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Remote Sens., Volume 4, Issue 4 (April 2012), Pages 810-1111

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Research

Open AccessArticle Improved Forest Biomass and Carbon Estimations Using Texture Measures from WorldView-2 Satellite Data
Remote Sens. 2012, 4(4), 810-829; doi:10.3390/rs4040810
Received: 1 February 2012 / Revised: 15 March 2012 / Accepted: 15 March 2012 / Published: 26 March 2012
Cited by 51 | PDF Full-text (1230 KB) | HTML Full-text | XML Full-text
Abstract
Accurate estimation of aboveground biomass and carbon stock has gained importance in the context of the United Nations Framework Convention on Climate Change (UNFCCC) and the Kyoto Protocol. In order to develop improved forest stratum–specific aboveground biomass and carbon estimation models for humid
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Accurate estimation of aboveground biomass and carbon stock has gained importance in the context of the United Nations Framework Convention on Climate Change (UNFCCC) and the Kyoto Protocol. In order to develop improved forest stratum–specific aboveground biomass and carbon estimation models for humid rainforest in northeast Madagascar, this study analyzed texture measures derived from WorldView-2 satellite data. A forest inventory was conducted to develop stratum-specific allometric equations for dry biomass. On this basis, carbon was calculated by applying a conversion factor. After satellite data preprocessing, vegetation indices, principal components, and texture measures were calculated. The strength of their relationships with the stratum-specific plot data was analyzed using Pearson’s correlation. Biomass and carbon estimation models were developed by performing stepwise multiple linear regression. Pearson’s correlation coefficients revealed that (a) texture measures correlated more with biomass and carbon than spectral parameters, and (b) correlations were stronger for degraded forest than for non-degraded forest. For degraded forest, the texture measures of Correlation, Angular Second Moment, and Contrast, derived from the red band, contributed to the best estimation model, which explained 84% of the variability in the field data (relative RMSE = 6.8%). For non-degraded forest, the vegetation index EVI and the texture measures of Variance, Mean, and Correlation, derived from the newly introduced coastal blue band, both NIR bands, and the red band, contributed to the best model, which explained 81% of the variability in the field data (relative RMSE = 11.8%). These results indicate that estimation of tropical rainforest biomass/carbon, based on very high resolution satellite data, can be improved by (a) developing and applying forest stratum–specific models, and (b) including textural information in addition to spectral information. Full article
Open AccessArticle LiDAR Sampling Density for Forest Resource Inventories in Ontario, Canada
Remote Sens. 2012, 4(4), 830-848; doi:10.3390/rs4040830
Received: 22 February 2012 / Revised: 16 March 2012 / Accepted: 16 March 2012 / Published: 27 March 2012
Cited by 41 | PDF Full-text (1060 KB) | HTML Full-text | XML Full-text
Abstract
Over the past two decades there has been an abundance of research demonstrating the utility of airborne light detection and ranging (LiDAR) for predicting forest biophysical/inventory variables at the plot and stand levels. However, to date there has been little effort to develop
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Over the past two decades there has been an abundance of research demonstrating the utility of airborne light detection and ranging (LiDAR) for predicting forest biophysical/inventory variables at the plot and stand levels. However, to date there has been little effort to develop a set of protocols for data acquisition and processing that would move governments or the forest industry towards cost-effective implementation of this technology for strategic and tactical (i.e., operational) forest resource inventories. The goal of this paper is to initiate this process by examining the significance of LiDAR data acquisition (i.e., point density) for modeling forest inventory variables for the range of species and stand conditions representing much of Ontario, Canada. Field data for approximately 200 plots, sampling a broad range of forest types and conditions across Ontario, were collected for three study sites. Airborne LiDAR data, characterized by a mean density of 3.2 pulses m−2 were systematically decimated to produce additional datasets with densities of approximately 1.6 and 0.5 pulses m−2. Stepwise regression models, incorporating LiDAR height and density metrics, were developed for each of the three LiDAR datasets across a range of forest types to estimate the following forest inventory variables: (1) average height (R2(adj) = 0.75–0.95); (2) top height (R2(adj) = 0.74–0.98); (3) quadratic mean diameter (R2(adj) = 0.55–0.85); (4) basal area (R2(adj) = 0.22–0.93); (5) gross total volume (R2(adj) = 0.42–0.94); (6) gross merchantable volume (R2(adj) = 0.35–0.93); (7) total aboveground biomass (R2(adj) = 0.23–0.93); and (8) stem density (R2(adj) = 0.17–0.86). Aside from a few cases (i.e., average height and density for some stand types), no decimation effect was observed with respect to the precision of the prediction of the majority of forest variables, which suggests that a mean density of 0.5 pulses m−2 is sufficient for plot and stand level modeling under these diverse forest conditions across Ontario. Full article
