Remote Sensing (Hyperspectral and Multispectral) Research Papers (original) (raw)

Ce travail a pour but d'etudier l'interet de l'utilisation de la teledetection aeroportee, soit des images CASI (Compact Airborne Spectral Imager) a haute resolution spatiale et spectrale, pour l'amelioration des connaissances sur les recifs coralliens de la Reunion. En raison du faible developpement des structures coralliennes de l'ile, nous avons etudie le recif corallien de la Saline. Nous avons choisi une methode d'echantillonnage non aleatoire sur une base de 118 points d'echantillonnage pour une zone d'etude de 342,2 ha (12 classes). Nous avons reconnu sur le terrain les communautes a dominance corallienne en chacun des points echantillons. Puis, l'analyse de l'image multibande du platier recifal a permis d'elaborer, dans un SIG (systeme d'information geographique), une carte thematique. Cette carte a permis d'identifier la repartition des communautes et leur degradation sur le platier recifal de la Saline ainsi que de quanti...

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- Cartography, Coral Reef, Remote Sensing (Hyperspectral and Multispectral)
- by Michael Crosby
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- Earth Sciences, Geology, Spatial Analysis, Limnology
- by Alejandro Hinojosa
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- Engineering, Cartography, Landscape Architecture, Environmental Management
- by Glen Green
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- Image Processing, Science, Multidisciplinary, Rain forest

The IKONOS satellite simultaneously collects 1-m panchromatic and 4-m multispectral images, providing the commercial and scientific community with a dramatic improvement in spatial resolution over previously available satellite imagery.... more

- by Rick Oleszczuk
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- Cartography, Remote Sensing, Photogrammetry, Image Analysis
- by Miguel Velez-Reyes and +1
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- Algorithms, Remote Sensing, Image Analysis, Information Extraction
- by Steven Sader
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- Forestry, Methodology, Time Series, Image Analysis

Introduction The variety and abundance of qualitative characteristics of agricultural products have been the main reason for the development of different types of Non-Destructive Methods (NDTs). Quality control of these products is one of the most important tasks in manufacturing processes. The use of control and automation has become more widespread, and new approaches provide opportunities for production competition through new technologies. The need to intensify the quality and quantity of a product leads to the use of advanced automated machines. Machine tools are increasingly becoming more automated and less reliant on human factors. Nowadays, visual machine technology and image processing techniques are widely used in the industry, particularly common in product quality control, robot guidance and self-guided mechanisms.

- by Vania V Estrela
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- Artificial Intelligence, Computer Vision, Agronomy, Image Processing

Diminishing returns of archaeological crop marks in lowland areas from traditional observer‐directed visible spectrum aerial survey with standard photographic cameras highlights a need to explore alternative approaches to maintain the effectiveness of survey programmes. Developments in low‐cost multispectral remote sensing have in part been driven by the growth of precision agriculture and, whilst contributing to the intensification of land use, these technologies may offer new spectral and temporal capacities for detecting, recording and monitoring historic landscapes. However, there are significant challenges to the deployment of such approaches, not least the costs of data acquisition and uncertainty about the best conditions for data collection. This study assesses the effectiveness of the Parrot Sequoia, a relatively low‐cost multispectral sensor recently developed for agricultural applications, for the detection of crop marks to inform archaeological survey. A series of observations were taken with the sensor mounted on an unmanned aerial vehicle (UAV) at Ravenshall, Fife, Scotland, between April and July 2017. The resulting reflectance maps are compared to red, green and blue (RGB) photographs taken with a Nikon D800E digital camera during seven light aircraft surveys, with the aim of testing the sensors' comparative ability to record crop mark developments over time. The contrast in reflectance between vegetation samples growing over buried archaeological remains and the surrounding field was assessed through separability in regional histogram values across different image band combinations. Separable values indicative of crop marks were found in both the multispectral and RGB results from June 2017 onwards. Several vegetation index (VI) maps, particularly the Simple Ratio (SR) and Normalised Difference Vegetation Index (NDVI), were found to be effective for distinguishing crop marks in the multispectral results. The Sequoia is a cost‐effective sensor offering improved spectral resolution over the RGB photographs, showing potential for subtle crop mark detection across compact study areas.

