Search

Detailed knowledge of both the sedimentological and ecological characteristics of the seafloor is essential when undertaking bottom-disturbing activities, but can be a challenge to obtain. Through backscatter data at different frequencies, collected with a multi-spectral multi-beam sonar, information on the structure of both the sediment surface and subsurface, and potentially also on the presence and distribution of benthic organisms, can be derived. We conducted two surveys at sea in summer 2021, in which we used an R2Sonic 2026 multi-spectral multi-beam sonar in the southern North Sea. Boxcore samples were taken to gather information on macrobenthos densities and sediment characteristics. The two studied areas were found to differ in seafloor morphology and correspondingly in the composition of the sediment composition and benthos distribution. Backscatter strength was used to classify the seafloor via the Bayesian method and via hierarchical clustering of angular variation. Relationships between the classification results for three frequencies and sediment and ecological variables were studied through redundancy analysis (RDA), for which hierarchical clustering of the angular variation in backscatter strength showed a higher model fit than Bayesian classification. We found that the density of the sand mason worm Lanice conchilega and percentages of dead shells, gravel and sand contributed most to the backscatter-based classification, with lower contributions of the percentages of mud and living bivalves. Our results suggest that acoustic backscatter can be used to delineate distinct seafloor regions, corresponding with concurrent gradients in ecological and sedimentological variables., Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Aircraft Noise and Climate Effects, Control & Operations

Acoustic classification using single-beam and multi-beam echosounders has been widely applied in characterizing seabed sediments. Although previous studies have shown a better discrimination of fine and coarse sediments using multi-spectral echosounder data, analysis regarding comprehensive seabed sediment properties is still needed. In this study, we used single-beam data of 24 kHz, as well as multi-beam data of 90 and 300 kHz to investigate the benefits of multi-spectral backscatter data in describing sediment properties including median grain size; weight percentages of gravel, sand, and mud; volume percentages of stones, shell fragments, and living bivalves; as well as density of acoustically hard animals (molluscs and the tube-building worm Lanice conchilega). We classified data of each frequency in an unsupervised manner, using K-means clustering for the single-beam echo time series and Bayesian classification for the multi-beam backscatter. Compared with the top-layer sediment properties, we found classification of 90 and 300 kHz consistent with variations of median grain size and L. conchilega density, whereas classification of 24 kHz can also be related to the percentages of shell fragments and stones. In addition, one acoustic class of 24 kHz might indicate a higher gravel content in the subsurface of the study area. Although quantitative relationships between backscatter and sediment properties are still difficult to achieve given a limited number of samples, using multi-spectral backscatter data is a potential approach to characterize seabed sediments from various perspectives., Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Aircraft Noise and Climate Effects, Control & Operations, Optical and Laser Remote Sensing

Monitoring tree phenology is important for understanding ecosystem functioning and for assessing ecosystem responses to climate change. Satellite imagery offers open-access global coverage but is restricted to forest-level analyses, due to its coarse spatial resolution. Unmanned aerial vehicle (UAV) imagery can monitor phenology at the individual tree level by utilizing a centimeter-scale resolution. Two research objectives were identified for this study: (1) to derive phenological metrics at the individual tree level, using various vegetation indices (VIs); and (2) to assess the accuracy of automatic crown delineation in a diverse ecosystem. To achieve this, fourteen multi-spectral UAV flights were performed, and the ability of the normalized difference vegetation index (NDVI), enhanced vegetation index 2 (EVI2), optimized soil-adjusted vegetation index (OSAVI), and chlorophyll index red-edge (CIre) to model seasonal phenology was assessed. A double logistic model was fitted on the VI observations for each individual tree, to derive the start of season (SOS) and end of season (EOS). Individual tree crowns were delineated automatically using marker-controlled watershed segmentation (MCWS), and the treetops were identified using a local maximum filter (LMF). Overall, the automatic segmentation performed well (F-score: 0.79, IoU: 0.58), with higher accuracies in single-species areas, while it underperformed in complex mixed forest structures. All VIs captured a strong seasonal signal for the deciduous trees and derived SOS and EOS estimates consistent with literature and ground observations. General phenological patterns included an early silver birch SOS, a quick beech budburst, and large within-species phenology variations for oak trees. Seasonal VI variation for coniferous evergreen trees was limited, and the resulting phenology estimates proved unreliable. In conclusion, these findings emphasize the capabilities of UAV imagery for individual tree crown phenology m

To facilitate the conservation of seafloor habitats and planning of offshore activities, there is a growing need for mapping marine benthos in an effective and efficient way. Acoustic data acquired by multi-beam echosounders (MBES) have been extensively used for large-scale and high-resolution seafloor characterization. A deeper understanding of the relationship between backscatter data and sediment compositions can help to identify the benthos occurrence from the MBES data. With two multi-spectral MBES datasets collected near the western Wadden Sea islands in the North Sea, we investigated the potential of mapping marine benthos through backscatter classification. Two unsupervised classification methods, i.e., Bayesian classification, which mainly exploits the backscatter strength from incident angles larger than 20°, and hierarchical clustering of the backscatter strength at different angular ranges, were employed and the results were compared. The classification results from both methods showed a good correspondence with sediment properties such as the median grain size. Moreover, based on a principal component analysis of bottom sample properties, the hierarchical clustering results indicated a better distinction between contributions from the gravel content and benthos occurrence, e.g., sand mason worm density, than Bayesian classification, through involving the backscatter angular variations. Classification for multiple frequencies, on the other hand, showed little difference regarding the relationship with bottom sample properties. Although the backscatter difference between frequencies was also found to positively correlate with certain sample properties, using multi-spectral features for acoustic classification in this study did not reveal additional information compared to single-frequency classification results., Aircraft Noise and Climate Effects, Control & Operations