(This article belongs to the Special Issue Laser Scanning in Forests)
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Open AccessArticle Estimating Net Primary Production of Turfgrass in an Urban-Suburban Landscape with QuickBird Imagery
Remote Sens. 2012, 4(4), 849-866; doi:10.3390/rs4040849
Received: 29 January 2012 / Revised: 15 March 2012 / Accepted: 16 March 2012 / Published: 27 March 2012
Cited by 11 | PDF Full-text (1348 KB) | HTML Full-text | XML Full-text
Abstract
Vegetation is a basic component of urban-suburban environments with significant area coverage. As a major vegetation type in US cities, urban turfgrass provides a range of important ecological services. This study examined the biological carbon fixation of turfgrass in a typical residential neighborhood
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Vegetation is a basic component of urban-suburban environments with significant area coverage. As a major vegetation type in US cities, urban turfgrass provides a range of important ecological services. This study examined the biological carbon fixation of turfgrass in a typical residential neighborhood by linking ground-based measurements, high resolution satellite remote sensing, and ecological modeling. The spatial distribution of turfgrass and its vegetative conditions were mapped with QuickBird satellite imagery. The significant amount of shadows existing in the imagery were detected and removed by taking advantage of the high radiometric resolution of the data. A remote sensing-driven production efficiency model was developed and parameterized with field biophysical measurements to estimate annual net primary production of turfgrass. The results indicated that turfgrass accounted for 38% of land cover in the study area. Turfgrass assimilated 0–1,301 g∙C∙m−2∙yr−1 depending on vegetative conditions and management intensity. The average annual net primary production per unit turfgrass cover by golf course grass (1,100.5 g∙C∙m−2) was much higher than that by regular lawn grass (771.2 g∙C∙m−2). However, lawn grass contributed more to the total net primary production than golf course grass due to its larger area coverage, although with higher spatial variability. Full article
Open AccessArticle A Novel Satellite Mission Concept for Upper Air Water Vapour, Aerosol and Cloud Observations Using Integrated Path Differential Absorption LiDAR Limb Sounding
Remote Sens. 2012, 4(4), 867-910; doi:10.3390/rs4040867
Received: 21 February 2012 / Revised: 15 March 2012 / Accepted: 20 March 2012 / Published: 27 March 2012
PDF Full-text (4739 KB) | HTML Full-text | XML Full-text
Abstract
We propose a new satellite mission to deliver high quality measurements of upper air water vapour. The concept centres around a LiDAR in limb sounding by occultation geometry, designed to operate as a very long path system for differential absorption measurements. We present
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We propose a new satellite mission to deliver high quality measurements of upper air water vapour. The concept centres around a LiDAR in limb sounding by occultation geometry, designed to operate as a very long path system for differential absorption measurements. We present a preliminary performance analysis with a system sized to send 75 mJ pulses at 25 Hz at four wavelengths close to 935 nm, to up to 5 microsatellites in a counter-rotating orbit, carrying retroreflectors characterized by a reflected beam divergence of roughly twice the emitted laser beam divergence of 15 µrad. This provides water vapour profiles with a vertical sampling of 110 m; preliminary calculations suggest that the system could detect concentrations of less than 5 ppm. A secondary payload of a fairly conventional medium resolution multispectral radiometer allows wide-swath cloud and aerosol imaging. The total weight and power of the system are estimated at 3 tons and 2,700 W respectively. This novel concept presents significant challenges, including the performance of the lasers in space, the tracking between the main spacecraft and the retroreflectors, the refractive effects of turbulence, and the design of the telescopes to achieve a high signal-to-noise ratio for the high precision measurements. The mission concept was conceived at the Alpbach Summer School 2010. Full article
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Open AccessArticle Segmentation of Shadowed Buildings in Dense Urban Areas from Aerial Photographs
Remote Sens. 2012, 4(4), 911-933; doi:10.3390/rs4040911
Received: 16 February 2012 / Revised: 17 March 2012 / Accepted: 19 March 2012 / Published: 29 March 2012
Cited by 9 | PDF Full-text (3440 KB) | HTML Full-text | XML Full-text
Abstract
Segmentation of buildings in urban areas, especially dense urban areas, by using remotely sensed images is highly desirable. However, segmentation results obtained by using existing algorithms are unsatisfactory because of the unclear boundaries between buildings and the shadows cast by neighboring buildings. In
