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Remote Sens., Volume 9, Issue 8 (August 2017)

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Cover Story (view full-size image) Surface inundation is known to have an important impact on biogeochemical, ecological and [...] Read more.
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Open AccessArticle An Integrated Approach to Generating Accurate DTM from Airborne Full-Waveform LiDAR Data
Remote Sens. 2017, 9(8), 871; https://doi.org/10.3390/rs9080871
Received: 1 June 2017 / Revised: 15 August 2017 / Accepted: 20 August 2017 / Published: 22 August 2017
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Abstract
In this study, full-waveform LiDAR data were exploited to detect weak returns backscattered by the bare terrain underneath vegetation canopies and thus improve the generation of a digital terrain model (DTM). Building on the methods of progressive generation of triangulation irregular network (TIN)
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In this study, full-waveform LiDAR data were exploited to detect weak returns backscattered by the bare terrain underneath vegetation canopies and thus improve the generation of a digital terrain model (DTM). Building on the methods of progressive generation of triangulation irregular network (TIN) model reported in the literature, we proposed an integrated approach where echo detection, terrain identification, and TIN generation were carried out iteratively. The proposed method was tested on a dataset collected by a Riegl LMS Q-560 scanner over a study area near Sault Ste. Marie, Ontario, Canada (46°33′56′′N, 83°25′18′′W). The results demonstrated that more terrain points under shrubs could be identified, and the generated DTMs exhibited more details in the terrain than those obtained using the progressive TIN method. In addition, 1275 points across this study area were surveyed on the ground and used to validate the proposed approach. The estimated elevations were shown to have a strong linear relationship with the measured ones, with R2 values above 0.98, and the RMSEs (Root Mean Squared Errors) between them were less than 0.15 m even for areas with hilly terrains underneath vegetation canopies. Full article
(This article belongs to the Section Forest Remote Sensing)
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Open AccessArticle A Study of Spatial Soil Moisture Estimation Using a Multiple Linear Regression Model and MODIS Land Surface Temperature Data Corrected by Conditional Merging
Remote Sens. 2017, 9(8), 870; https://doi.org/10.3390/rs9080870
Received: 25 July 2017 / Revised: 11 August 2017 / Accepted: 19 August 2017 / Published: 22 August 2017
Cited by 1 | PDF Full-text (53630 KB) | HTML Full-text | XML Full-text
Abstract
This study attempts to estimate spatial soil moisture in South Korea (99,000 km2) from January 2013 to December 2015 using a multiple linear regression (MLR) model and the Terra moderate-resolution imaging spectroradiometer (MODIS) land surface temperature (LST) and normalized distribution vegetation
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This study attempts to estimate spatial soil moisture in South Korea (99,000 km2) from January 2013 to December 2015 using a multiple linear regression (MLR) model and the Terra moderate-resolution imaging spectroradiometer (MODIS) land surface temperature (LST) and normalized distribution vegetation index (NDVI) data. The MODIS NDVI was used to reflect vegetation variations. Observed precipitation was measured using the automatic weather stations (AWSs) of the Korea Meteorological Administration (KMA), and soil moisture data were recorded at 58 stations operated by various institutions. Prior to MLR analysis, satellite LST data were corrected by applying the conditional merging (CM) technique and observed LST data from 71 KMA stations. The coefficient of determination (R2) of the original LST and observed LST was 0.71, and the R2 of corrected LST and observed LST was 0.95 for 3 selected LST stations. The R2 values of all corrected LSTs were greater than 0.83 for total 71 LST stations. The regression coefficients of the MLR model were estimated seasonally considering the five-day antecedent precipitation. The p-values of all the regression coefficients were less than 0.05, and the R2 values were between 0.28 and 0.67. The reason for R2 values less than 0.5 is that the soil classification at each observation site was not completely accurate. Additionally, the observations at most of the soil moisture monitoring stations used in this study started in December 2014, and the soil moisture measurements did not stabilize. Notably, R2 and root mean square error (RMSE) in winter were poor, as reflected by the many missing values, and uncertainty existed in observations due to freezing and mechanical errors in the soil. Thus, the prediction accuracy is low in winter due to the difficulty of establishing an appropriate regression model. Specifically, the estimated map of the soil moisture index (SMI) can be used to better understand the severity of droughts with the variability of soil moisture. Full article
(This article belongs to the Special Issue Satellite Remote Sensing for Water Resources in a Changing Climate)
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Open AccessArticle Detection of Asian Dust Storm Using MODIS Measurements
Remote Sens. 2017, 9(8), 869; https://doi.org/10.3390/rs9080869
Received: 30 June 2017 / Revised: 17 August 2017 / Accepted: 19 August 2017 / Published: 22 August 2017
Cited by 1 | PDF Full-text (6407 KB) | HTML Full-text | XML Full-text
Abstract
