This PDF file contains the front matter associated with SPIE Proceedings Volume 10430 including the Title Page, Copyright information, Table of Contents, Introduction, and Conference Committee listing.

Hyperspectral imaging instruments measure hundreds of spectral bands (at different wavelength channels) for the same area of the surface of the Earth. Typically the data cube collected by these sensors comprises several GBs per flight, which have attracted attention to on-board techniques for compression. Typically these compression techniques are expensive from the computational point of view. Due to this fact, a number of Compressive Sensing and Random Projection techniques have raised as an alternative to reduce the signal size on-board the sensor. The measuring process of these techniques usually consist on performing dot products between the signal and random vectors. The Compressive Sensing process is performed directly in the optic system, however, in this paper, we propose to perform the random projection measurement process on a low power consumption Graphic Processing Unit. The experiments are conducted on a Jetson TX1 board, which is well suited to perform vector operations such as dot products. These experiments have been performed to demonstrate the applicability, in terms of accuracy and time consuming, 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.

Collaborative representation has been a popular classifier for hyperspectral image classification because it can offer excellent classification accuracy with a closed-form solution. Collaborative representation can be implemented using a dictionary with training samples of all-classes, or using class-specific sub-dictionaries. In either case, a testing pixel is assigned to the class whose training samples offer the minimum representation residual. The Collaborative Representation Optimized Classifier with Tikhonov regularization (CROCT) was developed to combine these two types of collaborative representations to achieve the balance for optimized performance. The class-specific collaborative representation involves inverse operation of matrices constructed from class-specific samples, and the all-class version requires inversion operation of the matrix constructed from all samples. In this paper, we propose a low-complexity CROCT to avoid redundant operations in all-class and class-specific collaborative representations. It can further reduce the computational cost of CROCT while maintaining its excellent classification performance.

In order to increase Signal to Noise Ratio of very high resolution Earth observation satellites images, Time Delay Integration (TDI) sensors are usually used. A TDI device synchronizes the electronic charge transfer with the satellite speed to virtually increase exposure time and thus signal to noise ratio. It is sensitive to high frequency attitude disturbances which may induce blurring effects when increasing time exposure. In this paper, we present an on-board satellite implementation of multiframe registration to improve classical TDI performances. A solution to no longer constrain the number of lines to be accumulated would be to compute, in real-time, the shift between each line and resample them before summation. A motion sensor dedicated to shift measurements would be added in the focal plane. Then a fast real-time algorithm will compute shift between two consecutives images delivered by the motion sensor. The optimization study of the motion sensor and the performances of a gradient-based algorithm on these images will be presented.

When the electron plasma is blown from the solar wind and enters into the earth atmosphere, a large number of neutral particles are excited to cause the significant event called aurora phenomenon. In this process, there are several sources of excitation including electron impact, dissociative recombination, thermal electron excitation. Particularly, auroral optical radiation produced by electron impact on oxygen atoms is investigated to explore the relationship between secondary electron energy and spectroscopic emission features. Based on the ground observations of aurora spectral images, the emission characteristics reveal the primary electron energy and flux, the basic atmosphere of species concentration and electron temperature, abundant information of the deposited particles. With the consideration that the radiations of atomic oxygen 5577 Å and 6300 Å are representative auroral spectral lines, we use numerical calculations of relative intensity ratio I(λ5577)/I(λ6300) for various energies to approximate the true ratio. A theoretical primary energy is then determined and used to estimate radiation features at other spectral bands. The best approximated primary characteristic energy is determined as 0.585. The estimated pixel lines of λ6300 and λ6364 underestimate with a factor ranging from 0.95 to 2.2 and from 0.92 to 1.41, respectively.