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Segmentation of buildings in urban areas, especially dense urban areas, by using remotely sensed images is highly desirable. However, segmentation results obtained by using existing algorithms are unsatisfactory because of the unclear boundaries between buildings and the shadows cast by neighboring buildings. In this paper, an algorithm is proposed that successfully segments buildings from aerial photographs, including shadowed buildings in dense urban areas. To handle roofs having rough textures, digital numbers (DNs) are quantized into several quantum values. Quantization using several interval widths is applied during segmentation, and for each quantization, areas with homogeneous values are labeled in an image. Edges determined from the homogeneous areas obtained at each quantization are then merged, and frequently observed edges are extracted. By using a “rectangular index”, regions whose shapes are close to being rectangular are thus selected as buildings. Experimental results show that the proposed algorithm generates more practical segmentation results than an existing algorithm does. Therefore, the main factors in successful segmentation of shadowed roofs are (1) combination of different quantization results, (2) selection of buildings according to the rectangular index, and (3) edge completion by the inclusion of non-edge pixels that have a high probability of being edges. By utilizing these factors, the proposed algorithm optimizes the spatial filtering scale with respect to the size of building roofs in a locality. The proposed algorithm is considered to be useful for conducting building segmentation for various purposes. Full article
Open AccessArticle A Conceptual Model of Surface Reflectance Estimation for Satellite Remote Sensing Images Using in situ Reference Data
Remote Sens. 2012, 4(4), 934-949; doi:10.3390/rs4040934
Received: 14 February 2012 / Revised: 14 March 2012 / Accepted: 16 March 2012 / Published: 30 March 2012
Cited by 4 | PDF Full-text (976 KB) | HTML Full-text | XML Full-text
Abstract
For satellite remote sensing, radiances received at the sensor are not only affected by the atmosphere but also by the topographic properties of the terrain surface. As a result, atmospheric correction alone does not yield output images that truly reflect terrain surface properties,
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For satellite remote sensing, radiances received at the sensor are not only affected by the atmosphere but also by the topographic properties of the terrain surface. As a result, atmospheric correction alone does not yield output images that truly reflect terrain surface properties, namely surface reflectance (bidirectional reflectance factor, BRF) of objects on the earth surface. Following the concept of the radiometric control area (RCA)-based path radiance estimation method, we herein propose a statistical approach for surface reflectance estimation utilizing DEM data and surface reflectance of selected radiometric control areas. An algorithm for identification of shaded samples and a shape factor model were also developed in this study. The proposed RCA-based surface reflectance estimation method is capable of achieving good reflectance estimates in a region where elevation varies from 0 to approximately 600 m above the mean sea level. However, further study is recommended in order to extend the application of the proposed method to areas with substantial terrain variation. Full article
Open AccessArticle An International Comparison of Individual Tree Detection and Extraction Using Airborne Laser Scanning
Remote Sens. 2012, 4(4), 950-974; doi:10.3390/rs4040950
Received: 10 February 2012 / Revised: 15 March 2012 / Accepted: 15 March 2012 / Published: 30 March 2012
Cited by 107 | PDF Full-text (1285 KB) | HTML Full-text | XML Full-text
Abstract
The objective of the “Tree Extraction” project organized by EuroSDR (European Spatial data Research) and ISPRS (International Society of Photogrammetry and Remote Sensing) was to evaluate the quality, accuracy, and feasibility of automatic tree extraction methods, mainly based on laser scanner data. In
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The objective of the “Tree Extraction” project organized by EuroSDR (European Spatial data Research) and ISPRS (International Society of Photogrammetry and Remote Sensing) was to evaluate the quality, accuracy, and feasibility of automatic tree extraction methods, mainly based on laser scanner data. In the final report of the project, Kaartinen and Hyyppä (2008) reported a high variation in the quality of the published methods under boreal forest conditions and with varying laser point densities. This paper summarizes the findings beyond the final report after analyzing the results obtained in different tree height classes. Omission/Commission statistics as well as neighborhood relations are taken into account. Additionally, four automatic tree detection and extraction techniques were added to the test. Several methods in this experiment were superior to manual processing in the dominant, co-dominant and suppressed tree storeys. In general, as expected, the taller the tree, the better the location accuracy. The accuracy of tree height, after removing gross errors, was better than 0.5 m in all tree height classes with the best methods investigated in this experiment. For forest inventory, minimum curvature-based tree detection accompanied by point cloud-based cluster detection for suppressed trees is a solution that deserves attention in the future. Full article