- by Charles Moriarty and +1
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- Aerial Archaeology, Agriculture, Survey (Archaeological Method & Theory), Archaeological survey

This research focuses on the application of HyMap airborne hyperspectral data and ASTER satellite multispectral data to mineral exploration and lithologic mapping in the Arctic regions of central East Greenland. The study area is the Kap Simpson complex in central East Greenland. The Kap Simpson complex is one of the largest exposed Palaeogene felsic complexes of East Greenland. It has been the target of several mineral exploration projects. The analysis of the HyMap data produced a detailed picture of the spatial distribution of the alteration minerals in the Kap Simpson complex, unavailable from field-based studies in the area. The analysis of the ASTER data produced mineral maps which due to the moderate spatial and spectral resolution of the ASTER imagery can be useful for reconnaissance level mineral exploration. Colour composites of the mean normalized ASTER thermal bands display lithological information and detected a large felsic igneous intrusion that has not been shown on the recently compiled geological maps of the area. The results of this research have considerable potential to evaluate the use of hyperspectral and multispectral remote sensing for geological purposes in the Arctic regions of central East Greenland.

- by Erfin Elly
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- Mechanical Engineering, Aerospace Engineering, Remote Sensing, Space

Satellite‐based imagers have been delivering data products that offer new possibilities to study (archaeological) landscapes via remote recording of the Earth's spectral reflectance (and to some extent thermal emittance) since the 1970s. Although much declassified panchromatic spy footage and some more recent commercial multispectral imagery has already been used with great success in archaeological research, it is expected that the importance and utility of multi‐ and hyperspectral (together denoted spectral) spaceborne imagery will become more important in the near future. This is mainly due to the constant and simultaneous improvements in the spatial and spectral resolving power of these imaging systems. Together with the temporal and radiometric properties, these four characteristics define any remotely‐sensing spaceborne spectral imaging platform. Thus, these parameters allow one to decide on the archaeological usability of the footage acquired.

- by Ross Lunetta and +2
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- Topography, Geomatic Engineering, Vegetation, Remote Sensing System Engineering

It is of great interest in exploiting texture information for classification of hyperspectral imagery (HSI) at high spatial resolution. In this paper, a classification paradigm to exploit rich texture information of HSI is proposed. The proposed framework employs local binary patterns (LBPs) to extract local image features, such as edges, corners, and spots. Two levels of fusion (i.e., feature-level fusion and decision-level fusion) are applied to the extracted LBP features along with global Gabor features and original spectral features, where feature-level fusion involves concatenation of multiple features before the pattern classification process while decision-level fusion performs on probability outputs of each individual classification pipeline and soft-decision fusion rule is adopted to merge results from the classifier ensemble. Moreover, the efficient extreme learning machine with a very simple structure is employed as the classifier. Experimental results on several HSI data sets demonstrate that the proposed framework is superior to some traditional alternatives.

Archaeologists often rely on passive airborne optical remote sensing to deliver some of the core data for (European) landscape archaeology projects. Despite the many technological and theoretical evolutions that have characterised this field of archaeology, the dominant aerial photographic surveys, but also less common approaches to archaeological airborne reconnaissance, still suffer from many inherent biases imposed by sub-par sampling strategies, cost, instrument availability and post-processing issues. This paper starts with the concept of landscape (archaeology) and uses it to frame archaeological airborne remote sensing. After introducing the need for bias reduction when sampling an already distorted archaeological population and expanding on the ‘theory-neutral’ claim of aerial survey, the paper presents eight key characteristics that all have the potential to increase or decrease the subjectivity and bias when collecting airborne optical imagery with passive sensors. Within this setting, the paper then offers some technological-methodological reflection on the various passive airborne optical imaging solutions that landscape archaeology has come to rely upon in the past decades. In doing so, it calls into question the effectiveness and suitability of these highly subjective approaches for landscape archaeology. Finally, the paper proposes a new, more objective approach to aerial optical image acquisition with passive sensors. In the discussion, the text argues that the suggested exhaustive (or total) airborne sampling of the preserved archaeological record might transcend particular theoretical paradigms, while the data generated could span various interpretational perspectives and oppositional analytical approaches in landscape archaeology.

