Signal subspace identification in hyperspectral imagery

Terrestrial remote sensing imagery involves the acquisition of information from the Earth's surface without physical contact with the area under study. Among the remote sensing modalities, hyperspectral imaging has recently emerged as a powerful passive technology. This technology has been widely used in the fields of urban and regional planning, water resource management, environmental monitoring, food safety, counterfeit drugs detection, oil spill and other types of chemical contamination detection, biological hazards prevention, and target detection for military and security purposes [2-9]. Hyperspectral sensors sample the reflected solar radiation from the Earth surface in the portion of the spectrum extending from the visible region through the near-infrared and mid-infrared (wavelengths between 0.3 and 2.5 µm) in hundreds of narrow (of the order of 10 nm) contiguous bands [10]. This high spectral resolution can be used for object detection and for discriminating between different objects based on their spectral xharacteristics [6]. However, this huge spectral resolution yields large amounts of data to be processed. For example, the Airbone Visible/Infrared Imaging Spectrometer (AVIRIS) [11] collects a 512 (along track) X 614 (across track) X 224 (bands) X 12 (bits) data cube in 5 s, corresponding to about 140 MBs. Similar data collection ratios are achieved by other spectrometers [12]. Such huge data volumes put stringent requirements on communications, storage, and processing. The problem of signal sbspace identification of hyperspectral data represents a crucial first step in many hypersctral processing algorithms such as target detection, change detection, classification, and unmixing. The identification of this subspace enables a correct dimensionality reduction (DR) yelding gains in data storage and retrieval and in computational time and complexity. Additionally, DR may also improve algorithms performance since it reduce data dimensionality without losses in the useful signal components. The computation of statistical estimates is a relevant example of the advantages of DR, since the number of samples required to obtain accurate estimates increases drastically with the dimmensionality of the data (Hughes phnomenon) [13].

Implementação em hardware reconfigurável de método de separação de dados hiperespetrais

Relatório do Trabalho Final de Mestrado para obtenção do grau de Mestre em Engenharia de Electrónica e Telecomunicações

GPU implementation of the simplex identification via split augmented Lagrangian

Hyperspectral imaging can be used for object detection and for discriminating between different objects based on their spectral characteristics. One of the main problems of hyperspectral data analysis is the presence of mixed pixels, due to the low spatial resolution of such images. This means that several spectrally pure signatures (endmembers) are combined into the same mixed pixel. Linear spectral unmixing follows an unsupervised approach which aims at inferring pure spectral signatures and their material fractions at each pixel of the scene. The huge data volumes acquired by such sensors put stringent requirements on processing and unmixing methods. This paper proposes an efficient implementation of a unsupervised linear unmixing method on GPUs using CUDA. The method finds the smallest simplex by solving a sequence of nonsmooth convex subproblems using variable splitting to obtain a constraint formulation, and then applying an augmented Lagrangian technique. The parallel implementation of SISAL presented in this work exploits the GPU architecture at low level, using shared memory and coalesced accesses to memory. The results herein presented indicate that the GPU implementation can significantly accelerate the method's execution over big datasets while maintaining the methods accuracy.

Unsupervised hyperspectral signal subspace identification

Hyperspectral imaging sensors provide image data containing both spectral and spatial information from the Earth surface. The huge data volumes produced by these sensors put stringent requirements on communications, storage, and processing. This paper presents a method, termed hyperspectral signal subspace identification by minimum error (HySime), that infer the signal subspace and determines its dimensionality without any prior knowledge. The identification of this subspace enables a correct dimensionality reduction yielding gains in algorithm performance and complexity and in data storage. HySime method is unsupervised and fully-automatic, i.e., it does not depend on any tuning parameters. The effectiveness of the proposed method is illustrated using simulated data based on U.S.G.S. laboratory spectra and real hyperspectral data collected by the AVIRIS sensor over Cuprite, Nevada.