(This article belongs to the Special Issue Laser Scanning in Forests)
Open AccessArticle An Empirical Assessment of Temporal Decorrelation Using the Uninhabited Aerial Vehicle Synthetic Aperture Radar over Forested Landscapes
Remote Sens. 2012, 4(4), 975-986; doi:10.3390/rs4040975
Received: 13 February 2012 / Revised: 28 March 2012 / Accepted: 28 March 2012 / Published: 2 April 2012
Cited by 17 | PDF Full-text (1288 KB) | HTML Full-text | XML Full-text
Abstract
We present an empirical assessment of the impact of temporal decorrelation on interferometric coherence measured over a forested landscape. A series of repeat-pass interferometric radar images with a zero spatial baseline were collected with UAVSAR (Uninhabited Aerial Vehicle Synthetic Aperture Radar), a fully
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We present an empirical assessment of the impact of temporal decorrelation on interferometric coherence measured over a forested landscape. A series of repeat-pass interferometric radar images with a zero spatial baseline were collected with UAVSAR (Uninhabited Aerial Vehicle Synthetic Aperture Radar), a fully polarimetric airborne L-band radar system. The dataset provided temporal separations of 45 minutes, 2, 7 and 9 days. Coincident airborne lidar and weather data were collected. We theoretically demonstrate that UAVSAR measurement accuracy enables accurate quantification of temporal decorrelation. Data analysis revealed precipitation events to be the main driver of temporal decorrelation over the acquisition period. The experiment also shows temporal decorrelation increases with canopy height, and this pattern was found consistent across forest types and polarization. Full article
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Open AccessArticle Effect of Grain Size and Mineral Mixing on Carbonate Absorption Features in the SWIR and TIR Wavelength Regions
Remote Sens. 2012, 4(4), 987-1003; doi:10.3390/rs4040987
Received: 24 February 2012 / Revised: 27 March 2012 / Accepted: 28 March 2012 / Published: 10 April 2012
Cited by 9 | PDF Full-text (535 KB) | HTML Full-text | XML Full-text
Abstract
Reflectance spectra of carbonate minerals in the shortwave infrared (SWIR) and thermal infrared (TIR) wavelength regions contain a number of diagnostic absorption features. The shape of these features depends on various physical and chemical parameters. To accurately identify carbonate minerals or rocks in
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Reflectance spectra of carbonate minerals in the shortwave infrared (SWIR) and thermal infrared (TIR) wavelength regions contain a number of diagnostic absorption features. The shape of these features depends on various physical and chemical parameters. To accurately identify carbonate minerals or rocks in pure and mixed form, it is necessary to analyze the effects of the parameters on spectral characteristics. In this study, we analyzed spectral absorption feature characteristics of calcite and dolomite in the SWIR (features at 2.3 and 2.5 μm) and TIR (features at 11.5 and 14 μm) wavelength regions, as a function of grain size and carbonate mineral mixtures. Results showed that varying grain sizes and mineral contents in the sample, influence reflectance values and absorption feature characteristics. Absorption band positions of pure and mixed calcite and dolomite in the SWIR and TIR regions for both features were displaced slightly as observed in previous studies. The band positions of calcite and dolomite varied relative to grain size only in the TIR region. These positions shifted to longer wavelengths for the feature at 11.5 μm and to shorter wavelengths for the feature at 14 μm from fine to coarse grain size. The band positions of calcite-dolomite mixtures in the SWIR and TIR regions were determined by the quantity of calcite and dolomite in the sample. These results can be applied for the identification of pure and mixed calcite and dolomite, as well as estimating the relative abundance of both minerals with different grain size and mineral mixtures in a synthetic sample or rock. They can also be used as a preliminary proxy for assessing dolomitization patterns in carbonate rocks. Full article
Open AccessArticle Comparison of Methods for Estimation of Stem Volume, Stem Number and Basal Area from Airborne Laser Scanning Data in a Hemi-Boreal Forest
Remote Sens. 2012, 4(4), 1004-1023; doi:10.3390/rs4041004
Received: 6 February 2012 / Revised: 3 April 2012 / Accepted: 6 April 2012 / Published: 13 April 2012
Cited by 16 | PDF Full-text (565 KB) | HTML Full-text | XML Full-text
Abstract
This study compares methods to estimate stem volume, stem number and basal area from Airborne Laser Scanning (ALS) data for 68 field plots in a hemi-boreal, spruce dominated forest (Lat. 58°N, Long. 13°E). The stem volume was estimated with five different regression models:
