALERT
concept and architecture. The paper also discusses the implementation on an avionics test bench for
testing the architecture with real EO data, with the aim of demonstrating that it can meet the requirements of the
considered scenarios in terms of detection performance and provides technologies at a high TRL (4-5). When
proven, this will open unprecedented opportunities for the exploitation of civil EO products, especially in latency
sensitive scenarios, such as disaster management
Techniques of design optimisation for algorithms implemented in software
The overarching objective of this thesis was to develop tools for parallelising, optimising,
and implementing algorithms on parallel architectures, in particular General Purpose
Graphics Processors (GPGPUs). Two projects were chosen from different application areas
in which GPGPUs are used: a defence application involving image compression, and a
modelling application in bioinformatics (computational immunology). Each project had its
own specific objectives, as well as supporting the overall research goal.
The defence / image compression project was carried out in collaboration with the Jet
Propulsion Laboratories. The specific questions were: to what extent an algorithm designed
for bit-serial for the lossless compression of hyperspectral images on-board unmanned
vehicles (UAVs) in hardware could be parallelised, whether GPGPUs could be used to
implement that algorithm, and whether a software implementation with or without GPGPU
acceleration could match the throughput of a dedicated hardware (FPGA) implementation.
The dependencies within the algorithm were analysed, and the algorithm parallelised. The
algorithm was implemented in software for GPGPU, and optimised. During the optimisation
process, profiling revealed less than optimal device utilisation, but no further optimisations
resulted in an improvement in speed. The design had hit a local-maximum of performance.
Analysis of the arithmetic intensity and data-flow exposed flaws in the standard optimisation
metric of kernel occupancy used for GPU optimisation. Redesigning the implementation
with revised criteria (fused kernels, lower occupancy, and greater data locality) led to a new
implementation with 10x higher throughput. GPGPUs were shown to be viable for on-board
implementation of the CCSDS lossless hyperspectral image compression algorithm,
exceeding the performance of the hardware reference implementation, and providing
sufficient throughput for the next generation of image sensor as well.
The second project was carried out in collaboration with biologists at the University of
Arizona and involved modelling a complex biological system – VDJ recombination involved
in the formation of T-cell receptors (TCRs). Generation of immune receptors (T cell receptor
and antibodies) by VDJ recombination is an enormously complex process, which can
theoretically synthesize greater than 1018 variants. Originally thought to be a random
process, the underlying mechanisms clearly have a non-random nature that preferentially
creates a small subset of immune receptors in many individuals. Understanding this bias is a
longstanding problem in the field of immunology. Modelling the process of VDJ
recombination to determine the number of ways each immune receptor can be synthesized,
previously thought to be untenable, is a key first step in determining how this special
population is made. The computational tools developed in this thesis have allowed
immunologists for the first time to comprehensively test and invalidate a longstanding theory
(convergent recombination) for how this special population is created, while generating the
data needed to develop novel hypothesis
Evaluation and implementation of an auto-encoder for compression of satellite images in the ScOSA project
The thesis evaluates the efficiency of various autoencoder neural networks for image compression regarding satellite imagery. The results highlight the evaluation and implementation of autoencoder architectures and the procedures required to deploy neural networks to reliable embedded devices. The developed autoencoders evaluated, targeting a ZYNQ 7020 FPGA (Field Programmable Gate Array) and a ZU7EV FPGA
Evaluation and implementation of an auto-encoder for compression of satellite images in the ScOSA project
The thesis evaluates the efficiency of various autoencoder neural networks for image compression regarding satellite imagery. The results highlight the evaluation and implementation of autoencoder architectures and the procedures required to deploy neural networks to reliable embedded devices. The developed autoencoders evaluated, targeting a ZYNQ 7020 FPGA (Field Programmable Gate Array) and a ZU7EV FPGA
Engineering a Low-Cost Remote Sensing Capability for Deep-Space Applications
Systems engineering (SE) has been a useful tool for providing objective processes to breaking down complex technical problems to simpler tasks, while concurrently generating metrics to provide assurance that the solution is fit-for-purpose. Tailored forms of SE have also been used by cubesat mission designers to assist in reducing risk by providing iterative feedback and key artifacts to provide managers with the evidence to adjust resources and tasking for success. Cubesat-sized spacecraft are being planned, built and in some cases, flown to provide a lower-cost entry point for deep-space exploration. This is particularly important for agencies and countries with lower space exploration budgets, where specific mission objectives can be used to develop tailored payloads within tighter constraints, while also returning useful scientific results or engineering data.