This paper describes the aerial object recognition algorithm for on-board and stationary vision system. Suggested algorithm is intended to recognize the objects of a specific kind using the set of the reference objects defined by 3D models. The proposed algorithm based on the outer contour descriptor building. The algorithm consists of two stages: learning and recognition. Learning stage is devoted to the exploring of reference objects. Using 3D models we can build the database containing training images by rendering the 3D model from viewpoints evenly distributed on a sphere. Sphere points distribution is made by the geosphere principle. Gathered training image set is used for calculating descriptors, which will be used in the recognition stage of the algorithm. The recognition stage is focusing on estimating the similarity of the captured object and the reference objects by matching an observed image descriptor and the reference object descriptors. The experimental research was performed using a set of the models of the aircraft of the different types (airplanes, helicopters, UAVs). The proposed orientation estimation algorithm showed good accuracy in all case studies. The real-time performance of the algorithm in FPGA-based vision system was demonstrated.

In modern hard conditions for the whole worldwide oil production industry the problem of increasing volumes of produced oil has recently become vital. This problem concerns the existing oilfields cause due to low crude oil prices the possibilities to drill new ones has almost disappeared. In this paper, we describe a novel approach of oil production enhancement, based on online procedures of all oil field data processing. The essence is that we have developed a dynamic oilfield model that allows to simultaneously handle the information, stored in tNavigator, Schlumberger ECLIPSE 100/300 and other ‘popular’ formats in parallel. The model is developed on the basis of convolutional neural networks. An example of successful industrial experiment is depicted.

Synthetic Aperture Radar (SAR) imagery requires image reproduction through successive signal processing of received data before browsing images and extracting information. The received signal data records of the ALOS-2/PALSAR-2 are stored in the onboard mission data storage and transmitted to the ground. In order to compensate the storage usage and the capacity of transmission data through the mission date communication networks, the operation duty of the PALSAR-2 is limited. This balance strongly relies on the network availability. The observation operations of the present spaceborne SAR systems are rigorously planned by simulating the mission data balance, given conflicting user demands. This problem should be solved such that we do not have to compromise the operations and the potential of the next-generation spaceborne SAR systems. One of the solutions is to compress the SAR data through onboard image reproduction and information extraction from the reproduced images. This is also beneficial for fast delivery of information products and event-driven observations by constellation. The Emergence Studio (Sōhatsu kōbō in Japanese) with Japan Aerospace Exploration Agency is developing evaluation models of FPGA-based signal processing system for onboard SAR image reproduction. The model, namely, “Fast L1 Processor (FLIP)” developed in 2016 can reproduce a 10m-resolution single look complex image (Level 1.1) from ALOS/PALSAR raw signal data (Level 1.0). The processing speed of the FLIP at 200 MHz results in twice faster than CPU-based computing at 3.7 GHz. The image processed by the FLIP is no way inferior to the image processed with 32-bit computing in MATLAB.

Hyperspectral Imaging (HI) assembles high resolution spectral information from hundreds of narrow bands across the electromagnetic spectrum, thus generating 3D data cubes in which each pixel gathers the spectral information of the reflectance of every spatial pixel. As a result, each image is composed of large volumes of data, which turns its processing into a challenge, as performance requirements have been continuously tightened. For instance, new HI applications demand real-time responses. Hence, parallel processing becomes a necessity to achieve this requirement, so the intrinsic parallelism of the algorithms must be exploited. In this paper, a spatial-spectral classification approach has been implemented using a dataflow language known as RVCCAL. This language represents a system as a set of functional units, and its main advantage is that it simplifies the parallelization process by mapping the different blocks over different processing units. The spatial-spectral classification approach aims at refining the classification results previously obtained by using a K-Nearest Neighbors (KNN) filtering process, in which both the pixel spectral value and the spatial coordinates are considered. To do so, KNN needs two inputs: a one-band representation of the hyperspectral image and the classification results provided by a pixel-wise classifier. Thus, spatial-spectral classification algorithm is divided into three different stages: a Principal Component Analysis (PCA) algorithm for computing the one-band representation of the image, a Support Vector Machine (SVM) classifier, and the KNN-based filtering algorithm. The parallelization of these algorithms shows promising results in terms of computational time, as the mapping of them over different cores presents a speedup of 2.69x when using 3 cores. Consequently, experimental results demonstrate that real-time processing of hyperspectral images is achievable.

