Effect of recoil electron range on efficiency and on spatial resolution of very high resolution animal PET

2004 ◽  
Author(s):  
S. Park ◽  
W.L. Roger ◽  
N.H. Clinthorne
Keyword(s):  
High Resolution ◽  
Spatial Resolution ◽  
Recoil Electron ◽  
Animal Pet ◽  
Very High ◽  
Electron Range

scholarly journals Crown closure segmentation on wetland lowland forest using the mean shift algorithm

2021 ◽  
Vol 24 (2) ◽  
pp. 965
Author(s):  
Beni Iskandar ◽  
I Nengah Surati Jaya ◽  
Muhammad Buce Saleh
Keyword(s):  
High Resolution ◽  
Spatial Resolution ◽  
Forest Inventory ◽  
Optimal Parameter ◽  
Mean Shift ◽  
Lowland Forest ◽  
Study Objective ◽  
Parameter Combination ◽  
Crown Closure ◽  
Very High

The availability of high and very high-resolution imagery is helpful for forest inventory, particularly to measure the stand variables such as canopy dimensions, canopy density, and crown closure. This paper describes the examination of mean shift (MS) algorithm on wetland lowland forest. The study objective was to find the optimal parameters for crown closure segmentation Pleiades-1B and SPOT-6 imageries. The study shows that the segmentation of crown closure with the red band of Pleiades-1B image would be well segmented by using the parameter combination of (hs: 6, hr: 5, M: 33) having overall accuracy of 88.93% and Kappa accuracy of 73.76%, while the red, green, blue (RGB) composite of SPOT-6 image, the optimal parameter combination was (hs:2, hr: 8, M: 11), having overall accuracy of 85.72% and kappa accuracy of 68.33%. The Pleiades-1B image with a spatial resolution of (0.5 m) provides better accuracy than SPOT-5 of (1.5 m) spatial resolution. The differences between single spectral, synthetic, and RGB does not significantly affect the accuracy of segmentation. The study concluded that the segmentation of high and very high-resolution images gives promising results on forest inventory.

scholarly journals Classifying settlement types from multi-scale spatial patterns of building footprints

2020 ◽  
pp. 239980832092120 ◽  
Author(s):  
Warren C Jochem ◽  
Douglas R Leasure ◽  
Oliver Pannell ◽  
Heather R Chamberlain ◽  
Patricia Jones ◽  
...  
Keyword(s):  
High Resolution ◽  
Spatial Resolution ◽  
Spatial Patterns ◽  
Gaussian Mixture ◽  
Developed Countries ◽  
Urban Morphology ◽  
Multi Scale ◽  
High Resolution Imagery ◽  
Very High Resolution Imagery ◽  
Very High

Urban settlements and urbanised populations continue to grow rapidly and much of this transition is occurring in less developed countries. Remote sensing techniques are now often applied to monitor urbanisation and changes in settlement patterns. In particular, increasing availability of very high resolution imagery (<1 m spatial resolution) and computing power is enabling complete sets of settlement data in the form of building footprints to be extracted from imagery. These settlement data provide information on the changes occurring in cities, particularly in countries which may lack other data on urbanisation. While spatially detailed, extracted building footprints typically lack other information that identify building types or can be used to differentiate intra-urban land uses or neighbourhood types. This work demonstrates an approach to classifying settlement types through multi-scale spatial patterns of urban morphology visible in building footprint data extracted from very high resolution imagery. The work uses a Gaussian mixture modelling approach to select and hierarchically merge components into clusters. The results are maps classifying settlement types on a high spatial resolution (100 m) grid. The approach is applied in Kaduna, Nigeria; Kinshasa, Democratic Republic of the Congo; and Maputo, Mozambique and demonstrates the potential of computational methods to take advantage of large spatial datasets and extract meaningful information to support monitoring of urban areas. The model-based approach produces a hierarchy of potential clustering solutions, and we suggest that this can be used in partnership with local knowledge of the context when creating settlement typologies.

scholarly journals Automated crop plant counting from very high-resolution aerial imagery

2020 ◽  
Vol 21 (6) ◽  
pp. 1366-1384 ◽  
Author(s):  
João Valente ◽  
Bilal Sari ◽  
Lammert Kooistra ◽  
Henk Kramer ◽  
Sander Mücher
Keyword(s):  
High Resolution ◽  
Machine Vision ◽  
Surface Area ◽  
Spatial Resolution ◽  
Image Data ◽  
Validation Data ◽  
Counting Error ◽  
Use Of Resources ◽  
Automated Method ◽  
Very High

