- 标题
- 摘要
- 关键词
- 实验方案
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Hyperspectral Shallow-Water Remote Sensing with an Enhanced Benthic Classifier
摘要： Hyperspectral remote sensing inversion models utilize spectral information over optically shallow waters to retrieve optical properties of the water column, bottom depth and re?ectance, with the latter used in benthic classi?cation. Accuracy of these retrievals is dependent on the spectral endmember(s) used to model the bottom re?ectance during the inversion. Without prior knowledge of these endmember(s) current approaches must iterate through a list of endmember—a computationally demanding task. To address this, a novel lookup table classi?cation approach termed HOPE-LUT was developed for selecting the likely benthic endmembers of any hyperspectral image pixel. HOPE-LUT classi?es a pixel as sand, mixture or non-sand, then the latter two are resolved into the three most likely classes. Optimization subsequently selects the class (out of the three) that generated the best ?t to the remote sensing re?ectance. For a coral reef case, modeling results indicate very high benthic classi?cation accuracy (>90%) for depths less than 4 m of common coral reef benthos. These accuracies decrease substantially with increasing depth due to the loss of bottom information, especially the spectral signatures. We applied this technique to hyperspectral airborne imagery of Heron Reef, Great Barrier Reef and generated benthic habitat maps with higher classi?cation accuracy compared to standard inversion models.
关键词： hyperspectral,remote sensing,benthic classi?cation,coral reef,heron reef
更新于2025-09-04 15:30:14
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Hybrid Collaborative Representation for Remote-Sensing Image Scene Classification
摘要： In recent years, the collaborative representation-based classification (CRC) method has achieved great success in visual recognition by directly utilizing training images as dictionary bases. However, it describes a test sample with all training samples to extract shared attributes and does not consider the representation of the test sample with the training samples in a specific class to extract the class-specific attributes. For remote-sensing images, both the shared attributes and class-specific attributes are important for classification. In this paper, we propose a hybrid collaborative representation-based classification approach. The proposed method is capable of improving the performance of classifying remote-sensing images by embedding the class-specific collaborative representation to conventional collaborative representation-based classification. Moreover, we extend the proposed method to arbitrary kernel space to explore the nonlinear characteristics hidden in remote-sensing image features to further enhance classification performance. Extensive experiments on several benchmark remote-sensing image datasets were conducted and clearly demonstrate the superior performance of our proposed algorithm to state-of-the-art approaches.
关键词： remote-sensing images,kernel space,collaborative representation,hybrid collaborative representation
更新于2025-09-04 15:30:14
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An Object Model for Integrating Diverse Remote Sensing Satellite Sensors: A Case Study of Union Operation
摘要： In the Earth Observation sensor web environment, the rapid, accurate, and unified discovery of diverse remote sensing satellite sensors, and their association to yield an integrated solution for a comprehensive response to specific emergency tasks pose considerable challenges. In this study, we propose a remote sensing satellite sensor object model, based on the object-oriented paradigm and the Open Geospatial Consortium Sensor Model Language. The proposed model comprises a set of sensor resource objects. Each object consists of identification, state of resource attribute, and resource method. We implement the proposed attribute state description by applying it to different remote sensors. A real application, involving the observation of floods at the Yangtze River in China, is undertaken. Results indicate that the sensor inquirer can accurately discover qualified satellite sensors in an accurate and unified manner. By implementing the proposed union operation among the retrieved sensors, the inquirer can further determine how the selected sensors can collaboratively complete a specific observation requirement. Therefore, the proposed model provides a reliable foundation for sharing and integrating multiple remote sensing satellite sensors and their observations.