Globally, terrestrial aquatic interfaces, like deltas, face a large number of ecosystem stressors. Monitoring and characterizing mechanisms of change in these areas is relevant to supporting human populations while maintaining ecological integrity. This requires high spatial and temporal resolution satellite-based observations, which have only recently become available to observe rapid changes in these dynamic systems. Sentinel-2, launched in 2016, provides a 5-day temporal resolution and 10 m spatial resolution which allows for detecting fine-scale changes in both water quality and aquatic vegetation. This dissertation is motivated by two primary interacting themes: 1) using remote sensing to study water quality, vegetation composition and phenology across aquatic ecosystems, and 2) providing knowledge and insight to support ecosystem management and informed decision making. Using the high spatial and temporal resolution capabilities of Sentinel-2, this dissertation addresses three main research objectives that correspond to three manuscripts (Chapters 1 – 3). The study site for all works is the California Sacramento San Joaquin Delta. Chapter 1 evaluates the impacts of an emergency drought barrier on turbidity and endangered fish habitat suitability. This manuscript is already published in the Journal of American Water Resource Association. The results demonstrate how high spatial, high temporal resolution satellite data enhances field observations by providing additional spatial context. Chapter 2 provides a framework for mapping aquatic vegetation, specifically distinguishing between two types of invasive floating aquatic vegetation at the genus level – water primrose and water hyacinth. Classification accuracy assessments and comparisons to maps derived from higher resolution airborne imaging spectroscopy data demonstrate that Sentinel-2 can be used to fill in inter-annual gaps in aquatic vegetation maps from summer to fall. Chapter 3 builds off Chapter 2 and reveals the phenology of water primrose may be a key component of its recent invasion success. Water primrose patches show examples of niche breadth, priority effects, and environmental plasticity relative to water hyacinth and emergent vegetation. Our findings highlight the need for spatially resolved phenology metrics. Overall, this dissertation provides insights into water quality and aquatic vegetation invasion ecology and provides improved methods for ecosystem managers to continue to investigate ecosystem stressors.

Globally, terrestrial aquatic interfaces, like deltas, face a large number of ecosystem stressors. Monitoring and characterizing mechanisms of change in these areas is relevant to supporting human populations while maintaining ecological integrity. This requires high spatial and temporal resolution satellite-based observations, which have only recently become available to observe rapid changes in these dynamic systems. Sentinel-2, launched in 2016, provides a 5-day temporal resolution and 10 m spatial resolution which allows for detecting fine-scale changes in both water quality and aquatic vegetation. This dissertation is motivated by two primary interacting themes: 1) using remote sensing to study water quality, vegetation composition and phenology across aquatic ecosystems, and 2) providing knowledge and insight to support ecosystem management and informed decision making. Using the high spatial and temporal resolution capabilities of Sentinel-2, this dissertation addresses three main research objectives that correspond to three manuscripts (Chapters 1 – 3). The study site for all works is the California Sacramento San Joaquin Delta. Chapter 1 evaluates the impacts of an emergency drought barrier on turbidity and endangered fish habitat suitability. This manuscript is already published in the Journal of American Water Resource Association. The results demonstrate how high spatial, high temporal resolution satellite data enhances field observations by providing additional spatial context. Chapter 2 provides a framework for mapping aquatic vegetation, specifically distinguishing between two types of invasive floating aquatic vegetation at the genus level – water primrose and water hyacinth. Classification accuracy assessments and comparisons to maps derived from higher resolution airborne imaging spectroscopy data demonstrate that Sentinel-2 can be used to fill in inter-annual gaps in aquatic vegetation maps from summer to fall. Chapter 3 builds off Chapter 2 and r

Remote physiological monitoring is an active area of research that extends monitoring capabilities traditionally found in a clinical setting towards the home, telehealth, and beyond. In particular, there is interest in leveraging consumer electronic devices for sensing physiological characteristics such as heart rate, heart rate variability, and blood oxygen saturation. This thesis focuses on enhancing the understanding and usage of the sensing component for these applications to improve the performance and quality of cardio-physiological monitoring. First, a close relationship between the color spaces used for video compression and the color projection planes commonly used for heart rate estimation is identified. % that results in higher compression of the physiological signal. The study demonstrates the impact of this observation on real and synthetic data to provide a foundation to guide future video coding to optimize its configurations to better preserve the heart rate signal for health related applications. Second, an investigation with a commercial-off-the-shelf (COTS) multi-spectral sensor is presented with key observations related to the sampling rate, exposure settings, and multi-channel processing. These observations will enable better usage of the sensor for future studies and data collections that leverage the more precise spectral measurements from the multi-spectral sensor compared to standard RGB cameras.

A LED based illumination system in which five Galilean collimation systems have been used is reported on. It is part of a turbulence simulator for the evaluation of on ground telescopes instrumentation developed by INTA (optics) and LIDAX (opto-mechanics) for the IAC called IACATS. The illumination requirements (some visible and infrared lines) allow the use of five different LEDs (red, green, blue and two infrareds). In order to optimize the illumination level of each wavelength, a Galilean collimating optical configuration was constructed for each wavelength channel. The IACATS instrument simulates a scene consisting of a set of different binary stars simulating the required angular separation between them, ant their spectral characteristics. As a result, a visible and infrared multi-spectral illumination system has been integrated as a part of the turbulence simulator, and the features (opto-mechanical) and illumination characteristics are described in the following lines. ©2010 SPIE'.

This study utilizes advanced wavenumber selection techniques to improve the prediction of amylose content in grounded rice samples with near-infrared spectroscopy. Four different wavenumber selection techniques, i.e. covariate selection (CovSel), variable combination population analysis (VCPA), bootstrapping soft shrinkage (BOSS) and variable combination population analysis-iteratively retains informative variables (VCPA-IRIV), were used for model optimization and key wavenumbers selection. The results of the several wavenumber selection techniques were compared with the predictions reported previously on the same data set. All the four wavenumber selection techniques improved the predictive performance of amylose in rice samples. The best performance was obtained with VCPA, where, with only 11 wavenumbers-based model, the prediction error was reduced by 19% compared to what reported previously on the same data set. The selected wavenumbers can help in development of low-cost multi-spectral sensors for amylose prediction in rice samples.