Every year, a large number of aerosols are released from dust storms into the atmosphere, which may have potential impacts on the climate, environment, and air quality. Detecting dust aerosols and monitoring their movements and evolutions in a timely manner is a very
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Every year, a large number of aerosols are released from dust storms into the atmosphere, which may have potential impacts on the climate, environment, and air quality. Detecting dust aerosols and monitoring their movements and evolutions in a timely manner is a very significant task. Satellite remote sensing has been demonstrated as an effective means for observing dust aerosols. In this paper, an algorithm based on the multi-spectral technique for detecting dust aerosols was developed by combining measurements of moderate resolution imaging spectroradiometer (MODIS) reflective solar bands and thermal emissive bands. Data from dust events that occurred during the past several years were collected as training data for spectral and statistical analyses. According to the spectral curves of various scene types, a series of spectral bands was selected individually or jointly, and corresponding thresholds were defined for step-by-step scene classification. The multi-spectral algorithm was applied mainly to detect dust storms in Asia. The detection results were validated not only visually with MODIS true color images, but also quantitatively with products of Ozone Monitoring Instrument (OMI) and Cloud Aerosol Lidar with Orthogonal Polarization (CALIOP). The validations showed that this multi-spectral detection algorithm was suitable to monitor dust aerosols in the selected study areas. Full article
(This article belongs to the Section Atmosphere Remote Sensing)
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Open AccessArticle Optimal Decision Fusion for Urban Land-Use/Land-Cover Classification Based on Adaptive Differential Evolution Using Hyperspectral and LiDAR Data
Remote Sens. 2017, 9(8), 868; https://doi.org/10.3390/rs9080868
Received: 26 June 2017 / Revised: 15 August 2017 / Accepted: 18 August 2017 / Published: 22 August 2017
Cited by 4 | PDF Full-text (3658 KB) | HTML Full-text | XML Full-text
Abstract
Hyperspectral images and light detection and ranging (LiDAR) data have, respectively, the high spectral resolution and accurate elevation information required for urban land-use/land-cover (LULC) classification. To combine the respective advantages of hyperspectral and LiDAR data, this paper proposes an optimal decision fusion method
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Hyperspectral images and light detection and ranging (LiDAR) data have, respectively, the high spectral resolution and accurate elevation information required for urban land-use/land-cover (LULC) classification. To combine the respective advantages of hyperspectral and LiDAR data, this paper proposes an optimal decision fusion method based on adaptive differential evolution, namely ODF-ADE, for urban LULC classification. In the ODF-ADE framework the normalized difference vegetation index (NDVI), gray-level co-occurrence matrix (GLCM) and digital surface model (DSM) are extracted to form the feature map. The three different classifiers of the maximum likelihood classifier (MLC), support vector machine (SVM) and multinomial logistic regression (MLR) are used to classify the extracted features. To find the optimal weights for the different classification maps, weighted voting is used to obtain the classification result and the weights of each classification map are optimized by the differential evolution algorithm which uses a self-adaptive strategy to obtain the parameter adaptively. The final classification map is obtained after post-processing based on conditional random fields (CRF). The experimental results confirm that the proposed algorithm is very effective in urban LULC classification. Full article
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Open AccessArticle The Effects of Aerosol on the Retrieval Accuracy of NO2 Slant Column Density
Remote Sens. 2017, 9(8), 867; https://doi.org/10.3390/rs9080867
Received: 27 July 2017 / Revised: 16 August 2017 / Accepted: 19 August 2017 / Published: 22 August 2017
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Abstract
We investigate the effects of aerosol optical depth (AOD), single scattering albedo (SSA), aerosol peak height (APH), measurement geometry (solar zenith angle (SZA) and viewing zenith angle (VZA)), relative azimuth angle, and surface reflectance on the accuracy of NO2 slant column density
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We investigate the effects of aerosol optical depth (AOD), single scattering albedo (SSA), aerosol peak height (APH), measurement geometry (solar zenith angle (SZA) and viewing zenith angle (VZA)), relative azimuth angle, and surface reflectance on the accuracy of NO2 slant column density using synthetic radiance. High AOD and APH are found to decrease NO2 SCD retrieval accuracy. In moderately polluted (5 × 1015 molecules cm−2 < NO2 vertical column density (VCD) < 2 × 1016 molecules cm−2) and clean regions (NO2 VCD < 5 × 1015 molecules cm−2), the correlation coefficient (R) between true NO2 SCDs and those retrieved is 0.88 and 0.79, respectively, and AOD and APH are about 0.1 and is 0 km, respectively. However, when AOD and APH are about 1.0 and 4 km, respectively, the R decreases to 0.84 and 0.53 in moderately polluted and clean regions, respectively. On the other hand, in heavily polluted regions (NO2 VCD > 2 × 1016 molecules cm−2), even high AOD and APH values are found to have a negligible effect on NO2 SCD precision. In high AOD and APH conditions in clean NO2 regions, the R between true NO2 SCDs and those retrieved increases from 0.53 to 0.58 via co-adding four pixels spatially, showing the improvement in accuracy of NO2 SCD retrieval. In addition, the high SZA and VZA are also found to decrease the accuracy of the NO2 SCD retrieval. Full article