关键词： sensor web,sensor metadata,object-oriented paradigm,SensorML,remote sensing satellite sensor,sensor sharing and integration
更新于2025-09-04 15:30:14
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Quantifying lower tropospheric methane concentrations using GOSAT near-IR and TES thermal IR measurements
摘要： Evaluating surface fluxes of CH4 using total column data requires models to accurately account for the transport and chemistry of methane in the free troposphere and stratosphere, thus reducing sensitivity to the underlying fluxes. Vertical profiles of methane have increased sensitivity to surface fluxes because lower tropospheric methane is more sensitive to surface fluxes than a total column, and quantifying free-tropospheric CH4 concentrations helps to evaluate the impact of transport and chemistry uncertainties on estimated surface fluxes. Here we demonstrate the potential for estimating lower tropospheric CH4 concentrations through the combination of free-tropospheric methane measurements from the Aura Tropospheric Emission Spectrometer (TES) and XCH4 (dry-mole air fraction of methane) from the Greenhouse gases Observing SATellite – Thermal And Near-infrared for carbon Observation (GOSAT TANSO, herein GOSAT for brevity). The calculated precision of these estimates ranges from 10 to 30 ppb for a monthly average on a 4? × 5? latitude/longitude grid making these data suitable for evaluating lower-tropospheric methane concentrations. Smoothing error is approximately 10 ppb or less. Comparisons between these data and the GEOS-Chem model demonstrate that these lower-tropospheric CH4 estimates can resolve enhanced concentrations over flux regions that are challenging to resolve with total column measurements. We also use the GEOS-Chem model and surface measurements in background regions across a range of latitudes to determine that these lower-tropospheric estimates are biased low by approximately 65 ppb, with an accuracy of approximately 6 ppb (after removal of the bias) and an actual precision of approximately 30 ppb. This 6 ppb accuracy is consistent with the accuracy of TES and GOSAT methane retrievals.
关键词： methane,remote sensing,GOSAT,TES,lower troposphere
更新于2025-09-04 15:30:14
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Development of a Multispectral Albedometer and Deployment on an Unmanned Aircraft for Evaluating Satellite Retrieved Surface Reflectance over Nevada’s Black Rock Desert
摘要： Bright surfaces across the western U.S. lead to uncertainties in satellite derived aerosol optical depth (AOD) where AOD is typically overestimated. With this in mind, a compact and portable instrument was developed to measure surface albedo on an unmanned aircraft system (UAS). This spectral albedometer uses two Hamamatsu micro-spectrometers (range: 340–780 nm) for measuring incident and reflected solar radiation at the surface. The instrument was deployed on 5 October 2017 in Nevada’s Black Rock Desert (BRD) to investigate a region of known high surface reflectance for comparison with albedo products from satellites. It was found that satellite retrievals underestimate surface reflectance compared to the UAS mounted albedometer. To highlight the importance of surface reflectance on the AOD from satellite retrieval algorithms, a 1-D radiative transfer model was used. The simple model was used to determine the sensitivity of AOD with respect to the change in albedo and indicates a large sensitivity of AOD retrievals to surface reflectance for certain combinations of surface albedo and aerosol optical properties. This demonstrates the need to increase the number of surface albedo measurements and an intensive evaluation of albedo satellite retrievals to improve satellite-derived AOD. The portable instrument is suitable for other applications as well.
关键词： UAS,UAV,MODIS,albedo,LANDSAT,drone,satellite remote sensing,AOD,unmanned aircraft system
更新于2025-09-04 15:30:14
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Sulfur Dioxide (SO2) Monitoring Over Kirkuk City Using Remote Sensing Data
摘要： Air pollution mapping is now being an important issue to manage and enhance the environment of a city. The major problems of air pollution mapping is the data acquisition due to the high cost of instruments and the high spatial distribution requirements. This study aimed to monitor Sulfur Dioxide over Kirkuk city using Landsat-8 thermal bands to provide Department of Environment Kirkuk with low-cost Sulfur Dioxide concentration maps to better manage the city. The study used correlation analysis to find a relationship between Sulfur Dioxide ground-based measurements and satellite data. The ground-based measurements were collected from (17) stations distributed in Kirkuk city in January, 2014 using NOVA device to measure SO2 concentrations. The research showed a good correlation between ground- based measurements and satellite data with (R2=0.48 for band 11 and R2= 0.52 for band 10). Therefore, the study resulted that with band 10 of Landsat-8 data, better SO2 can be monitored than using band 11. It is recommended to other researchers to investigate the ability of free remote sensing data to monitor all elements that specify the air quality of a city.
关键词： LST,Environment,Kirkuk city,SO2,TIRS,Remote sensing,GIS,Air pollution,Sulfur dioxide
更新于2025-09-04 15:30:14
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Effects of daily meteorology on the interpretation of space-based remote sensing of NO<sub>2</sub>
摘要： Retrievals of tropospheric NO2 columns from UV/visible observations of reflected sunlight require a priori vertical profiles to account for the variation in sensitivity of the observations to NO2 at different altitudes. These profiles vary in space and time but are usually approximated using models that do not resolve the full details of this variation. Currently, no operational retrieval simulates these a priori profiles at both high spatial and high temporal resolution. Here we examine the additional benefits of daily variations in a priori profiles for retrievals already simulating a priori NO2 profiles at sufficiently high spatial resolution to identify variations of NO2 within urban and power plant plumes. We show the effects of introducing daily variation into a priori profiles can be as large as 40% and 3×1015 molec. cm?2 for an individual day and lead to corrections as large as 10% for a monthly average in a case study of Atlanta, GA. Comparing an optimized retrieval to a more standard one, we find that NOx emissions estimated from space-based remote sensing can increase by ～100% when daily variations in plume location and shape are accounted for in the retrieval.