In recent years drones have become an innovative and powerful tool for the documentation of archaeological sites and for the identification of buried remains. In this study, firstly, the Greek-Etruscan sanctuary of Gravisca (Tarquinia, Italy) was documented using drone aerial photogrammetry. Secondly, it has been possible to accurately identify around the site the marks of buried structures analysing the health status of vegetation through the multispectral remote sensing and applying vegetation indexes. The identified marks may allow to hypothesize the presence of an Isis sanctuary in Gravisca, already attested by epigraphic sources.

- by FOLD&R the Journal of Fasti Online and +1
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- Archaeology, Archeologia, Isis Cult, Aerial Photogrammetry
- by Jose Jesus Pasapera
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- Geophysics, Information Retrieval, Remote Sensing, Meteorology

The problem of recursively approximating motion resulting from the Optical Flow (OF) in video thru Total Least Squares (TLS) techniques is addressed. TLS method solves an inconsistent system Gu=z , with G and z in error due to temporal/spatial derivatives, and nonlinearity, while the Ordinary Least Squares (OLS) model has noise only in z. Sources of difficulty involve the non-stationarity of the field, the ill-posedness, and the existence of noise in the data. Three ways of applying the TLS with different noise conjectures to the end problem are observed. First, the classical TLS (cTLS) is introduced, where the entries of the error matrices of each row of the augmented matrix [G;z] have zero mean and the same standard deviation. Next, the Generalized Total Least Squares (GTLS) is defined to provide a more stable solution, but it still has some problems. The Generalized Scaled TLS (GSTLS) has G and z tainted by different sources of additive zero-mean Gaussian noise and scaling [G;z] by nonsingular D and E, that is, D[G;z]E makes the errors iid with zero mean and a diagonal covariance matrix. The scaling is computed from some knowledge on the error distribution to improve the GTLS estimate. For moderate levels of additive noise, GSTLS outperforms the OLS, and the GTLS approaches. Although any TLS variant requires more computations than the OLS, it is still applicable with proper scaling of the data matrix.

- by Steven Acker
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- Geophysics, Algorithms, Forestry, Remote Sensing

Water supply devices enable afforestation in dry climates and on poor lands with generally high success rates. Previous survival analyses have been based on the direct observation of each individual plant in the field, which entails considerable effort and costs. This study provides a low-cost method to discriminate between live and dead plants in afforestations that can efficiently replace traditional field inspections through the use of UAVs equipped with RGB and NIR sensors. The method combines the use of a conventional camera with an identical camera modified to record the NIR channel. Survival analysis was performed with digital image processing techniques based on calculated indices associated with plant vigour and PCA-based decorrelation. The method yielded results with high global accuracy rates (∼96.2%) with a minimum percentage of doubtful plants, even in young plantations (seedlings < 30 cm tall). The procedure could be particularly useful in hazardous areas.

- by Marcos Arza-García
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- Forestry, Multispectral Imaging, Unmanned Aircraft Systems, Survival Analysis

In the article is made an overview of the application of different satellite remote sensing methods and technologies in detection of the abiotic stress in coniferous landscapes. The review paper is discussing in short the application of different remote sensing technologies such as: satellite multispectral and infrared (thermal), imaging spectrometry and fluorescence imaging. The studied period spans from the onset of the satellite remote sensing in the 1960s until present day. In conclusion, are drawn requirements for the perspective technologies in satellite remote sensing which should address the fast and reliable detection of the manifestation of abiotic stress in coniferous landscapes.

Fluvial avulsion is an important process in the dynamics of the riverscapes and plays a key role in the drainage network evolution in lowland areas, also influencing past and present social processes and economic activities. Crevasse splays represent significant geomorphological features for understanding the fluvial morphodynamics in lowland areas dominated by avulsion processes. Within wide floodplains characterized by very low elevation ranges, the detection and accurate mapping of crevasse splay morphology and features, such as crevasse channels, levees, and deposit, can be very challenging considering floodplain extension, anthropic impact on the natural channels network, logistic difficulties, and in some cases, climate conditions that prevent field work. This research aims at improving the detection and mapping of crevasse splays in lowland areas through the combination of different remote sensing techniques based on optical multispectral imagery and topographic data derived from satellite earth observation missions. The Lower Mesopotamia Plain (LMP) offers a unique opportunity to study the avulsion processes because it presents numerous examples of crevasse splays, characterized by different sizes and states of activity. Furthermore, in this area, a strong correlation exists between the formation and development of crevasse splays and the expansion of agriculture and early societies since the Early Holocene. Different supervised classification (SC) methods of Landsat 8 satellite images have been tested together with topographic analysis of the microrelief, carried out based on two different 1-arcsec DEMs (AW3D30 and GDEM2). The results of this study demonstrate that the combination of multispectral imagery analysis and topographic analysis of the microrelief is useful for discerning different crevasse elements, distinguishing between active and relict landforms. The methodological approach proved helpful for improving the mapping of erosional and depositional landforms generated by the avulsion process and, in the study area, provided the best results for the active landforms.