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This study compares methods to estimate stem volume, stem number and basal area from Airborne Laser Scanning (ALS) data for 68 field plots in a hemi-boreal, spruce dominated forest (Lat. 58°N, Long. 13°E). The stem volume was estimated with five different regression models: one model based on height and density metrics from the ALS data derived from the whole field plot, two models based on similar combinations derived from 0.5 m raster cells, and two models based on canopy volumes from the ALS data. The best result was achieved with a model based on height and density metrics derived from 0.5 m raster cells (Root Mean Square Error or RMSE 37.3%) and the worst with a model based on height and density metrics derived from the whole field plot (RMSE 41.9%). The stem number and the basal area were estimated with: (i) area-based regression models using height and density metrics from the ALS data; and (ii) single tree-based information derived from local maxima in a normalized digital surface model (nDSM) mean filtered with different conditions. The estimates from the regression model were more accurate (RMSE 52.7% for stem number and 21.5% for basal area) than those derived from the nDSM (RMSE 63.4%–91.9% and 57.0%–175.5%, respectively). The accuracy of the estimates from the nDSM varied depending on the filter size and the conditions of the applied filter. This suggests that conditional filtering is useful but sensitive to the conditions. Full article
(This article belongs to the Special Issue Laser Scanning in Forests)
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Open AccessArticle From Land Cover to Land Use: A Methodology to Assess Land Use from Remote Sensing Data
Remote Sens. 2012, 4(4), 1024-1045; doi:10.3390/rs4041024
Received: 18 February 2012 / Revised: 5 April 2012 / Accepted: 6 April 2012 / Published: 17 April 2012
Cited by 8 | PDF Full-text (4288 KB) | HTML Full-text | XML Full-text
Abstract
The “land use” concept has evolved during recent decades and it is now considered as the socioeconomic function of land. Land use representation and land use change assessment through remote sensing still remains one of the major challenges for the remote sensing scientific
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The “land use” concept has evolved during recent decades and it is now considered as the socioeconomic function of land. Land use representation and land use change assessment through remote sensing still remains one of the major challenges for the remote sensing scientific community. In this paper we present a methodological approach based on remote sensing techniques to assess land use in accordance with the requirements of the United Nations Framework Climate Change Convention, UNFCCC (1995). The methodology is based mainly on the recognition of the land key elements and their function and on the adoption of the “predominant land use” criteria in the classification scheme settled by rules. The concept that underpins these rules is that the land use function of land can be expressed through hierarchical relationships among key land elements, and that these functional relationships are based on thresholds reflecting the relevance and predominance of key land elements in the observed area. When analyses are supported by high (10–30 m) or very high ( < 10 m) spatial resolution remote sensing data, the methodology provides a systematic approach for the representation of land use that is consistent with the concepts and methodologies developed by the International Panel on Climate Change(IPCC) to fulfill UNFCCC commitments. In particular, data with high and very high spatial resolution provide good results, with overall accuracies above 87% in the identification of key land elements that characterize land use classes. The methodology could be used to assess land use in any context (e.g., for any land use category or in any country and region) as it is based on the definition of user/project rules that should be tailored on the land use function of any territory. Full article
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Open AccessArticle Dynamics of a Coupled System: Multi-Resolution Remote Sensing in Assessing Social-Ecological Responses during 25 Years of Gas Field Development in Arctic Russia
Remote Sens. 2012, 4(4), 1046-1068; doi:10.3390/rs4041046
Received: 13 February 2012 / Revised: 4 April 2012 / Accepted: 6 April 2012 / Published: 17 April 2012
Cited by 11 | PDF Full-text (2862 KB) | HTML Full-text | XML Full-text
Abstract
Hydrocarbon exploration has been underway in the north of West Siberia for several decades. Giant gas fields on the Yamal Peninsula are expected to begin feeding the Nord Stream pipeline to Western Europe in late 2012. Employing a variety of high- to very