Abstract Knowing before harvesting how many plants have emerged and how they are growing is key in optimizing labour and efficient use of resources. Unmanned aerial vehicles (UAV) are a useful tool for fast and cost efficient data acquisition. However, imagery need to be converted into operational spatial products that can be further used by crop producers to have insight in the spatial distribution of the number of plants in the field. In this research, an automated method for counting plants from very high-resolution UAV imagery is addressed. The proposed method uses machine vision—Excess Green Index and Otsu’s method—and transfer learning using convolutional neural networks to identify and count plants. The integrated methods have been implemented to count 10 weeks old spinach plants in an experimental field with a surface area of 3.2 ha. Validation data of plant counts were available for 1/8 of the surface area. The results showed that the proposed methodology can count plants with an accuracy of 95% for a spatial resolution of 8 mm/pixel in an area up to 172 m2. Moreover, when the spatial resolution decreases with 50%, the maximum additional counting error achieved is 0.7%. Finally, a total amount of 170 000 plants in an area of 3.5 ha with an error of 42.5% was computed. The study shows that it is feasible to count individual plants using UAV-based off-the-shelf products and that via machine vision/learning algorithms it is possible to translate image data in non-expert practical information.

scholarly journals Design of Neutron Microscopes Equipped with Wolter Mirror Condenser and Objective Optics for High-Fidelity Imaging and Beam Transport

2020 ◽  
Vol 6 (10) ◽  
pp. 100 ◽  
Author(s):  
Muhammad Abir ◽  
Daniel S. Hussey ◽  
Boris Khaykovich
Keyword(s):  
High Resolution ◽  
Spatial Resolution ◽  
Neutron Imaging ◽  
High Fidelity ◽  
Signal Rate ◽  
Pinhole Imaging ◽  
Standard Neutron ◽  
Resolution Imaging ◽  
Very High ◽  
Better Than

We present and compare the designs of three types of neutron microscopes for high-resolution neutron imaging. Like optical microscopes, and unlike standard neutron imaging instruments, these microscopes have both condenser and image-forming objective optics. The optics are glancing-incidence axisymmetric mirrors and therefore suitable for polychromatic neutron beams. The mirrors are designed to provide a magnification of 10 to achieve a spatial resolution of better than 10 μm. The resolution of the microscopes is determined by the mirrors rather than by the L/Dratio as in conventional pinhole imaging, leading to possible dramatic improvements in the signal rate. We predict the increase in the signal rate by at least two orders of magnitude for very high-resolution imaging, which is always flux limited. Furthermore, in contrast to pinhole imaging, in the microscope, the samples are placed far from the detector to allow for a bulky sample environment without sacrificing spatial resolution.

scholarly journals Fractional Cover Mapping of Invasive Plant Species by Combining Very High-Resolution Stereo and Multi-Sensor Multispectral Imageries

Forests ◽  
2019 ◽  
Vol 10 (7) ◽  
pp. 540 ◽  
Author(s):  
Siddhartha Khare ◽  
Hooman Latifi ◽  
Sergio Rossi ◽  
Sanjay Kumar Ghosh
Keyword(s):  
High Resolution ◽  
Plant Species ◽  
Spatial Resolution ◽  
Spectral Reflectance ◽  
Invasive Plant ◽  
Training Data ◽  
Landsat 8 ◽  
Invasive Plant Species ◽  
Fractional Cover ◽  
Very High

Invasive plant species are major threats to biodiversity. They can be identified and monitored by means of high spatial resolution remote sensing imagery. This study aimed to test the potential of multiple very high-resolution (VHR) optical multispectral and stereo imageries (VHRSI) at spatial resolutions of 1.5 and 5 m to quantify the presence of the invasive lantana (Lantana camara L.) and predict its distribution at large spatial scale using medium-resolution fractional cover analysis. We created initial training data for fractional cover analysis by classifying smaller extent VHR data (SPOT-6 and RapidEye) along with three dimensional (3D) VHRSI derived digital surface model (DSM) datasets. We modelled the statistical relationship between fractional cover and spectral reflectance for a VHR subset of the study area located in the Himalayan region of India, and finally predicted the fractional cover of lantana based on the spectral reflectance of Landsat-8 imagery of a larger spatial extent. We classified SPOT-6 and RapidEye data and used the outputs as training data to create continuous field layers of Landsat-8 imagery. The area outside the overlapping region was predicted by fractional cover analysis due to the larger extent of Landsat-8 imagery compared with VHR datasets. Results showed clear discrimination of understory lantana from upperstory vegetation with 87.38% (for SPOT-6), and 85.27% (for RapidEye) overall accuracy due to the presence of additional VHRSI derived DSM information. Independent validation for lantana fractional cover estimated root-mean-square errors (RMSE) of 11.8% (for RapidEye) and 7.22% (for SPOT-6), and R2 values of 0.85 and 0.92 for RapidEye (5 m) and SPOT-6 (1.5 m), respectively. Results suggested an increase in predictive accuracy of lantana within forest areas along with increase in the spatial resolution for the same Landsat-8 imagery. The variance explained at 1.5 m spatial resolution to predict lantana was 64.37%, whereas it decreased by up to 37.96% in the case of 5 m spatial resolution data. This study revealed the high potential of combining small extent VHR and VHRSI- derived 3D optical data with larger extent, freely available satellite data for identification and mapping of invasive species in mountainous forests and remote regions.