Emitted smoke composition is determined by properties of the biomass burning source and ambient ecosystem. However, conditions that mediate the partitioning of black carbon (BC) and brown carbon (BrC) formation, as well as the spatial and temporal factors that drive particle evolution, are not understood adequately for many climate and air-quality related modeling applications. In situ observations provide considerable detail about aerosol microphysical and chemical properties, although sampling is extremely limited. Satellites offer the frequent global coverage that would allow for statistical characterization of emitted and evolved smoke, but generally lack microphysical detail. However, once properly validated, data from the National Aeronautics and Space Administration (NASA) Earth Observing System’s Multi-Angle Imaging Spectroradiometer (MISR) instrument can create at least a partial picture of smoke particle properties and plume evolution. We use in situ data from the Department of Energy’s Biomass Burning Observation Project (BBOP) field campaign to assess the strengths and limitations of smoke particle retrieval results from the MISR Research Aerosol (RA) retrieval algorithm. We then use MISR to characterize wildfire smoke particle properties and to identify the relevant aging factors in several cases, to the extent possible. The RA successfully maps qualitative changes in effective particle size, light absorption, and its spectral dependence, when compared to in situ observations. By observing the entire plume uniformly, the satellite data can be interpreted in terms of smoke plume evolution, including size-selective deposition, new-particle formation, and locations within the plume where BC or BrC dominates.

Infrastructure expands rapidly in the Arctic due to industrial development. At the same time, climate change impacts are pronounced in the Arctic. Ground temperatures are, for example, increasing as well as coastal erosion. A consistent account of the current human footprint is needed in order to evaluate the impact on the environments as well as risk for infrastructure. Identification of roads and settlements with satellite data is challenging due to the size of single features and low density of clusters. Spatial resolution and spectral characteristics of satellite data are the main issues regarding their separation. The Copernicus Sentinel-1 and -2 missions recently provided good spatial coverage and at the same time comparably high pixel spacing starting with 10 m for modes available across the entire Arctic. The purpose of this study was to assess the capabilities of both, Sentinel-1 C-band Synthetic Aperture Radar (SAR) and the Sentinel-2 multispectral information for Arctic focused mapping. Settings differ across the Arctic (historic settlements versus industrial, locations on bedrock versus tundra landscapes) and reference data are scarce and inconsistent. The type of features and data scarcity demand specific classification approaches. The machine learning approaches Gradient Boosting Machines (GBM) and deep learning (DL)-based semantic segmentation have been tested. Records for the Alaskan North Slope, Western Greenland, and Svalbard in addition to high-resolution satellite data have been used for validation and calibration. Deep learning is superior to GBM with respect to users accuracy. GBM therefore requires comprehensive postprocessing. SAR provides added value in case of GBM. VV is of benefit for road identification and HH for detection of buildings. Unfortunately, the Sentinel-1 acquisition strategy is varying across the Arctic. The majority is covered in VV+VH only. DL is of benefit for road and building detection but misses large proportions of oth

Multi-spectral imaging is a powerful approach in spectral analysis of objects with spatial details. However, such approaches are most often time consuming and experimentally complex since both spectral and spatial information in two dimensions are to be resolved. Presented here is an alternative approach to thee-dimensional spectroscopy, utilizing the Compressive Sensing methodology to treat a digital image as an underrepresented system of equations. By utilizing the properties of random bases and enforcing sparsity onto a spectral two-dimensional scene using a binary mask the system of equations can be solved numerically and thus allow spectral separation of two-dimensional spectral scenes in a post-processing script. To achieve this goal, an approach is presented in this report where 1) a spectrometer setup was modified and utilized, 2) an image analysis approach based on Compressive Sensing was developed 3) the method was employed and analyzed for a number of different data sets and 4) an objective measure of reconstruction quality of spectral components was created and evaluated. The outcome of this work clearly show that Compressive Imaging has potential to become a widely used tool in multispectral imaging and that it, despite some drawbacks, is a big step forwards from conventional three-dimensional spectrometry, 3D-spektroskopi - Att blanda färg och sortera pussel Att blanda färg är något alla gjort i något läge av livet. Det bygger på att ögat har svårt att skilja på olika färger. Det har forskare löst genom att använda en spektrometer, som dock sätter andra begränsningar. I det här projektet visar vi att man kan undvika några av de begränsningarna med hjälp av en ny teknik, vilket skulle hjälpa forskning inom flera olika vetenskapliga fält genom att ge forskare ett helt nytt verktyg att arbeta med. Att blått och gult blir grönt när man blandar färg är något man får lära sig tidigt. Anledningen till att det blir grönt är för att färgpulvret är så fint att vi inte kan skilja det gula från det blå när det är blandat. Detta gör att ögat ser både blått och gult, vilket det tolkar som grönt. Inom fysiken finns ett vetenskapligt fält som kallas Spektroskopi och där är man intresserad av att se vilka olika färger saker lyser med (Spektral information), vilket betyder att ögat inte räcker till. Istället använder man sig av en spektrometer, ett instrument som delar upp ljus beroende på dess färg genom diffraktion (samma princip som solljus uppdelat i en regnbåge). Ett problem är dock att man förlorar information om hur det man tittar på ser ut, vilken form det har o.s.v. Spatiell information. Det blir lite som att lägga pussel utan att veta om motivet i förväg - att lägga ett pussel går att göra men att blanda alla bitar från flera pussel och sedan försöka lägga dem, utan att veta vilka bitar som hör till vilket pussel, är mycket svårare. Med andra ord, att titta på färger från ett väldigt litet eller enformigt objekt är inte svårt men att titta på färger från flera saker samtidigt, eller från ett objekt som lyser med olika färger på olika ställen, är mycket svårare. Det här projektet ämnar testa en metod som eventuellt skulle lösa problemet att det är svårt att få både spatiell och spektral information från ett objekt samtidigt. Genom att använda en metod från datavetenskap som ka