Parallel hyperspectral coded aperture for compressive sensing on GPUs

The application of compressive sensing (CS) to hyperspectral images is an active area of research over the past few years, both in terms of the hardware and the signal processing algorithms. However, CS algorithms can be computationally very expensive due to the extremely large volumes of data collected by imaging spectrometers, a fact that compromises their use in applications under real-time constraints. This paper proposes four efficient implementations of hyperspectral coded aperture (HYCA) for CS, two of them termed P-HYCA and P-HYCA-FAST and two additional implementations for its constrained version (CHYCA), termed P-CHYCA and P-CHYCA-FAST on commodity graphics processing units (GPUs). HYCA algorithm exploits the high correlation existing among the spectral bands of the hyperspectral data sets and the generally low number of endmembers needed to explain the data, which largely reduces the number of measurements necessary to correctly reconstruct the original data. The proposed P-HYCA and P-CHYCA implementations have been developed using the compute unified device architecture (CUDA) and the cuFFT library. Moreover, this library has been replaced by a fast iterative method in the P-HYCA-FAST and P-CHYCA-FAST implementations that leads to very significant speedup factors in order to achieve real-time requirements. The proposed algorithms are evaluated not only in terms of reconstruction error for different compressions ratios but also in terms of computational performance using two different GPU architectures by NVIDIA: 1) GeForce GTX 590; and 2) GeForce GTX TITAN. Experiments are conducted using both simulated and real data revealing considerable acceleration factors and obtaining good results in the task of compressing remotely sensed hyperspectral data sets.

Hyperspectral signal subspace estimation

Given an hyperspectral image, the determination of the number of endmembers and the subspace where they live without any prior knowledge is crucial to the success of hyperspectral image analysis. This paper introduces a new minimum mean squared error based approach to infer the signal subspace in hyperspectral imagery. The method, termed hyperspectral signal identification by minimum error (HySime), is eigendecomposition based and it does not depend on any tuning parameters. It first estimates the signal and noise correlation matrices and then selects the subset of eigenvalues that best represents the signal subspace in the least squared error sense. The effectiveness of the proposed method is illustrated using simulated data based on U.S.G.S. laboratory spectra and real hyperspectral data collected by the AVIRIS sensor over Cuprite, Nevada.

A toolbox for hyperspectral image analysis

This paper introduces a new toolbox for hyperspectral imagery, developed under the MATLAB environment. This toolbox provides easy access to different supervised and unsupervised classification methods. This new application is also versatile and fully dynamic since the user can embody their own methods, that can be reused and shared. This toolbox, while extends the potentiality of MATLAB environment, it also provides a user-friendly platform to assess the results of different methodologies. In this paper it is also presented, under the new application, a study of several different supervised and unsupervised classification methods on real hyperspectral data.

Lossless compression of hyperspectral images

Technological progress has made a huge amount of data available at increasing spatial and spectral resolutions. Therefore, the compression of hyperspectral data is an area of active research. In somefields, the original quality of a hyperspectral image cannot be compromised andin these cases, lossless compression is mandatory. The main goal of this thesisis to provide improved methods for the lossless compression of hyperspectral images. Both prediction- and transform-based methods are studied. Two kinds of prediction based methods are being studied. In the first method the spectra of a hyperspectral image are first clustered and and an optimized linear predictor is calculated for each cluster. In the second prediction method linear prediction coefficients are not fixed but are recalculated for each pixel. A parallel implementation of the above-mentioned linear prediction method is also presented. Also,two transform-based methods are being presented. Vector Quantization (VQ) was used together with a new coding of the residual image. In addition we have developed a new back end for a compression method utilizing Principal Component Analysis (PCA) and Integer Wavelet Transform (IWT). The performance of the compressionmethods are compared to that of other compression methods. The results show that the proposed linear prediction methods outperform the previous methods. In addition, a novel fast exact nearest-neighbor search method is developed. The search method is used to speed up the Linde-Buzo-Gray (LBG) clustering method.