(This article belongs to the Section Atmosphere Remote Sensing)
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Open AccessArticle Azimuth Ambiguities Removal in Littoral Zones Based on Multi-Temporal SAR Images
Remote Sens. 2017, 9(8), 866; https://doi.org/10.3390/rs9080866
Received: 31 May 2017 / Revised: 16 August 2017 / Accepted: 17 August 2017 / Published: 22 August 2017
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Abstract
Synthetic aperture radar (SAR) is one of the most important techniques for ocean monitoring. Azimuth ambiguities are a real problem in SAR images today, which can cause performance degradation in SAR ocean applications. In particular, littoral zones can be strongly affected by land-based
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Synthetic aperture radar (SAR) is one of the most important techniques for ocean monitoring. Azimuth ambiguities are a real problem in SAR images today, which can cause performance degradation in SAR ocean applications. In particular, littoral zones can be strongly affected by land-based sources, whereas they are usually regions of interest (ROI). Given the presence of complexity and diversity in littoral zones, azimuth ambiguities removal is a tough problem. As SAR sensors can have a repeat cycle, multi-temporal SAR images provide new insight into this problem. A method for azimuth ambiguities removal in littoral zones based on multi-temporal SAR images is proposed in this paper. The proposed processing chain includes co-registration, local correlation, binarization, masking, and restoration steps. It is designed to remove azimuth ambiguities caused by fixed land-based sources. The idea underlying the proposed method is that sea surface is dynamic, whereas azimuth ambiguities caused by land-based sources are constant. Thus, the temporal consistence of azimuth ambiguities is higher than sea clutter. It opens up the possibilities to use multi-temporal SAR data to remove azimuth ambiguities. The design of the method and the experimental procedure are based on images from the Sentinel data hub of Europe Space Agency (ESA). Both Interferometric Wide Swath (IW) and Stripmap (SM) mode images are taken into account to validate the proposed method. This paper also presents two RGB composition methods for better azimuth ambiguities visualization. Experimental results show that the proposed method can remove azimuth ambiguities in littoral zones effectively. Full article
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Open AccessArticle Erosion Associated with Seismically-Induced Landslides in the Middle Longmen Shan Region, Eastern Tibetan Plateau, China
Remote Sens. 2017, 9(8), 864; https://doi.org/10.3390/rs9080864
Received: 17 May 2017 / Revised: 15 August 2017 / Accepted: 18 August 2017 / Published: 21 August 2017
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Abstract
The 2008 Wenchuan earthquake and associated co-seismic landslide was the most recent expression of the rapid deformation and erosion occurring in the eastern Tibetan Plateau. The erosion associated with co-seismic landslides balances the long-term tectonic uplift in the topographic evolution of the region;
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The 2008 Wenchuan earthquake and associated co-seismic landslide was the most recent expression of the rapid deformation and erosion occurring in the eastern Tibetan Plateau. The erosion associated with co-seismic landslides balances the long-term tectonic uplift in the topographic evolution of the region; however, the quantitative relationship between earthquakes, uplift, and erosion is still unknown. In order to quantitatively distinguish the seismically-induced erosion in the total erosion, here, we quantify the Wenchuan earthquake-induced erosion using the digital elevation model (DEM) differential method and previously-reported landslide volumes. Our results show that the seismically-induced erosion is comparable with the pre-earthquake short-term erosion. The seismically-induced erosion rate contributes ~50% of the total erosion rate, which suggests that the local topographic evolution of the middle Longmen Shan region may be closely related to tectonic events, such as the 2008 Wenchuan earthquake. We propose that seismically-induced erosion is a very important component of the total erosion, particularly in active orogenic regions. Our results demonstrate that the remote sensing technique of differential DEM provides a powerful tool for evaluating the volume of co-seismic landslides produced in intermountain regions by strong earthquakes. Full article
(This article belongs to the Special Issue Remote Sensing of Landslides)
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Open AccessTechnical Note A Dynamic Landsat Derived Normalized Difference Vegetation Index (NDVI) Product for the Conterminous United States
Remote Sens. 2017, 9(8), 863; https://doi.org/10.3390/rs9080863
Received: 13 July 2017 / Revised: 15 August 2017 / Accepted: 18 August 2017 / Published: 21 August 2017
Cited by 9 | PDF Full-text (19550 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Satellite derived vegetation indices (VIs) are broadly used in ecological research, ecosystem modeling, and land surface monitoring. The Normalized Difference Vegetation Index (NDVI), perhaps the most utilized VI, has countless applications across ecology, forestry, agriculture, wildlife, biodiversity, and other disciplines. Calculating satellite derived
[...] Read more.