Multi-spectral imaging is a powerful approach in spectral analysis of objects with spatial details. However, such approaches are most often time consuming and experimentally complex since both spectral and spatial information in two dimensions are to be resolved. Presented here is an alternative approach to thee-dimensional spectroscopy, utilizing the Compressive Sensing methodology to treat a digital image as an underrepresented system of equations. By utilizing the properties of random bases and enforcing sparsity onto a spectral two-dimensional scene using a binary mask the system of equations can be solved numerically and thus allow spectral separation of two-dimensional spectral scenes in a post-processing script. To achieve this goal, an approach is presented in this report where 1) a spectrometer setup was modified and utilized, 2) an image analysis approach based on Compressive Sensing was developed 3) the method was employed and analyzed for a number of different data sets and 4) an objective measure of reconstruction quality of spectral components was created and evaluated. The outcome of this work clearly show that Compressive Imaging has potential to become a widely used tool in multispectral imaging and that it, despite some drawbacks, is a big step forwards from conventional three-dimensional spectrometry, 3D-spektroskopi - Att blanda färg och sortera pussel Att blanda färg är något alla gjort i något läge av livet. Det bygger på att ögat har svårt att skilja på olika färger. Det har forskare löst genom att använda en spektrometer, som dock sätter andra begränsningar. I det här projektet visar vi att man kan undvika några av de begränsningarna med hjälp av en ny teknik, vilket skulle hjälpa forskning inom flera olika vetenskapliga fält genom att ge forskare ett helt nytt verktyg att arbeta med. Att blått och gult blir grönt när man blandar färg är något man får lära sig tidigt. Anledningen till att det blir grönt är för att färgpulvret är så fint att vi inte kan skilja det gula från det blå när det är blandat. Detta gör att ögat ser både blått och gult, vilket det tolkar som grönt. Inom fysiken finns ett vetenskapligt fält som kallas Spektroskopi och där är man intresserad av att se vilka olika färger saker lyser med (Spektral information), vilket betyder att ögat inte räcker till. Istället använder man sig av en spektrometer, ett instrument som delar upp ljus beroende på dess färg genom diffraktion (samma princip som solljus uppdelat i en regnbåge). Ett problem är dock att man förlorar information om hur det man tittar på ser ut, vilken form det har o.s.v. Spatiell information. Det blir lite som att lägga pussel utan att veta om motivet i förväg - att lägga ett pussel går att göra men att blanda alla bitar från flera pussel och sedan försöka lägga dem, utan att veta vilka bitar som hör till vilket pussel, är mycket svårare. Med andra ord, att titta på färger från ett väldigt litet eller enformigt objekt är inte svårt men att titta på färger från flera saker samtidigt, eller från ett objekt som lyser med olika färger på olika ställen, är mycket svårare. Det här projektet ämnar testa en metod som eventuellt skulle lösa problemet att det är svårt att få både spatiell och spektral information från ett objekt samtidigt. Genom att använda en metod från datavetenskap som ka

There is an ongoing duel between military sensor development and developments in signature management. The last decade, with warfare characterized by joint expeditionary operations and asymmetry, has favored sensors. However, on account of the worsening security situation in Europe, there is now also an increasing interest in efforts to increase survivability of own military platforms. Spectral design is one of several promising technologies with extensive research potentially suitable for Low Observable platforms. It involves creating desired spectral optical responses from surfaces, in this case reducing contrast to background, by choosing suitable materials and structures. The challenge to a military decision-maker, faced with inherent uncertainties concerning the future and with limited resources, is how to choose among alternative capabilities, technologies or equipment. Correspondingly, on account of the system character of the signature attribute, researchers in technologies for signature management has difficulties communicating relevant basis for these decisions. The scope of this thesis is therefore to find and analyze patterns in decision situations involving technology or technical systems for military use, and the purpose is to propose conceptual and methodological contributions to support future decisionmaking. The technology focus is on spectral design and the application in focus is signature management of Low Observable military platforms. The research objective is addressed from a military system and capability centric perspective using methods from several disciplines in the military sciences domain. The result is synthesized from four separate studies: 1) on spectral design using systematic review of literature, 2) on military utility using a concept formation method, 3) on modeling for how to operationalize a link between spectral design and measures of military utility using methods of military operations research, and, 4) on cases of systems e

In recent years, sequential tests for detecting structural changes in time series have been adapted for deforestation monitoring using satellite data. The input time series of such sequential tests is typically a vegetation index (e.g., NDVI), which uses two or three bands and ignores all other bands. Being limited to a vegetation index will not benefit from the richer spectral information provided by newly launched satellites and will bring two bottle-necks for deforestation monitoring. Firstly, it is hard to select a suitable vegetation index a priori. Secondly, a single vegetation index is typically affected by seasonal signals, noise and other natural dynamics, which decrease its power for deforestation detection. A novel multispectral time series change monitoring method that combines dimension reduction methods with a sequential hypothesis test is proposed to address these limitations. For each location, the proposed method automatically chooses a “suitable” index for deforestation monitoring. To demonstrate our approach, we implemented it in two study areas: a dry tropical forest in Bolivia (time series length: 444) with strong seasonality and a moist tropical forest in Brazil (time series length: 225) with almost no seasonality. Our method significantly improves accuracy in the presence of strong seasonality, in particular the temporal lag between disturbance and its detection.

The availability of Internet, line-of-sight and satellite identification and surveillance information as well as low-power, low-cost embedded systems-on-a-chip and a wide range of visible to long-wave infrared cameras prompted Embry Riddle Aeronautical University to collaborate with the University of Alaska Arctic Domain Awareness Center (ADAC) in summer 2016 to prototype a camera system we call the SDMSI (Software-Defined Multi-spectral Imager). The concept for the camera system from the start has been to build a sensor node that is drop-in-place for simple roof, marine, pole-mount, or buoy-mounts. After several years of component testing, the integrated SDMSI is now being tested, first on a roof-mount at Embry Riddle Prescott. The roof-mount testing demonstrates simple installation for the high spatial, temporal and spectral resolution SDMSI. The goal is to define and develop software and systems technology to complement satellite remote sensing and human monitoring of key resources such as drones, aircraft and marine vessels in and around airports, roadways, marine ports and other critical infrastructure. The SDMSI was installed at Embry Riddle Prescott in fall 2016 and continuous recording of long-wave infrared and visible images have been assessed manually and compared to salient object detection to automatically record only frames containing objects of interest (e.g. aircraft and drones). It is imagined that ultimately users of the SDMSI can pair with it via wireless to browse salient images. Further, both ADS-B (Automatic Dependent Surveillance-Broadcast) and S-AIS (Satellite Automatic Identification System) data are envisioned to be used by the SDMSI to form expectations for observing in future tests. This paper presents the preliminary results of several experiments and compares human review with smart image processing in terms of the receiver-operator characteristic. The system design and software are open architecture, such that other researchers are enco