Hyperspectral field measurements to determine dry matter yield and nutritive value of legume-grass swards

Summary: Productivity, botanical composition and forage quality of legume-grass swards are important factors for successful arable farming in both organic and conventional farming systems. As these attributes can vary considerably within a field, a non-destructive method of detection while doing other tasks would facilitate a more targeted management of crops, forage and nutrients in the soil-plant-animal system. This study was undertaken to explore the potential of field spectral measurements for a non destructive prediction of dry matter (DM) yield, legume proportion in the sward, metabolizable energy (ME), ash content, crude protein (CP) and acid detergent fiber (ADF) of legume-grass mixtures. Two experiments were conducted in a greenhouse under controlled conditions which allowed collecting spectral measurements which were free from interferences such as wind, passing clouds and changing angles of solar irradiation. In a second step this initial investigation was evaluated in the field by a two year experiment with the same legume-grass swards. Several techniques for analysis of the hyperspectral data set were examined in this study: four vegetation indices (VIs): simple ratio (SR), normalized difference vegetation index (NDVI), enhanced vegetation index (EVI) and red edge position (REP), two-waveband reflectance ratios, modified partial least squares (MPLS) regression and stepwise multiple linear regression (SMLR). The results showed the potential of field spectroscopy and proved its usefulness for the prediction of DM yield, ash content and CP across a wide range of legume proportion and growth stage. In all investigations prediction accuracy of DM yield, ash content and CP could be improved by legume-specific calibrations which included mixtures and pure swards of perennial ryegrass and of the respective legume species. The comparison between the greenhouse and the field experiments showed that the interaction between spectral reflectance and weather conditions as well as incidence angle of light interfered with an accurate determination of DM yield. Further research is hence needed to improve the validity of spectral measurements in the field. Furthermore, the developed models should be tested on varying sites and vegetation periods to enhance the robustness and portability of the models to other environmental conditions.

FPGA-based architecture for hyperspectral endmember extraction

Hyperspectral instruments have been incorporated in satellite missions, providing data of high spectral resolution of the Earth. This data can be used in remote sensing applications, such as, target detection, hazard prevention, and monitoring oil spills, among others. In most of these applications, one of the requirements of paramount importance is the ability to give real-time or near real-time response. Recently, onboard processing systems have emerged, in order to overcome the huge amount of data to transfer from the satellite to the ground station, and thus, avoiding delays between hyperspectral image acquisition and its interpretation. For this purpose, compact reconfigurable hardware modules, such as field programmable gate arrays (FPGAs) are widely used. This paper proposes a parallel FPGA-based architecture for endmember’s signature extraction. This method based on the Vertex Component Analysis (VCA) has several advantages, namely it is unsupervised, fully automatic, and it works without dimensionality reduction (DR) pre-processing step. The architecture has been designed for a low cost Xilinx Zynq board with a Zynq-7020 SoC FPGA based on the Artix-7 FPGA programmable logic and tested using real hyperspectral data sets collected by the NASA’s Airborne Visible Infra-Red Imaging Spectrometer (AVIRIS) over the Cuprite mining district in Nevada. Experimental results indicate that the proposed implementation can achieve real-time processing, while maintaining the methods accuracy, which indicate the potential of the proposed platform to implement high-performance, low cost embedded systems, opening new perspectives for onboard hyperspectral image processing.

Hyperspectral sensing to detect the impact of herbicide drift on cotton growth and yield

Yield loss in crops is often associated with plant disease or external factors such as environment, water supply and nutrient availability. Improper agricultural practices can also introduce risks into the equation. Herbicide drift can be a combination of improper practices and environmental conditions which can create a potential yield loss. As traditional assessment of plant damage is often imprecise and time consuming, the ability of remote and proximal sensing techniques to monitor various bio-chemical alterations in the plant may offer a faster, non-destructive and reliable approach to predict yield loss caused by herbicide drift. This paper examines the prediction capabilities of partial least squares regression (PLS-R) models for estimating yield. Models were constructed with hyperspectral data of a cotton crop sprayed with three simulated doses of the phenoxy herbicide 2,4-D at three different growth stages. Fibre quality, photosynthesis, conductance, and two main hormones, indole acetic acid (IAA) and abscisic acid (ABA) were also analysed. Except for fibre quality and ABA, Spearman correlations have shown that these variables were highly affected by the chemical. Four PLS-R models for predicting yield were developed according to four timings of data collection: 2, 7, 14 and 28 days after the exposure (DAE). As indicated by the model performance, the analysis revealed that 7 DAE was the best time for data collection purposes (RMSEP = 2.6 and R2 = 0.88), followed by 28 DAE (RMSEP = 3.2 and R2 = 0.84). In summary, the results of this study show that it is possible to accurately predict yield after a simulated herbicide drift of 2,4-D on a cotton crop, through the analysis of hyperspectral data, thereby providing a reliable, effective and non-destructive alternative based on the internal response of the cotton leaves.