- by Davide Nadali and +1
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- Remote Sensing, Geoarchaeology, Mesopotamian Archaeology, Remote sensing and GIS applications in Landscape Research
- by Hendav Berwari
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- Applied Mathematics, Environmental Science, Remote Sensing, Methodology

We would like to invite you to join this exciting new project as a chapter contributor on one of the topics listed below. Since this is a textbook, a great deal of this chapter entails a survey on the topic under the paradigm of cyber-physical systems, what can be done onboard and remotely, the distributed nature of the system and some exercises on futurology (anticipating trends can shed some light on upcoming designs). IET will bring great visibility to your work. Each chapter should be around 20-25 pages each and can be submitted as a Word or Latex File. The IET will send you additional info (formatting, permission form, etc.) with the contributor's agreement once you have decided to contribute to the book. Visit http://www.theiet.org/resources/author-hub/books/index.cfm to get all contributor's information to an IET research-level book. Each book is expected to have a total number of 500 printed pages (with approximately 550 words per page and a 20% allowance for figures and tables). We have included a tentative schedule and list of topics below. If this is something you would consider, please send us the title of your chapter, a short description/abstract of the chapter content, and your full contact details. We will expect original content and new insights for this book. You can, of course, reuse published material but the percentage of material reuse for the chapter should be less than 40%. The IET will run a piracy software on the full manuscript to control that you are including original material and will reject chapters who contain a large amount of already-published material so please do take this into consideration. We would appreciate your feedback by December 31, 2017. Please do not hesitate to contact us if you have any queries. We look forward to working with you towards the successful publication.

- by Vania V Estrela and +1
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- Robotics, Control Systems Engineering, Scientific Visualization, Robotics (Computer Science)
- by Mahesh Pal
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- Algorithms, Back Propagation, Neural Networks, Maximum Likelihood
- by Anna Pacheco
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- Cartography, Remote Sensing, Water, Carbon

- by Vania V Estrela
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- Robotics, Aerospace Engineering, Computer Science, Robotics (Computer Science)
- by Mahesh Pal
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- Geography, Remote Sensing, Neural Networks, Clustering Algorithms
- by Claudia Leyva
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- Engineering, Cartography, Landscape Architecture, Environmental Management
- by Douglas King
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- Cartography, Image Processing, Remote Sensing, Spatial Analysis

Remote sensing is a suitable candidate for monitoring rapid changes in Polar regions, offering high-resolution spectral, spatial and radiometric data. This paper focuses on the spectral properties of dominant plant species acquired during the first week of August 2015. Twenty-eight plots were selected, which could easily be identified in the field as well as on RapidEye satellite imagery. Spectral measurements of individual species were acquired, and heavy metal contamination stress factors were measured contemporaneously. As a result, a unique spectral library of dominant plant species, heavy metal concentrations and damage ratios were achieved with an indication that species-specific changes due to environmental conditions can best be differentiated in the 1401–2400 nm spectral region. Two key arctic tundra species, Cassiope tetragona and Dryas octopetala, exhibited significant differences in this spectral region that were linked to a changing health status. Relationships between field and satellite measurements were comparable, e.g., the Red Edge Normalized Difference Vegetation Index (RENDVI) showed a strong and significant relationship (R 2 = 0.82; p = 0.036) for the species Dryas octopetala. Cadmium and Lead were below detection levels while manganese, copper and zinc acquired near Longyearbyen were at concentrations comparable to other places in Svalbard. There were high levels of nickel near Longyearbyen (0.014 mg/g), while it was low (0.004 mg/g) elsewhere.