Achieving more timely, accurate and transparent information on the distribution and dynamics of the world’s land cover is essential to understanding the fundamental characteristics, processes and threats associated with human-nature-climate interactions. Higher resolution (~30–50 m) land cover mapping is expected to advance the understanding of the multi-dimensional interactions of the human-nature-climate system with the potentiality of representing most of the biophysical processes and characteristics of the land surface. However, mapping at 30-m resolution is complicated with existing manual techniques, due to the laborious procedures involved with the analysis and interpretation of huge volumes of satellite data. To cope with this problem, an automated technique was explored for the production of a high resolution land cover map at a national scale. The automated technique consists of the construction of a reference library by the optimum combination of the spectral, textural and topographic features and predicting the results using the optimum random forests model. The feature-rich reference library-driven automated technique was used to produce the Japan 30-m resolution land cover (JpLC-30) map of 2013–2015. The JpLC-30 map consists of seven major land cover types: water bodies, deciduous forests, evergreen forests, croplands, bare lands, built-up areas and herbaceous. The resultant JpLC-30 map was compared to the existing 50-m resolution JAXA High Resolution Land-Use and Land-Cover (JHR LULC) map with reference to Google Earth™ images. The JpLC-30 map provides more accurate and up-to-date land cover information than the JHR LULC map. This research recommends an effective utilization of the spectral, textural and topographic information to increase the accuracy of automated land cover mapping.

Traditional methods to assess the soil organic carbon (SOC) content based on soil sampling and analysis are time consuming and expensive, and the results are influenced by the sampling design. The aim of this study was to investigate the potential of UAS (Unmanned Aerial Systems) multi-spectral imagery (480-1000 nm) for estimating the SOC content in bare cultivated soils at a high spatial resolution (12 cm). We performed UAS analysis on the Hoosfield Spring Barley experiment at Rothamsted (UK) where adjacent plots with distinctly different SOC contents, due to different long-term management practices, provide a valuable resource to evaluate this approach. We acquired images (wavelength: 480-550-670-780-880-1000 nm) at an altitude of 120 m over an area of 2 ha using a multi-spectral camera mounted on an UAS. The high-resolution images captured small-scale variations at the soil surface (e.g. shadows, tillage and wheels marks). After a projection in new dimensions by a PCA, we calibrated a support vector machine regression using observations from conventional soil sampling and SOC measurements. The performance of the calibration had a R2 of 0.98 and a RMSE of 0.17%C. A cross-validation showed that the model was robust, with an average R2 of 0.95 and a RMSE of 0.21%. An external validation dataset was used to evaluate the predicted spatial patterns of SOC content and a good fit with an RMSE 0.26%C was obtained. Although this study shows that the methodology has a clear potential for use in precision agriculture or monitoring important soil properties following changes in management, we also identify and discuss its limitations and current shortcomings. © 2016 Elsevier B.V.

Availability of off-the-shelf infrared sensors combined with high definition visible cameras has made possible the construction of a Software Defined Multi-Spectral Imager (SDMSI) combining long-wave, near-infrared and visible imaging. The SDMSI requires a real-time embedded processor to fuse images and to create real-time depth maps for opportunistic uplink in sensor networks. Researchers at Embry Riddle Aeronautical University working with University of Alaska Anchorage at the Arctic Domain Awareness Center and the University of Colorado Boulder have built several versions of a low-cost drop-in-place SDMSI to test alternatives for power efficient image fusion. The SDMSI is intended for use in field applications including marine security, search and rescue operations and environmental surveys in the Arctic region. Based on Arctic marine sensor network mission goals, the team has designed the SDMSI to include features to rank images based on saliency and to provide on camera fusion and depth mapping. A major challenge has been the design of the camera computing system to operate within a 10 to 20 Watt power budget. This paper presents a power analysis of three options: 1) multi-core, 2) field programmable gate array with multi-core, and 3) graphics processing units with multi-core. For each test, power consumed for common fusion workloads has been measured at a range of frame rates and resolutions. Detailed analyses from our power efficiency comparison for workloads specific to stereo depth mapping and sensor fusion are summarized. Preliminary mission feasibility results from testing with off-the-shelf long-wave infrared and visible cameras in Alaska and Arizona are also summarized to demonstrate the value of the SDMSI for applications such as ice tracking, ocean color, soil moisture, animal and marine vessel detection and tracking. The goal is to select the most power efficient solution for the SDMSI for use on UAVs (Unoccupied Aerial Vehicles) and other drop-in-pl

Described is the development of a multispectral image labeling system with emphasis on Unmanned Ground Vehicles(UGVs). UGVs operating in unstructured environments face significant problems detecting viable paths when LIDAR is the sole source for perception. Promising advances in computer vision and machine learning has shown that multispectral imagery can be effective at detecting materials in unstructured environments [1][2][3][4][5][6]. This thesis seeks to extend previous work[6][7] by performing pixel level classification with multispectral features and texture. First the images are spatially registered to create a multispectral image cube. Visual, near infrared, shortwave infrared, and visible/near infrared polarimetric data are considered. The aligned images are then used to extract features which are fed to machine learning algorithms. The class list includes common materials present in rural and urban scenes such as vehicles, standing water, various forms of vegetation, and concrete. Experiments are conducted to explore the data requirement for a desired performance and the selection of a hyper-parameter for the textural features. A complete system is demonstrated, progressing from the data collection and labeling to the analysis of the classifier performance.