Estimating the colors of paintings

Observers can adjust the spectrum of illumination on paintings for optimal viewing experience. But can they adjust the colors of paintings for the best visual impression? In an experiment carried out on a calibrated color moni- tor images of four abstract paintings obtained from hyperspectral data were shown to observers that were unfamiliar with the paintings. The color volume of the images could be manipulated by rotating the volume around the axis through the average (a*, b*) point for each painting in CIELAB color space. The task of the observers was to adjust the angle of rotation to produce the best subjective impression from the paintings. It was found that the distribution of angles selected for data pooled across paintings and observers could be de- scribed by a Gaussian function centered at 10o, i.e. very close to the original colors of the paintings. This result suggest that painters are able to predict well what compositions of colors observers prefer.

Modelling plant species distribution in alpine grasslands using airborne imaging spectroscopy.

Remote sensing using airborne imaging spectroscopy (AIS) is known to retrieve fundamental optical properties of ecosystems. However, the value of these properties for predicting plant species distribution remains unclear. Here, we assess whether such data can add value to topographic variables for predicting plant distributions in French and Swiss alpine grasslands. We fitted statistical models with high spectral and spatial resolution reflectance data and tested four optical indices sensitive to leaf chlorophyll content, leaf water content and leaf area index. We found moderate added-value of AIS data for predicting alpine plant species distribution. Contrary to expectations, differences between species distribution models (SDMs) were not linked to their local abundance or phylogenetic/functional similarity. Moreover, spectral signatures of species were found to be partly site-specific. We discuss current limits of AIS-based SDMs, highlighting issues of scale and informational content of AIS data.

Sensorische Erfassung von Grünlandbiomassen als Grundlage für ein teilflächenspezifisches Management im Ökologischen Landbau