- by Bogdan Zagajewski and +4
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- Vegetation Ecology, Hyperspectral remote sensing, Arctic Climate, Svalbard
- by Ciro D'Elia
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- Cognitive Science, Mathematics, Computer Science, Image Processing
- by Miguel Velez-Reyes
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- Computer Science, Algorithms, Remote Sensing, Image Analysis
- by Tuomas Häme
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- Remote Sensing, Standard Deviation, Land Use, STEM
- by Chinglian Mawi
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- Landscape Ecology, Image Processing, Remote Sensing, Climate Change
- by Lawrence Martz
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- Cartography, Algorithms, Wetlands, Geometry
- by Raimundo Filho
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- Applied Mathematics, Environmental Science, Remote Sensing, Methodology

"Morphological observations (terrestrial laser scanning) and sediment analysis are used to study beach cusp
morphodynamics at Melbourne Beach (Florida, USA), a moderately sloped beach characterized by fine
sand. The study couples rapid high-resolution surveying with surficial sediment sampling over multiple
tidal cycles. Surveys were run ~500 m alongshore and sediment samples were collected intertidally over a period of 5 days. Beach cusp location within larger scale beach morphology is shown to directly influence
cusp growth as either gross erosional or gross accretional. Sediment characteristics within the beach cusp
morphology are reported coincident with cusp evolution. Variations in particle size distribution kurtosis
are exhibited as the cusps evolve, however no significant correlation is seen between grain size and
position between horn and embayment. During the end of the study, a storm resulted in beach cusp
destruction and increased sediment sorting."

- by Lori Collins and +2
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- Environmental Science, Geomorphology, Coastal Management, Coastal Processes
- by Ciro D'Elia
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- Cognitive Science, Mathematics, Computer Science, Image Processing
- by Renchu Guan
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- Geophysics, Algorithms, Statistics, Remote Sensing
- by Susan Ringrose
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- Geomorphology, Remote Sensing, Climate Change, Hydrology
- by Ciro D'Elia
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- Cognitive Science, Mathematics, Computer Science, Image Processing

There is a description of modern satellite-based Earth remote sensing systems in the paper. It shows the contradiction between the tendency to increase the resolution, increasing the number of spectral channels of onboard equipment against the limited on-board computational resources and communication channels using modern technologies. A solution in the form of intellectualization visual data processing is proposed by using neural network technology in the Earth remote sensing systems.

- by T. Malthus
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- Physiology, Assessment, Structure, Photosynthesis

The chlorophyll meter (CM) has been commonly used for in-season nitrogen (N) management of corn (Zea mays L.). Nevertheless, it has limited potential for site-specific N management in large fields due to difficulties in using it to generate N status maps. The objective of this study was to determine how well CM readings can be estimated using aerial hyper-spectral and simulated multi-spectral remote sensing images at different corn growth stages. Two field experiments were conducted in Minnesota, USA during 2005 involving different N application rates and timings on a corn-soybean [Glycine max (L.) Merr.] rotation field and a corn-corn rotation field. Four flights were made during the growing season using the AISA Eagle Hyperspectral Imager and CM readings were collected at four or five different growth stages. The results indicated that single multi-spectral and hyperspectral band or vegetation index could explain 64–86% and 73–88% of the variability in CM readings, respectively, except at growth stage V9 in the corn-soybean rotation field where no band or vegetation index could explain more than 37% of the variability in CM readings. Multiple regression analysis demonstrated that the combination of 2–4 broad-bands or 3–8 narrow-bands could explain 41–92% or 61–94% of the variability in CM readings across the two fields and different corn growth stages investigated. It was concluded that the combination of CM readings with high spatial resolution hyperspectral or multispectral remote sensing images can overcome the limitations of using them individually, thus offering a practical solution to N deficiency detection and possibly

- by Ruxandra VINTILA and +2
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- Sustainable agriculture, Precision Agriculture, Agriculture, Hyperspectral remote sensing
- by Rajagopal Vadivel
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- Biomass, Stress, Biological Sciences, Growth
- by Reinold Pasterkamp
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- Algorithms, Remote Sensing, Methodology, Computing
- by Jorge Núñez
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- Geophysics, Image Processing, Remote Sensing, Image fusion