This report describes a proof of concept for multi-rotor localised surveillance using a multi-spectral sensor for plant biosecurity applications. A literature review was conducted on previous applications using airborne multispectral imaging for plant biosecurity purposes. A ready built platform was purchased and modified in order to fit and provide suitable clearance for a Tetracam Mini-MCA multispectral camera. The appropriate risk management documents were developed allowing the platform and the multi-spectral camera to be tested extensively. However, due to technical difficulties with the platform the Mini- MCA was not mounted to the platform. Once a suitable platform is developed, future extensions can be conducted into the suitability of the Mini-MCA for airborne surveillance of Australian crops.

Downy mildew (DM) caused by the biotrophic obligate oomycete Peronospora arborescens (Berk.) is one of the most economically limiting diseases of opium poppy (Papaver somniferum L.) worldwide. The first symptoms appear as small chlorotic leaf lesions, which can evolve to curled and thickened tissues that become deformed and necrotic as the disease develops. The present study explored the use of high-resolution thermal and multi-spectral imagery as an indicator of DM infection. Work was conducted in two opium poppy field plots artificially infected by P. arborescens. Airborne thermal and multi-spectral imagery were acquired at 200 mm resolution on three dates in spring of 2009 using an unmanned aerial vehicle (UAV). Leaf reflectance and transmittance spectra of DM asymptomatic and symptomatic opium poppy leaves were measured using an integrating sphere. Simulation work was conducted with the coupled PROSPECT + SAILH radiative transfer model to assess the effects of the variability found in an opium poppy plot developing a DM epidemic on the normalized difference vegetation index (NDVI) and the green/red index (R550/R670) calculated from the multi-spectral imagery. The airborne flights enabled DM detection by using image-derived canopy temperature (Tc) normalized by air temperature (Tc − Ta) and the green/red index (R550/R670). Tmin for each grid unit was calculated to estimate pure-vegetation temperature removing background and soil effects. Tmin − Ta and R550/R670 were assessed as a function of aggregated NDVI clusters to compare asymptomatic and symptomatic plants normalized by similar growth levels. Results demonstrated that Tc − Ta and the R550/R670 index were related to physiological stress caused by DM infection. In addition, Tmin − Ta was found to decrease as the NDVI increased and symptomatic plants reached significantly higher (P < 0.05) temperatures for an NDVI ≥0.6. The R550/R670 index was positively correlated with the NDVI, showing significantly higher v

The new generation of aerial photographers is using different wavelengths to sense archaeological features. This is effective but can be expensive. Here the authors use data already collected for environmental management purposes, and evaluate it for archaeological prospection on pasture. They explore the visibility of features in different seasons and their sensitivity to different wavelengths, using principal components analysis to seek out the best combinations. It turns out that this grassland gave up its secrets most readily in January, when nothing much was growing, and overall the method increased the number of known sites by a good margin. This study is of the greatest importance for developing the effective survey of the world's landscape, a quarter of which is under grass.

Accurate prostate cancer localization with noninvasive imaging can be used to guide biopsy, radiotherapy and surgery as well as to monitor disease progression [1]. Published studies have shown multispectral magnetic resonance imaging (MRI), i.e., a combination of multiple types of MR images, as a promising noninvasive imaging technique for the localization of prostate cancer.

"Both locally (San Francisco Bay Area) and nationally, evidence is mounting that particulate matter poses a serious health risk. Locally, concentrations of 10 micron particles are highest on cold nights, during the months of December and January. Analysis of the composition of these 10 micron particles suggests that a large percentage is wood smoke. Currently, there are no adequate ways to estimate the number of lit fireplaces on a given night. NASA Ames Research Center, the Naval Postgraduate School and San Francisco Bay Area Air Quality Management District performed a joint research project to determine the feasibility of using thermal imagery to detect lit fireplaces. This thesis addresses the use of an airborne multi-spectral remote sensing system to detect lit fireplaces. The focus will be on the remote sensing equipment used for fireplace detection, the development of the test plan, airborne data collection, ground truthing and data analysis." -- Abstract, http://archive.org/details/residentiallitfi109458916, Lieutenant Commander, United States Navy, Approved for public release; distribution is unlimited.

A new method has been developed to automatically extract moving vehicles and subsequently determine their speeds from a pair of QuickBird (QB) panchromatic (PAN) and multispectral (MS) images. Since the PAN and MS sensors of QB have a slight time lag (approximately 0.2 s), the speed of a moving vehicle can be determined from the difference in the positions of the vehicle observed in the PAN and MS images due to the time lag. An object-based approach can be used to extract a vehicle from the PAN image, which has a resolution of 0.6 m. However, it is difficult to accurately extract the position of a vehicle from an MS image because its resolution is 2.4 m. Thus, an area correlation method is proposed to determine the location of a vehicle from an MS image at a sub-pixel level. The speed of the moving vehicle can then be calculated by using the vehicle extraction results. This approach was tested on several parts of a QB image covering central Tokyo, Japan, and the accuracy of the results is demonstrated in this study., QC 20140922

[EN] Hyperspectral imaging systems are starting to be used as a scientific tool for food quality assessment. A typical hyperspectral image is composed of a set of a relatively wide range of monochromatic images corresponding to continuous wavelengths that normally contain redundant information or may exhibit a high degree of correlation. In addition, computation of the classifiers used to deal with the data obtained from the images can become excessively complex and time-consuming for such high-dimensional datasets, and this makes it difficult to incorporate such systems into an industry that demands standard protocols or high-speed processes. Therefore, recent works have focused on the development of new systems based on this technology that are capable of analysing quality features that cannot be inspected using visible imaging. Many of those studies have also centred on finding new statistical techniques to reduce the hyperspectral images to multispectral ones, which are easier to implement in automatic, non-destructive systems. This article reviews recent works that use hyperspectral imaging for the inspection of fruit and vegetables. It explains the different technologies available to acquire the images and their use for the non-destructive inspection of the internal and external features of these products. Particular attention is paid to the works aimed at reducing the dimensionality of the images, with details of the statistical techniques most commonly used for this task. © 2011 Springer Science+Business Media, LLC.