Task of processing and analysis of obtained Earth remote sensing data on ultra-small spacecraft board is actual taking into consideration significant expenditures of energy for data transfer and low productivity of computers. Thereby, there is an issue of effective and reliable storage of the general information flow obtained from onboard systems of information collection, including Earth remote sensing data, into a specialized data base. The paper has considered peculiarities of database management system operation with the multilevel memory structure. For storage of data in data base the format has been developed that describes a data base physical structure which contains required parameters for information loading. Such structure allows reducing a memory size occupied by data base because it is not necessary to store values of keys separately. The paper has shown architecture of the relational database management system oriented into embedment into the onboard ultra-small spacecraft software. Data base for storage of different information, including Earth remote sensing data, can be developed by means of such database management system for its following processing. Suggested database management system architecture has low requirements to power of the computer systems and memory resources on the ultra-small spacecraft board. Data integrity is ensured under input and change of the structured information.

Supervised classification allows handling a wide range of remote sensing hyperspectral applications. Enhancing the spatial organization of the pixels over the image has proven to be beneficial for the interpretation of the image content, thus increasing the classification accuracy. Denoising in the spatial domain of the image has been shown as a technique that enhances the structures in the image. This paper proposes a multi-component denoising approach in order to increase the classification accuracy when a classification method is applied. It is computed on multicore CPUs and NVIDIA GPUs. The method combines feature extraction based on a 1Ddiscrete wavelet transform (DWT) applied in the spectral dimension followed by an Extended Morphological Profile (EMP) and a classifier (SVM or ELM). The multi-component noise reduction is applied to the EMP just before the classification. The denoising recursively applies a separable 2D DWT after which the number of wavelet coefficients is reduced by using a threshold. Finally, inverse 2D-DWT filters are applied to reconstruct the noise free original component. The computational cost of the classifiers as well as the cost of the whole classification chain is high but it is reduced achieving real-time behavior for some applications through their computation on NVIDIA multi-GPU platforms.

Hyperspectral imaging systems provide images in which single pixels have information from across the electromagnetic spectrum of the scene under analysis. These systems divide the spectrum into many contiguos channels, which may be even out of the visible part of the spectra. The main advantage of the hyperspectral imaging technology is that certain objects leave unique fingerprints in the electromagnetic spectrum, known as spectral signatures, which allow to distinguish between different materials that may look like the same in a traditional RGB image. Accordingly, the most important hyperspectral imaging applications are related with distinguishing or identifying materials in a particular scene. In hyperspectral imaging applications under real-time constraints, the huge amount of information provided by the hyperspectral sensors has to be rapidly processed and analysed. For such purpose, parallel hardware devices, such as Field Programmable Gate Arrays (FPGAs) are typically used. However, developing hardware applications typically requires expertise in the specific targeted device, as well as in the tools and methodologies which can be used to perform the implementation of the desired algorithms in the specific device. In this scenario, the Open Computing Language (OpenCL) emerges as a very interesting solution in which a single high-level synthesis design language can be used to efficiently develop applications in multiple and different hardware devices. In this work, the Fast Algorithm for Linearly Unmixing Hyperspectral Images (FUN) has been implemented into a Bitware Stratix V Altera FPGA using OpenCL. The obtained results demonstrate the suitability of OpenCL as a viable design methodology for quickly creating efficient FPGAs designs for real-time hyperspectral imaging applications.

