Hyperspectral imaging instruments allow data collection in hundreds of spectral bands for the same area on the surface of the Earth. The resulting multidimensional data cube typically comprises several GBs per ight. Due to the extremely large volumes of data collected by imaging spectrometers, hyperspectral data compression, dimensionality reduction and Compressive Sensing (CS) techniques has received considerable interest in recent years. These data are usually acquired by a satellite or an airbone instrument and sent to a ground station on Earth for subsequent processing. Usually the bandwidth connection between the satellite/airborne platform and the ground station is reduced, which limits the amount of data that can be transmitted. As a result, there is a clear need for (either lossless or lossy) hyperspectral data compression techniques that can be applied on-board the imaging instrument.

This paper, presents a study of the power and time consumption and accuracy of a parallel implementation for a spectral compressive acquisition method on a Jetson TX2 platform, which is well suited to perform vector operations such as dot products. This implementation exploits the architecture at low level, using shared memory and coalesced accesses to memory. The conducted experiments have been performed to demonstrate the applicability, in terms of accuracy, time consuming and power consumption of these methods for onboard processing. The results show that by using this low power consumption GPU is it possible to obtain real-time performance with a very limited power requirement.

Palm trees are considered a symbolic agricultural heritage in the United Arab Emirates (UAE). Date palms constitute 98% of fruit trees in the UAE, which is one of the world’s top ten producers of dates. This is due to the great efforts carried out in the planting management and applying the best practices in insuring the health status as well as maintaining the production rate which indeed requires frequent mapping and monitoring. The traditional way of mapping palm trees was implemented manually which has resulted in the lack of accuracy, more time consuming and intensive human interactions. Remote sensing including satellites and Unmanned Aerial Vehicles (UAVs) has contributed to providing potential solutions in terms of large areas coverage, spatial and spectral information. In this paper, we propose an automated approach to detect and count individual palm trees from UAV using a combination of spectral and spatial analyses. The proposed approach comprises two main steps; the first step discriminates the vegetation from the surrounding objects by applying the Normalized Difference Vegetation Index (NDVI). The second step detects individual palm trees using a combination of Circular Hough Transform (CHT) and the morphological operators. Precision, recall and F-measure are calculated to assess the performance of the proposed method. Experimental results reveal that more than 95% of the palm trees in the study areas are detected correctly when compared with the manually interpreted ground truth.

Change Detection (CD) techniques applied over multitemporal multispectral or hyperspectral remote sensing images allow monitoring changes in the land use or catastrophe tracking, among other applications. A multiclass CD technique for multidimensional images that is robust in the presence of noise is presented in this paper. The technique combines fusion at feature level to perform a first change/no change labeling (binary CD) and a later stage with fusion at decision level that performs a supervised multidate classification of the changed pixels (multiclass CD) obtaining the final from-to change map. The acquisition of multidimensional images usually corrupts the original signal by adding noise. This noise can be related with natural random processes or it can be produced during the sensor operation. Additive White Gaussian Noise (AWGN) and speckle noise simulate these effects. In this paper the robustness of the proposed CD technique in noisy scenarios for these two types of noise of varying intensity is evaluated. The experimental results show that the proposed technique is more robust than other alternatives, achieving accuracies close to those obtained in the absence of noise. The proposed technique is designed to be efficiently computed in GPU, thus dealing with the high computational cost of the processing of multidimensional images.

Future onboard computing applications require significantly greater computing performance than is currently provided by standard space qualified onboard computers. Examples of such applications include onboard data analysis and (rendezvous) navigation tasks. Therefore, the German Aerospace Center is currently developing Scalable On-board Computing for Space Avionics (ScOSA). The aim of the ScOSA onboard computing platform is to deliver high performance, reliability, scalability and cost-efficiency.

To reach these properties, a distributed computing platform approach is used, by which reliable radiation hardened computing nodes (LEON3's) are combined with several high performance computing nodes (Xilinx Zynq), connected over a high-bandwidth SpaceWire network. The execution platform consists of a distributed task-based framework.

In this paper, the architecture, features and capabilities of the ScOSA onboard computing platform are presented from an application developer's view. A brief summary of the design goals and the general hardware and software architecture of the ScOSA system will be introduced. This is followed by a description of the programming model and the application interface, with a focus on how the distributed nature of the ScOSA system is handled. It will also be shown how an existing application can be integrated in the ScOSA system.

The main part of this paper will focus on the computing performance attainable from the ScOSA platform. There will be a comparison of the computing performance of an example application executed on the ScOSA system versus a standard PC. It will also be demonstrated how the performance of an application can be improved by adapting it to the distributed computing architecture of the ScOSA platform. Furthermore, a short overview of the failure detection and recovery features of the ScOSA platform are described and how they can be integrated into an application.