[EN] Hyperspectral imaging systems are starting to be used as a scientific tool for food quality assessment. A typical hyperspectral image is composed of a set of a relatively wide range of monochromatic images corresponding to continuous wavelengths that normally contain redundant information or may exhibit a high degree of correlation. In addition, computation of the classifiers used to deal with the data obtained from the images can become excessively complex and time-consuming for such high-dimensional datasets, and this makes it difficult to incorporate such systems into an industry that demands standard protocols or high-speed processes. Therefore, recent works have focused on the development of new systems based on this technology that are capable of analysing quality features that cannot be inspected using visible imaging. Many of those studies have also centred on finding new statistical techniques to reduce the hyperspectral images to multispectral ones, which are easier to implement in automatic, non-destructive systems. This article reviews recent works that use hyperspectral imaging for the inspection of fruit and vegetables. It explains the different technologies available to acquire the images and their use for the non-destructive inspection of the internal and external features of these products. Particular attention is paid to the works aimed at reducing the dimensionality of the images, with details of the statistical techniques most commonly used for this task. © 2011 Springer Science+Business Media, LLC.

A software application for automatic descriptive feature extraction from image-objects, FETEX 2.0, is presented and described in this paper. The input data include a multispectral high resolution digital image and a vector file in shapefile format containing the polygons or objects, usually extracted from a geospatial database. The design of the available descriptive features or attributes has been mainly focused on the description of agricultural parcels, providing a variety of information: spectral information from the different image bands; textural descriptors of the distribution of the intensity values based on the grey level co-occurrence matrix, the wavelet transform and a factor of edgeness; structural features describing the spatial arrangement of the elements inside the objects, based on the semivariogram curve and the Hough transform; and several descriptors of the object shape. The output file is a table that can be produced in four alternative formats, containing a vector of features for every object processed. This table of numeric values describing the objects from different points of view can be externally used as input data for any classification software. Additionally, several types of graphs and images describing the feature extraction procedure are produced, useful for interpretation and understanding the process. A test of the processing times is included, as well as an application of the program in a real parcel-based classification problem, providing some results and analyzing the applicability, the future improvement of the methodologies, and the use of additional types of data sets. This software is intended to be a dynamic tool, integrating further data and feature extraction algorithms for the progressive improvement of land use/land cover database classification and agricultural database updating processes. © 2011 Elsevier B.V.

A software application for automatic descriptive feature extraction from image-objects, FETEX 2.0, is presented and described in this paper. The input data include a multispectral high resolution digital image and a vector file in shapefile format containing the polygons or objects, usually extracted from a geospatial database. The design of the available descriptive features or attributes has been mainly focused on the description of agricultural parcels, providing a variety of information: spectral information from the different image bands; textural descriptors of the distribution of the intensity values based on the grey level co-occurrence matrix, the wavelet transform and a factor of edgeness; structural features describing the spatial arrangement of the elements inside the objects, based on the semivariogram curve and the Hough transform; and several descriptors of the object shape. The output file is a table that can be produced in four alternative formats, containing a vector of features for every object processed. This table of numeric values describing the objects from different points of view can be externally used as input data for any classification software. Additionally, several types of graphs and images describing the feature extraction procedure are produced, useful for interpretation and understanding the process. A test of the processing times is included, as well as an application of the program in a real parcel-based classification problem, providing some results and analyzing the applicability, the future improvement of the methodologies, and the use of additional types of data sets. This software is intended to be a dynamic tool, integrating further data and feature extraction algorithms for the progressive improvement of land use/land cover database classification and agricultural database updating processes. © 2011 Elsevier B.V.

Industrial quality inspection using image analysis on astaxanthin coating in aquaculture feed pellets is of great importance for automatic production control. In this study multi-spectral image analysis of pellets was performed using LDA, QDA, SNV and PCA on pixel level and mean value of pixels for each pellet. Classication using LDA or QDA on pellet mean or median values showed better results than using the pixel values or PCA.

In this study, a new methodology for the reconstruction of line features from multispectral stereo aerial images is presented. We take full advantage of the existing multispectral information in aerial images all over the steps of pre-processing and edge detection. To accurately describe the straight line segments, a principal component analysis technique is adapted. The line to line correspondences between the stereo images are established using a new pair-wise stereo matching approach. The approach involves new constraints, and the redundancy inherent in pair relations gives us a possibility to reduce the number of false matches in a probabilistic manner. The methodology is tested over three different urban test sites and provided good results for line matching and reconstruction.

This effort we acquired and registered a multi-spectral dynamic image test set with the intent of using the imagery to assess the operational effectiveness of static and dynamic image fusion techniques for a range of relevant military tasks. This paper describes the image acquisition methodology, the planned human visual performance task approach, the lessons learned during image acquisition and the plans for a future, improved image set, resolution assessment methodology and human visual performance task. © 2009 SPIE.

Surveillance and tracking systems typically use a single colour modality for their input. These systems work well in controlled conditions but often fail with low lighting, shadowing, smoke, dust, unstable backgrounds or when the foreground object is of similar colouring to the background. With advances in technology and manufacturing techniques, sensors that allow us to see into the thermal infrared spectrum are becoming more affordable. By using modalities from both the visible and thermal infrared spectra, we are able to obtain more information from a scene and overcome the problems associated with using visible light only for surveillance and tracking. Thermal images are not affected by lighting or shadowing and are not overtly affected by smoke, dust or unstable backgrounds. We propose and evaluate three approaches for fusing visual and thermal images for person tracking. We also propose a modified condensation filter to track and aid in the fusion of the modalities. We compare the proposed fusion schemes with using the visual and thermal domains on their own, and demonstrate that significant improvements can be achieved by using multiple modalities.