scholarly journals Very High Resolution Observations of the Luminous Water Masers in NGC 4258

1988 ◽  
Vol 129 ◽  
pp. 231-232
Author(s):  
M. J. Claussen ◽  
M. J. Reid ◽  
M. H. Schneps ◽  
K.-Y. Lo ◽  
J. M. Moran ◽  
...  
Keyword(s):  
High Resolution ◽  
Spatial Resolution ◽  
Molecular Gas ◽  
Nearby Galaxy ◽  
Active Nucleus ◽  
Maser Emission ◽  
Water Masers ◽  
Very High

We summarize the results and interpretation of a four station transcontinental VLBI experiment of the luminous water masers in the nearby galaxy NGC 4258. At a distance of 5 Mpc, the longest baseline of the experiment provides spatial resolution of less than 1016 cm. The strongest maser emission was detected on all baselines, and was found to consist of at least two features separated by about 0.1 mas (1016 cm). Weaker features are possibly spread over a region up to 1 mas in size. These results provide evidence that supports the scenario described by Claussen and Lo (1986) which suggests that the very luminous water masers reside in molecular gas that immediately surrounds the central, active nucleus.

scholarly journals Design of a very high-resolution small animal PET scanner using a silicon scatter detector insert

2007 ◽  
Vol 52 (15) ◽  
pp. 4653-4677 ◽  
Author(s):  
Sang-June Park ◽  
W Leslie Rogers ◽  
Neal H Clinthorne
Keyword(s):  
High Resolution ◽  
Small Animal ◽  
Small Animal Pet ◽  
Animal Pet ◽  
Very High

scholarly journals Leveraging Deep Neural Networks to Map Caribou Lichen in High-Resolution Satellite Images Based on a Small-Scale, Noisy UAV-Derived Map

2021 ◽  
Vol 13 (14) ◽  
pp. 2658
Author(s):  
Shahab Jozdani ◽  
Dongmei Chen ◽  
Wenjun Chen ◽  
Sylvain G. Leblanc ◽  
Christian Prévost ◽  
...  
Keyword(s):  
High Resolution ◽  
Spatial Resolution ◽  
Large Scale ◽  
False Positive Rate ◽  
Scale Up ◽  
Small Sample ◽  
Small Scale ◽  
Sample Site ◽  
High Resolution Satellite Images ◽  
Very High

Lichen is an important food source for caribou in Canada. Lichen mapping using remote sensing (RS) images could be a challenging task, however, as lichens generally appear in unevenly distributed, small patches, and could resemble surficial features. Moreover, collecting lichen labeled data (reference data) is expensive, which restricts the application of many robust supervised classification models that generally demand a large quantity of labeled data. The goal of this study was to investigate the potential of using a very-high-spatial resolution (1-cm) lichen map of a small sample site (e.g., generated based on a single UAV scene and using field data) to train a subsequent classifier to map caribou lichen over a much larger area (~0.04 km2 vs. ~195 km2) and a lower spatial resolution image (in this case, a 50-cm WorldView-2 image). The limited labeled data from the sample site were also partially noisy due to spatial and temporal mismatching issues. For this, we deployed a recently proposed Teacher-Student semi-supervised learning (SSL) approach (based on U-Net and U-Net++ networks) involving unlabeled data to assist with improving the model performance. Our experiments showed that it was possible to scale-up the UAV-derived lichen map to the WorldView-2 scale with reasonable accuracy (overall accuracy of 85.28% and F1-socre of 84.38%) without collecting any samples directly in the WorldView-2 scene. We also found that our noisy labels were partially beneficial to the SSL robustness because they improved the false positive rate compared to the use of a cleaner training set directly collected within the same area in the WorldView-2 image. As a result, this research opens new insights into how current very high-resolution, small-scale caribou lichen maps can be used for generating more accurate large-scale caribou lichen maps from high-resolution satellite imagery.

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