Precision agriculture is a farming management concept based on the use of advanced sensors for monitoring crops. Among the different alternatives to host these devices, Unmanned Aerial Vehicles (UAVs) arises as a good tradeoff between costs, spatial resolution and effective time needed during inspections, overcoming other difficulties presented in Earth Observation (EO) satellites or airborne remote sensing. In this work the authors present the decisions, mounting and problems encountered in the development of an UAV platform for precision agriculture since its conception at the early stages. This flying platform has a payload which comprises an industrial VIS/NIR hyperspectral camera, an RGB camera and a GPU. Due to the features encountered in hyperspectral sensors, hundreds of bands are able to be captured at a time, which means that it is possible to calculate several Vegetation Index (VI), in which two or more bands give information related to the vegetation properties such as vigor assessment, water status, biomass prediction and health monitoring just to name some. This study is being focused on an extensive vineyard in the island of Gran Canaria, Spain. However, the limitation of present LiPo batteries together with the inclusion of heavy payload in the UAVs impose severe restrictions in their autonomy, and hence in this work a software has been developed in order to optimize the trajectory of the drone based on the coordinates of the field to be inspected, the height of flight, speed and the percentage of battery left. This code is included in the GPU, which is also in charge of controlling the sensors and synchronize the images obtained by the RGB sensor with the lines obtained by the pushbroom hyperspectral sensor and the GPS coordinates. Preliminary images and results will be given from the first flights of this platform and also with the analysis made to some winery laves in our laboratories with our VIS/NIR/SWIR infraesturcture.

In this paper we introduce the Mars planet-wide 3D surface modelling work performed within the EU FP-7 iMars project which completed last year. In this report, we describe a fully automated multi-resolution DTM processing chain developed by the Imaging Group at UCL-MSSL, called CASP-GO based upon the heritage NASA Ames Stereo Pipeline (ASP) and the Gotcha image matcher. The CASP-GO system has been integrated into the Microsoft Azure cloud computing environment and successfully processed ~5,300 unique CTX DTMs covering ~19% of the Martian surface at 18m resolution.

The on-board compression of remote sensed hyperspectral images is an important task nowadays. One of the main difficulties is that the compression of these images must be performed in the satellite which carries the hyperspectral sensor, where the available power, time, and computational resources are limited. Moreover, it is important to achieve high compression ratios without compromising the quality of the decompressed image for the ulterior hyperspectral imaging applications. The HyperLCA compressor aims to fulfill these requirements, providing an efficient lossy compression process that allows achieving very high compression ratios while preserving the most relevant information for the subsequent hyperspectral applications. One extra advantage of the HyperLCA compressor is that it allows to fix the compression ratio to be achieved. In this work, the effect of the specified compression ratio in the computational burden of the compressor has been evaluated, also considering the rest of the input parameters and configurations of the HyperLCA compressor. The obtained results verify that the computational cost of the HyperLCA compressor decreases for higher compression ratios, with independence of the specified configuration. Additionally, the obtained results also suggest that this compressor could produce real-time compression results for on-board applications.

The special features of Stereo Imaging for LiDAR and hyperspectral sensor are multi-source data and complex algorithm, which will bring huge challenges to embedded real-time processing. To improve system performance, efficient software design is important. In this paper, based on the hardware platform with FPGA+2C6678, a hierarchical parallel model for software design is studied. In intermediate layer, an adaptive dynamic scheduling strategy and a twostage pipeline parallel architecture based on message transmission are presented, which provide efficient connection between the top application design and the bottom hardware environment. The results indicate that this model is strongly supportive for the high-performance of embedded system, and is beneficial for the open and universal design.

This paper describes the complex object distance measuring algorithm for the stereoscopic real-time onboard vision system. This complex algorithm includes two correlation-based algorithms with the different performance complexity and accuracy. The most accurate basic algorithm is a two-dimensional template matching procedure. The other basic algorithm is one-dimensional matching algorithm that used cumulative images as an input. The switching between the algorithms is based on the proposed algorithm performance indicator. This indicator is based on the mean object and background brightness comparison. The experimental research was performed using a set of artificial and natural video sequences. The proposed complex estimation algorithm showed good accuracy in all case studies. The real-time performance of the algorithm in FPGA-based vision system was demonstrated.