Hyperspectral image unmixing is an important part of hyperspectral data analysis. The mixed pixel decomposition consists of two steps, endmember (the unique signatures of pure ground components) extraction and abundance (the proportion of each endmember in each pixel) estimation. Recently, a Discrete Particle Swarm Optimization algorithm (DPSO) was proposed for accurately extract endmembers with high optimal performance. However, the DPSO algorithm shows very high computational complexity, which makes the endmember extraction procedure very time consuming for hyperspectral image unmixing. Thus, in this paper, the DPSO endmember extraction algorithm was parallelized, implemented on the CUDA (GPU K20) platform, and evaluated by real hyperspectral remote sensing data. The experimental results show that with increasing the number of particles the parallelized version obtained much higher computing efficiency while maintain the same endmember exaction accuracy.

It has been already made, calibrated and tested a geometry normalized electromagnetic system (GNES) for metal defect examination. The GNES has an automatic data acquisition system which supporting the efficiency and accuracy of the measurement. The data will be displayed on the computer monitor as a graphic display then saved automatically in the Microsoft Excel format. The transmitter will transmit the frequency pair (FP) signals i.e. 112.5 Hz and 337.5 Hz; 112.5 Hz and 1012.5 Hz; 112.5 Hz and 3037.5 Hz; 337.5 Hz and 1012.5 Hz; 337.5 Hz and 3037.5 Hz. Simultaneous transmissions of two electromagnetic waves without distortions by the transmitter will induce an eddy current in the metal. This current, in turn, will produce secondary electromagnetic fields which are measured by the receiver together with the primary fields. Measurement of percent change of a vertical component of the fields will give the percent response caused by the metal or the defect. The response examinations were performed by the models with various type of defect for the master curves. The materials of samples as a plate were using Aluminum, Brass, and Copper. The more of the defects is the more reduction of the eddy current response. The defect contrasts were tended to decrease when the more depth of the defect position. The magnitude and phase of the eddy currents will affect the loading on the coil thus its impedance. The defect must interrupt the surface eddy current flow to be detected. Defect lying parallel to the current path will not cause any significant interruption and may not be detected. The main factors which affect the eddy current response are metal conductivity, permeability, frequency, and geometry.

The present paper has suggested a technology for search, indexing, cataloging and distribution of aerospace images on the basis of geo-information approach, cluster and spectral analysis. It has considered information and algorithmic support of the system. Functional circuit of the system and structure of the geographical data base have been developed on the basis of the geographical online portal technology. Taking into account heterogeneity of information obtained from various sources it is reasonable to apply a geoinformation platform that allows analyzing space location of objects and territories and executing complex processing of information. Geoinformation platform is based on cartographic fundamentals with the uniform coordinate system, the geographical data base, a set of algorithms and program modules for execution of various tasks. The technology for adding by particular users and companies of images taken by means of professional and amateur devices and also processed by various software tools to the array system has been suggested. Complex usage of visual and instrumental approaches allows significantly expanding an application area of Earth remote sensing data. Development and implementation of new algorithms based on the complex usage of new methods for processing of structured and unstructured data of high volumes will increase periodicity and rate of data updating. The paper has shown that application of original algorithms for search, indexing and cataloging of aerospace images will provide an easy access to information spread by hundreds of suppliers and allow increasing an access rate to aerospace images up to 5 times in comparison with current analogues.

Based on the scattering effect of optical vortex, the remote sensing for aerosol particles in marine atmosphere is investigated. The influences of orbital angular momentum (OAM) mode and beam width of Laguerre-Gaussian (LG) beam on the performance on the remote sensing for aerosol particles in marine atmosphere are analyzed numerically. The scattering properties of aerosol particles by LG beam are more sensitive to the change of relative humidity, compared to that of plane wave and Gaussian beam (GB). Remote sensing systems based on LG beam with OAM modes show superiority over traditional plane wave and Gaussian beam cases in detecting the changes in relative humidity of marine atmosphere aerosol. Results also indicate that, when the number of OAM modes p (l) increase, the Radar CrossSection (RCS) of aerosol particles alternating appear extreme values in the forward and backward scattering, because of the special ring-shaped distribution of LG beam. The forward and backward scattering of aerosol particles decrease with the increase in beam waist. As beam waist is less than the radius of aerosol particle, there exists a minimum value in the forward direction.