关键词： a priori profiles,emissions,satellite remote sensing,NO2,air quality
更新于2025-09-04 15:30:14
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Detection of coarse woody debris using airborne light detection and ranging (LiDAR)
摘要： Coarse woody debris (CWD) is an essential component of forest ecosystems that provides habitat for diverse species, functions in water and nutrient cycling, and can be a potential surface fuel in wildfires. CWD detection and mapping would enhance forestry and wildlife research and management but passive remote sensing technologies cannot provide information on features beneath forest canopy, while field-based CWD inventories are not practical for mapping CWD over large areas. Airborne light detecting and ranging (LiDAR) is a remote sensing technology that provides detailed information on three-dimensional vegetation structure that could overcome limitations of passive remote sensing technologies and field-based inventories. Our objectives were to evaluate whether airborne LiDAR could be used to detect individual pieces of CWD. We measured 1679 pieces of CWD at 144 field plots from 2015 to 2016. We acquired high-density (~24 first returns/m2) LiDAR data in 2014, filtered out canopy and sub-canopy returns using a height threshold based on field measurements of CWD, and used height-filtered data to determine which field-measured pieces of CWD were visible in the resulting point cloud. CWD pieces that were detected constituted 50% of plot CWD volume, and there was a strong, positive correlation between total plot CWD volume and volume of detected pieces (r = 0.96). Overall, we detected 23% of the individual pieces of CWD we measured. Large pieces of CWD were most likely to be detected, with the majority of pieces ≥30 cm diameter or ≥13.9 m long detected. Canopy density, shrub density, and forest type did not influence detection probability. CWD detection rates increased from 1 pulses/m2 to 16 pulses/m2, and CWD detection rate was constant from 16 pulses/m2 to 24 pulses/m2. Our results demonstrate that airborne LiDAR can be used to detect CWD. LiDAR-based detection and mapping of CWD will be most useful for applications that focus on larger and longer pieces of CWD or applications focused on total CWD volume.
关键词： Wildlife habitat,Coarse woody debris,Remote sensing,LiDAR
更新于2025-09-04 15:30:14
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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Fast Sample Generation with Variational Bayesian for Limited Data Hyperspectral Image Classification
摘要： Labeling data for hyperspectral remote sensing image classification is a tedious and cost-intensive task. As a consequence, it is oftentimes necessary to perform classification when only very limited number of labeled training data is available. Several approaches have been proposed to address this problem. A recent proposal is to generate additional synthetic samples from a Gaussian Mixture Model for each class. One challenge with this approach lies in determining the number of components in the GMM. In this paper, we propose an approximation algorithm to select the number of components, namely Variational Bayesian (VB). The main advantage of VB is that it does not require multiple clustering computations in advance. Variational Bayesian not only greatly decreases the computational cost, but also generates comparable or better results in comparison to other methods.
关键词： synthetic data,hyperspectral remote sensing image classification,limited training data,Gaussian mixture model (GMM),Variational Bayesian (VB)
更新于2025-09-04 15:30:14
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A synthetic review of remote sensing applications to detect nearshore bars
摘要： Nearshore bars are important morphologic features associated with intermediate and dissipative natural beaches. Bars impact the direction, magnitude, and patterns of sediment transport in the nearshore. They serve as a buffer against extreme and meso-scale events. In this review article, we investigate remotely-based observations, specifically near-Earth and satellite imagery, which have been used to investigate nearshore bars. Several recent advances in technology and techniques allow the remote measurement of bar width and height, beach slope, shoreline orientation, and bar count. Video monitoring imagery is presently the most popular method to derive these data. However, spatial prediction models using satellite imagery can also provide reliable bar morphodynamic information.
关键词： Bar monitoring,Coastal morphodynamics,Remote sensing,Geomorphology,Bar storm response
更新于2025-09-04 15:30:14