The Doppler spectrum of the electromagnetic (EM) scattering field from the two-dimensional dynamic sea surface is calculated based on the composite scattering model. The two-dimensional dynamic sea surfaces are generally simulated as a superposition of large-scale gravity waves and small-scale capillary ripples. On this basis, the Doppler spectrum of the EM scattering field from the two-dimensional dynamic sea surface can be calculated based on the composite scattering model, which takes both the quasi-specular scattering and Bragg scattering mechanism into account. However, due to the high resolution and real-time dynamic complexity of the dynamic sea surfaces, the calculation of the Doppler spectrum will be computationally expensive and very time-consuming. In this paper, a GPU-based algorithm of Doppler spectrum was proposed by utilizing the Tesla K80 GPUs with diverse CUDA optimization techniques. The GPU-based Doppler spectrum implementation includes five optimization strategies: first, the temporary arrays are utilized to reduce the repeat float-points operations in the loop; then the device memory was effectively exploited to reduce the data transfer time between the CPU and GPU; the fast math compiler option was also utilized to further improve the computation performance of the Doppler spectrum calculation; finally the data transfer time between the device and host memories can be effectively hide by using the asynchronous data transfer (ADT). Compared to the CPU serial program executed on Intel(R) Core(TM) i5-3450 CPU, the GPU-based Doppler spectrum implementation can achieve a significant speedup of1200× .

Satellite imagery is considered as a powerful tool to map and monitor natural and man-made resources changes globally. Moreover, it greatly contributes to boost scientific researches, supports decision making process regarding important issues and has a tremendous potential to improve economic outcomes. However, the dramatic growth in the field of communication and the internet allows satellite imagery to be easily distributed, tampered and duplicated by unauthorized users. As a result, the protection of its intellectual property has become of a great demand. To overcome this issue, the concept of “watermarking” has been introduced as a potential solution for copyright protection and ownership authentication of satellite images. The principle of watermarking here is based on embedding a pattern of invisible identification information (for instance logo) into a satellite image so that it can be detected or extracted later to identify the copyright owner. Unlike other sorts of data, satellite images are complex in nature and contain very sensitive information which can be easily degraded by inserting additional information. This means that applying watermarking technique to satellite images would affect negatively on its quality, which totally interferes with the main purpose of building high resolution satellites. Thus, a proper technique should be developed to overcome this problem. Recently, digital watermarking has been a hot research topic in academia and industry, where a wide number of schemes have been proposed and evaluated. This paper deals with the design, implementation and evaluation of a novel watermarking model for preserving the copyright and authentication of DubaiSat-2 images. The proposed algorithm must be robust against intentional and non-intentional attacks such as compression, scaling, rotation, filtering, cropping, contrast enhancement etc. In addition, the algorithm should be fragile in such a way that it should be very sensitive to any minor tampering/modification and it should precisely detect tampered area on the satellite image. Moreover, the watermark should be imperceptible to the human eye. Five quality indices are used to assess the performance of the proposed prototype, which consist of the peak signal to noise ratio (PSNR), wavelet domain signal to noise ratio (WSNR), structure similarity index measurement (SSIM), normalized correlation (NC) and mean square error (MSE).

Based on the averaging principle, an algorithm for subpixel processing of an aerospace image is developed to increase spatial resolution. With it, a high-resolution image can be gotten by processing several original shots with a lower resolution. Input information is a series of aerospace images that are offset relative to one another by half a pixel. There is one generated image with increased resolution appears as the result of the processing.

Control and refinement of the geodetic linkage according to control points are one of the stages for processing of satellite image with high spatial resolution. The present paper has suggested a solution of two issues connected with high speed search of control points. Firstly, continuous supporting coating synthesized from separate images obtained from the spacecraft Landsat-8 has been created. Existing solutions for storage of the reference data have been studied and implementation of the tile storage being significantly exceeding than open solutions GeoServer, MapServer by speed has been suggested. Secondly, a possibility of effective parallel implementation of algorithms for search of control points using modern computer technology including multicore CPU and GPU has been researched. Method to search control points allowing forming a set of control points with the specified reliability and speed greatly increasing the correlation-based algorithm speed has been suggested.

Based on the scattering effect of vortex beams, the remote sensing for aerosol particles in different atmosphere environments is investigated. In this paper we regard the aerosol particle to a coated-spherical particle other than an uniform single particle as it’s more consistent with the way it exists in the actual atmosphere. The influences of orbital angular momentum (OAM) mode, the beam width of vortex beams, the outer and inner radius on the performance on the remote sensing for coated-spherical particles in marine atmosphere are analyzed numerically. Results indicated that the runtime will sharply increase when the number of the orbital angular momentum modes is large enough. Therefore, the parallel acceleration technology is used by us in this paper to solve this problem.

The propagation properties of Terahertz (THz) electromagnetic waves in a weakly ionized dusty plasma are introduced in this study. Epstein profile is adopted to describe the non-uniformity of the electron density in this simulation. By utilizing SMM approximation method, numerical calculation results show that dusty particles in plasma significantly affects the propagation. Specifically, the dusty plasma parameters have great influence on the reflection and transmission characteristics. Meanwhile, various interesting features concerning the reflection and transmission coefficients are obtained which are extremely important for the plasma sheath around the hypersonic vehicle.

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