We present an algorithm that produces a fused false color representation of a combined multiband IR and visual imaging system for maritime applications. Multispectral IR imaging techniques are increasingly deployed in maritime operations, to detect floating mines or to find small dinghies and swimmers during search and rescue operations. However, maritime backgrounds usually contain a large amount of clutter that severely hampers the detection of small targets. Our new algorithm deploys the correlation between the target signatures in two different IR frequency bands (3-5 and 8-12 ìm) to construct a fused IR image with a reduced amount of clutter. The fused IR image is then combined with a visual image in a false color RGB representation for display to a human operator. The algorithm works as follows. First, both individual IR bands are filtered with a morphological opening top-hat transform to extract small details. Second, a common image is extracted from the two filtered IR bands, and assigned to the red channel of an RGB image. Regions of interest that appear in both IR bands remain in this common image, while most uncorrelated noise details are filtered out. Third, the visual band is assigned to the green channel and, after multiplication with a constant (typically 1.6) also to the blue channel. Fourth, the brightness and colors of this intermediate false color image are renormalized by adjusting its first order statistics to those of a representative reference scene. The result of these four steps is a fused color image, with naturalistic colors (bluish sky and grayish water), in which small targets are clearly visible. Keywords: target detection; false color fusion, small targets, clutter, image fusion

A computational camera attempts to digitally capture the essence of visual information by exploiting the synergistic combination of task-specific optics, illumination, sensors and processing. In this course we will study this emerging multi-disciplinary field at the intersection of signal processing, applied optics, computer graphics and vision, electronics, art, and online sharing through social networks. If novel cameras can be designed to sample light in radically new ways, then rich and useful forms of visual information may be recorded — beyond those present in traditional photographs. Furthermore, if computational process can be made aware of these novel imaging models, them the scene can be analyzed in higher dimensions and novel aesthetic renderings of the visual information can be synthesized. We will discuss and play with thermal cameras, multi-spectral cameras, high-speed, and 3D range-sensing cameras and camera arrays. We will learn about opportunities in scientific and medical imaging, mobile-phone based photography, camera for HCI and sensors mimicking animal eyes. We will learn about the complete camera pipeline. In several hands-on projects we will build physical imaging prototypes and understand how each stage of the imaging process can be manipulated.

A computational camera attempts to digitally capture the essence of visual information by exploiting the synergistic combination of task-specific optics, illumination, sensors and processing. In this course we will study this emerging multi-disciplinary field at the intersection of signal processing, applied optics, computer graphics and vision, electronics, art, and online sharing through social networks. If novel cameras can be designed to sample light in radically new ways, then rich and useful forms of visual information may be recorded — beyond those present in traditional photographs. Furthermore, if computational process can be made aware of these novel imaging models, them the scene can be analyzed in higher dimensions and novel aesthetic renderings of the visual information can be synthesized. We will discuss and play with thermal cameras, multi-spectral cameras, high-speed, and 3D range-sensing cameras and camera arrays. We will learn about opportunities in scientific and medical imaging, mobile-phone based photography, camera for HCI and sensors mimicking animal eyes. We will learn about the complete camera pipeline. In several hands-on projects we will build physical imaging prototypes and understand how each stage of the imaging process can be manipulated.

Knowledge of water depth is a crucial for planning military amphibious operations. Bathymetry from remote sensing with multispectral or hyperspectral imagery provides an opportunity to acquire water depth data faster than traditional hydrographic survey methods without the need to deploy a hydrographic survey vessel. It also provides a means of collecting bathymetric data covertly. This research explores two techniques for deriving bathymetry and assesses them for use by those involved in providing support to military operations. To support this aim a fieldwork campaign was undertaken in May, 2000, in northern Queensland. The fieldwork collected various inherent and apparent water optical properties and was concurrent with airborne hyperspectral imagery collection, space-based multispectral imagery collection and a hydrographic survey. The water optical properties were used to characterise the water and to understand how they affect deriving bathymetry from imagery. The hydrographic data was used to assess the performance of the bathymetric techniques. Two methods for deriving bathymetry were trialled. One uses a ratio of subsurface irradiance reflectance at two wavelengths and then tunes the result with known water depths. The other inverts the radiative transfer equation utilising the optical properties of the water to derive water depth. Both techniques derived water depth down to approximately six to seven metres. At that point the Cowley Beach waters became optically deep. Sensitivity analysis of the inversion method found that it was most sensitive to errors in vertical attenuation Kd and to errors in transforming the imagery into subsurface irradiance reflectance, R(0-) units. Both techniques require a priori knowledge to derive depth and a more sophisticated approach would be required to determine water depth without prior knowledge of the area of interest. This research demonstrates that water depth can be accurately mapped with optical techniques in less t

Spectrally derived metrics from remotely sensed data measurements have been developed to improve understanding of land cover and its dynamics. Today there are an increasing number of remote sensing systems with varying characteristics that provide a wide range of data that can be synthesized for Earth system science. A more detailed understanding is needed on how to correlate measurements between sensors. One factor that is often overlooked is the effect of a sensor's relative spectral response (RSR) on broadband spectral measurements. This study examined the variability in spectral measurements due to RSR differences between different remote sensing systems and the implications of these variations on the accuracy and consistency of the normalized difference vegetation index (NDVI). A theoretical model study and a sensor simulation study of laboratory and remotely sensed hyper-spectral data of known land cover types was developed to provide insight into the effect on NDVI due to differences in RSR measurements of various land cover signatures. This research has shown that the convolution of RSR, signature reflectance and solar irradiance in land cover measurements leads to complex interactions and generally small differences between sensor measurements. Error associated with cross-senor calibration of signature measurements and the method of band radiance conversion to reflectance also contributed to measurement discrepancies. The effect of measurement discrepancies between sensors on the accuracy and consistency of NDVI measurements of vegetation was found to be dependent on the increasing sensitivity of NDVI to decreasing band measurements. A concept of isolines of NDVI error was developed as a construct for understanding and predicting the effect of differences in band measurements between sensors on NDVI. NDVI difference of less than 0.05 can be expected for many sensor comparisons of vegetation, however, some cases will lead to higher differences. For vegetatio

Understanding the properties of time-varying illumination spectra is of importance in all applications where dynamical color changes due to changes in illumination characteristics have to be analyzed or synthesized. Examples are (dynamical) color constancy and the creation of realistic animations. In this article we show how group theoretical methods can be used to describe sequences of time changing illumination spectra with only few parameters. From the description we can also derive a differential equation that describes the illumination changes. We illustrate the method with investigations of black-body radiation and measured sequences of daylight spectra.