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Hydrocarbon exploration has been underway in the north of West Siberia for several decades. Giant gas fields on the Yamal Peninsula are expected to begin feeding the Nord Stream pipeline to Western Europe in late 2012. Employing a variety of high- to very high-resolution satellite-based sensors, we have followed the establishment and spread of Bovanenkovo, the biggest and first field to be developed. Extensive onsite field observations and measurements of land use and land cover changes since 1985 have been combined with intensive participant observation in all seasons among indigenous Nenets reindeer herders and long-term gas field workers during 2004–2007 and 2010–2011. Time series and multi-resolution imagery was used to build a chronology of the gas field’s development. Large areas of partially or totally denuded tundra and most forms of expanding infrastructure are readily tracked with Landsat scenes (1985, 1988, 2000, 2009, 2011). SPOT (1993, 1998) and ASTER (2001) were also used. Quickbird-2 (2004) and GeoEye (2010) were most successful in detecting small-scale anthropogenic disturbances as well as individual camps of nomadic herders moving in the vicinity of the gas field. For assessing gas field development the best results are obtained by combining lower resolution with Very High Resolution (VHR) imagery (spatial resolution < 5 m) and fieldwork. Nenets managing collective and privately owned herds of reindeer have proven adept in responding to a broad range of intensifying industrial impacts at the same time as they have been dealing with symptoms of a warming climate. Here we detail both the spatial extent of gas field growth and the dynamic relationship between Nenets nomads and their rapidly evolving social-ecological system. Full article
(This article belongs to the Special Issue Human-Induced Global Change)
Open AccessArticle SR-4000 and CamCube3.0 Time of Flight (ToF) Cameras: Tests and Comparison
Remote Sens. 2012, 4(4), 1069-1089; doi:10.3390/rs4041069
Received: 9 February 2012 / Revised: 9 April 2012 / Accepted: 10 April 2012 / Published: 18 April 2012
Cited by 19 | PDF Full-text (1869 KB) | HTML Full-text | XML Full-text
Abstract
In this paper experimental comparisons between two Time-of-Flight (ToF) cameras are reported in order to test their performance and to give some procedures for testing data delivered by this kind of technology. In particular, the SR-4000 camera by Mesa Imaging AG and the
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In this paper experimental comparisons between two Time-of-Flight (ToF) cameras are reported in order to test their performance and to give some procedures for testing data delivered by this kind of technology. In particular, the SR-4000 camera by Mesa Imaging AG and the CamCube3.0 by PMD Technologies have been evaluated since they have good performances and are well known to researchers dealing with Time-of-Flight (ToF) cameras. After a brief overview of commercial ToF cameras available on the market and the main specifications of the tested devices, two topics are presented in this paper. First, the influence of camera warm-up on distance measurement is analyzed: a warm-up of 40 minutes is suggested to obtain the measurement stability, especially in the case of the CamCube3.0 camera, that exhibits distance measurement variations of several centimeters. Secondly, the variation of distance measurement precision variation over integration time is presented: distance measurement precisions of some millimeters are obtained in both cases. Finally, a comparison between the two cameras based on the experiments and some information about future work on evaluation of sunlight influence on distance measurements are reported. Full article
(This article belongs to the Special Issue Time-of-Flight Range-Imaging Cameras)
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Open AccessArticle A Real-Time Method to Detect and Track Moving Objects (DATMO) from Unmanned Aerial Vehicles (UAVs) Using a Single Camera
Remote Sens. 2012, 4(4), 1090-1111; doi:10.3390/rs4041090
Received: 29 February 2012 / Revised: 11 April 2012 / Accepted: 13 April 2012 / Published: 20 April 2012
Cited by 27 | PDF Full-text (12004 KB) | HTML Full-text | XML Full-text
Abstract
We develop a real-time method to detect and track moving objects (DATMO) from unmanned aerial vehicles (UAVs) using a single camera. To address the challenging characteristics of these vehicles, such as continuous unrestricted pose variation and low-frequency vibrations, new approaches must be developed.
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We develop a real-time method to detect and track moving objects (DATMO) from unmanned aerial vehicles (UAVs) using a single camera. To address the challenging characteristics of these vehicles, such as continuous unrestricted pose variation and low-frequency vibrations, new approaches must be developed. The main concept proposed in this work is to create an artificial optical flow field by estimating the camera motion between two subsequent video frames. The core of the methodology consists of comparing this artificial flow with the real optical flow directly calculated from the video feed. The motion of the UAV between frames is estimated with available parallel tracking and mapping techniques that identify good static features in the images and follow them between frames. By comparing the two optical flows, a list of dynamic pixels is obtained and then grouped into dynamic objects. Tracking these dynamic objects through time and space provides a filtering procedure to eliminate spurious events and misdetections. The algorithms have been tested with a quadrotor platform using a commercial camera. Full article
(This article belongs to the Special Issue Unmanned Aerial Vehicles (UAVs) based Remote Sensing)
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