In this work, a tailored SE tradespace approach was used to help determine how a 6 unit (6U) cubesat could be built from commercial-off-the-shelf (COTS)-based components and undertake remote sensing missions near Mars or near-Earth Asteroids. The primary purpose of these missions is to carry a hyperspectral sensor sensitive to 600-800nm wavelengths (hereafter defined as “red-edge”), that will investigate mineralogy characteristics commonly associated with oxidizing and hydrating environments in red-edge. Minerals of this type remain of high interest for indicators of present or past habitability for life, or active geologic processes. Implications of operating in a deep-space environment were considered as part of engineering constraints of the design, including potential reduction of available solar energy, changes in thermal environment and background radiation, and vastly increased communications distances.
The engineering tradespace analysis identified realistic COTS options that could satisfy mission objectives for the 6U cubesat bus while also accommodating a reasonable degree of risk. The exception was the communication subsystem, in which case suitable capability was restricted to one particular option. This analysis was used to support an additional trade investigation into the type of sensors that would be most suitable for building the red-edge hyperspectral payload. This was in part constrained by ensuring not only that readily available COTS sensors were used, but that affordability, particularly during a geopolitical environment that was affecting component supply surety and access to manufacturing facilities, was optimized. It was found that a number of sensor options were available for designing a useful instrument, although the rapid development and life-of-type issues with COTS sensors restricted the ability to obtain useful metrics on their performance in the space environment.
Additional engineering testing was conducted by constructing hyperspectral sensors using sensors popular in science, technology, engineering and mathematics (STEM) contexts. Engineering and performance metrics of the payload containing the sensors was conducted; and performance of these sensors in relevant analogous environments. A selection of materials exhibiting spectral phenomenology in the red-edge portion of the spectrum was used to produce metrics on the performance of the sensors. It was found that low-cost cameras were able to distinguish between most minerals, although they required a wider spectral range to do so. Additionally, while Raspberry Pi cameras have been popular with scientific applications, a low-cost camera without a Bayer filter markedly improved spectral sensitivity. Consideration for space-environment testing was also trialed in additional experiments using high-altitude balloons to reach the near-space environment. The sensor payloads experienced conditions approximating the surface of Mars, and results were compared with Landsat 7, a heritage Earth sensing satellite, using a popular vegetation index. The selected Raspberry Pi cameras were able to provide useful results from near-space that could be compared with space imagery.
Further testing incorporated comparative analysis of custom-built sensors using readily available Raspberry Pi and astronomy cameras, and results from Mastcam and Mastcam/z instruments currently on the surface of Mars. Two sensor designs were trialed in field settings possessing Mars-analogue materials, and a subset of these materials were analysed using a laboratory-grade spectro-radiometer. Results showed the Raspberry Pi multispectral camera would be best suited for broad-scale indications of mineralogy that could be targeted by the pushbroom sensor. This sensor was found to possess a narrower spectral range than the Mastcam and Mastcam/z but was sensitive to a greater number of bands within this range. The pushbroom sensor returned data on spectral phenomenology associated with attributes of Minerals of the type found on Mars. The actual performance of the payload in appropriate conditions was important to provide critical information used to risk reduce future designs. Additionally, the successful outcomes of the trials reduced risk for their application in a deep space environment.
The SE and practical performance testing conducted in this thesis could be developed further to design, build and fly a hyperspectral sensor, sensitive to red-edge wavelengths, on a deep-space cubesat mission. Such a mission could be flown at reasonable cost yet return useful scientific and engineering data