Vor dem Hintergund der Integration des wissensbasierten Managementsystems Precision Farming in den Ökologischen Landbau wurde die Umsetzung bestehender sowie neu zu entwickelnder Strategien evaluiert und diskutiert. Mit Blick auf eine im Precision Farming maßgebende kosteneffiziente Ertragserfassung der im Ökologischen Landbau flächenrelevanten Leguminosen-Grasgemenge wurden in zwei weiteren Beiträgen die Schätzgüten von Ultraschall- und Spektralsensorik in singulärer und kombinierter Anwendung analysiert. Das Ziel des Precision Farming, ein angepasstes Management bezogen auf die flächeninterne Variabilität der Standorte umzusetzen, und damit einer Reduzierung von Betriebsmitteln, Energie, Arbeit und Umwelteffekten bei gleichzeitiger Effektivitätssteigerung und einer ökonomischen Optimierung zu erreichen, deckt sich mit wesentlichen Bestrebungen im Ökogischen Landbau. Es sind vorrangig Maßnahmen zur Erfassung der Variabilität von Standortfaktoren wie Geländerelief, Bodenbeprobung und scheinbare elektrische Leitfähigkeit sowie der Ertragserfassung über Mähdrescher, die direkt im Ökologischen Landbau Anwendung finden können. Dagegen sind dynamisch angepasste Applikationen zur Düngung, im Pflanzenschutz und zur Beseitigung von Unkräutern aufgrund komplexer Interaktionen und eines eher passiven Charakters dieser Maßnahmen im Ökologischen Landbau nur bei Veränderung der Applikationsmodelle und unter Einbindung weiterer dynamischer Daten umsetzbar. Beispiele hiefür sind einzubeziehende Mineralisierungsprozesse im Boden und organischem Dünger bei der Düngemengenberechnung, schwer ortsspezifisch zuzuordnende präventive Maßnamen im Pflanzenschutz sowie Einflüsse auf bodenmikrobiologische Prozesse bei Hack- oder Striegelgängen. Die indirekten Regulationsmechanismen des Ökologischen Landbaus begrenzen daher die bisher eher auf eine direkte Wirkung ausgelegten dynamisch angepassten Applikationen des konventionellen Precision Farming. Ergänzend sind innovative neue Strategien denkbar, von denen die qualitätsbezogene Ernte, der Einsatz hochsensibler Sensoren zur Früherkennung von Pflanzenkrankheiten oder die gezielte teilflächen- und naturschutzorientierte Bewirtschaftung exemplarisch in der Arbeit vorgestellt werden. Für die häufig große Flächenanteile umfassenden Leguminosen-Grasgemenge wurden für eine kostengünstige und flexibel einsetzbare Ertragserfassung die Ultraschalldistanzmessung zur Charakterisierung der Bestandeshöhe sowie verschiedene spektrale Vegetationsindices als Schätzindikatoren analysiert. Die Vegetationsindices wurden aus hyperspektralen Daten nach publizierten Gleichungen errechnet sowie als „Normalized Difference Spectral Index“ (NDSI) stufenweise aus allen möglichen Wellenlängenkombinationen ermittelt. Die Analyse erfolgte für Ultraschall und Vegetationsindices in alleiniger und in kombinierter Anwendung, um mögliche kompensatorische Effekte zu nutzen. In alleiniger Anwendung erreichte die Ultraschallbestandeshöhe durchweg bessere Schätzgüten, als alle einzelnen Vegetationsindices. Bei den letztgenannten erreichten insbesondere auf Wasserabsorptionsbanden basierende Vegetationsindices eine höhere Schätzgenauigkeit als traditionelle Rot/Infrarot-Indices. Die Kombination beider Sensorda-ten ließ eine weitere Steigerung der Schätzgüte erkennen, insbesondere bei bestandesspezifischer Kalibration. Hierbei kompensieren die Vegetationsindices Fehlschätzungen der Höhenmessung bei diskontinuierlichen Bestandesdichtenänderungen entlang des Höhengradienten, wie sie beim Ährenschieben oder durch einzelne hochwachsende Arten verursacht werden. Die Kombination der Ultraschallbestandeshöhe mit Vegetationsindices weist das Potential zur Entwicklung kostengünstiger Ertragssensoren für Leguminosen-Grasgemenge auf. Weitere Untersuchungen mit hyperspektralen Vegetationsindices anderer Berechnungstrukturen sowie die Einbindung von mehr als zwei Wellenlängen sind hinsichtlich der Entwicklung höherer Schätzgüten notwendig. Ebenso gilt es, Kalibrierungen und Validationen der Sensorkombination im artenreichen Grasland durchzuführen. Die Ertragserfassung in den Leguminosen-Grasgemengen stellt einen wichtigen Beitrag zur Erstellung einer Ertragshistorie in den vielfältigen Fruchtfolgen des Ökologischen Landbaus dar und ermöglicht eine verbesserte Einschätzung von Produktionspotenzialen und Defizitarealen für ein standortangepasstes Management.

Caracterização espectral da água do reservatório de Itupararanga, SP, a partir de imagens hiperespectrais hyperion e análise derivativa

The current accessibility to hyperspectral images of Hyperion/EO1 orbital sensor has brought new perspectives for studies of aquatic environments for allowing the remote estimative of several optically active constituents (OACs) in water body. The changes in the composition and concentration of OACs cause different patterns of absorption and scattering of electromagnetic radiation, likely to be detected using hyperspectral data. Therefore, an investigation was conducted taking into account the spectral characterization of water of a reservoir intended for public supply (Itupararanga Reservoir), from Hyperion/EO1 images and derivative analysis technique applied to spectral curves generated. Simultaneously to the acquisition of a Hyperion/EO1 image, a field campaign was carried out to collect limnological data in situ in georeferenced points. After radiometric correction of the image, reflectance curves of pixels were extracted for each station and the curves obtained were subjected to the technique of derivative analysis, which revealed features of absorption and scattering mainly associated to the presence of algal pigments. The results obtained show the presence of phytoplankton and algal activity, matching the field observation.