Satellite derived vegetation indices (VIs) are broadly used in ecological research, ecosystem modeling, and land surface monitoring. The Normalized Difference Vegetation Index (NDVI), perhaps the most utilized VI, has countless applications across ecology, forestry, agriculture, wildlife, biodiversity, and other disciplines. Calculating satellite derived NDVI is not always straight-forward, however, as satellite remote sensing datasets are inherently noisy due to cloud and atmospheric contamination, data processing failures, and instrument malfunction. Readily available NDVI products that account for these complexities are generally at coarse resolution; high resolution NDVI datasets are not conveniently accessible and developing them often presents numerous technical and methodological challenges. We address this deficiency by producing a Landsat derived, high resolution (30 m), long-term (30+ years) NDVI dataset for the conterminous United States. We use Google Earth Engine, a planetary-scale cloud-based geospatial analysis platform, for processing the Landsat data and distributing the final dataset. We use a climatology driven approach to fill missing data and validate the dataset with established remote sensing products at multiple scales. We provide access to the composites through a simple web application, allowing users to customize key parameters appropriate for their application, question, and region of interest. Full article
(This article belongs to the Special Issue Google Earth Engine Applications)
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Open AccessArticle Mapping Regional Urban Extent Using NPP-VIIRS DNB and MODIS NDVI Data
Remote Sens. 2017, 9(8), 862; https://doi.org/10.3390/rs9080862
Received: 21 June 2017 / Revised: 5 August 2017 / Accepted: 17 August 2017 / Published: 21 August 2017
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Abstract
The accurate and timely monitoring of regional urban extent is helpful for analyzing urban sprawl and studying environmental issues related to urbanization. This paper proposes a classification scheme for large-scale urban extent mapping by combining the Day/Night Band of the Visible Infrared Imaging
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The accurate and timely monitoring of regional urban extent is helpful for analyzing urban sprawl and studying environmental issues related to urbanization. This paper proposes a classification scheme for large-scale urban extent mapping by combining the Day/Night Band of the Visible Infrared Imaging Radiometer Suite on the Suomi National Polar-orbiting Partnership Satellite (NPP-VIIRS DNB) and the Normalized Difference Vegetation Index from the Moderate Resolution Imaging Spectroradiometer products (MODIS NDVI). A Back Propagation (BP) neural network based one-class classification method, the Present-Unlabeled Learning (PUL) algorithm, is employed to classify images into urban and non-urban areas. Experiments are conducted in mainland China (excluding surrounding islands) to detect urban areas in 2012. Results show that the proposed model can successfully map urban area with a kappa of 0.842 on the pixel level. Most of the urban areas are identified with a producer’s accuracy of 79.63%, and only 10.42% the generated urban areas are misclassified with a user’s accuracy of 89.58%. At the city level, among 647 cities, only four county-level cities are omitted. To evaluate the effectiveness of the proposed scheme, three contrastive analyses are conducted: (1) comparing the urban map obtained in this paper with that generated by the Defense Meteorological Satellite Program/Operational Linescan System Nighttime Light Data (DMSP/OLS NLD) and MODIS NDVI and with that extracted from MCD12Q1 in MODIS products; (2) comparing the performance of the integration of NPP-VIIRS DNB and MODIS NDVI with single input data; and (3) comparing the classification method used in this paper (PUL) with a linear method (Large-scale Impervious Surface Index (LISI)). According to our analyses, the proposed classification scheme shows great potential to map regional urban extents in an effective and efficient manner. Full article
(This article belongs to the Special Issue Recent Advances in Remote Sensing with Nighttime Lights)
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Open AccessArticle Effects of Small-Scale Gold Mining Tailings on the Underwater Light Field in the Tapajós River Basin, Brazilian Amazon
Remote Sens. 2017, 9(8), 861; https://doi.org/10.3390/rs9080861
Received: 15 June 2017 / Revised: 12 August 2017 / Accepted: 15 August 2017 / Published: 21 August 2017
PDF Full-text (11020 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Artisanal and Small-scale Gold Mining (ASGM) within the Amazon region has created several environmental impacts, such as mercury contamination and changes in water quality due to increased siltation. This paper describes the effects of water siltation on the underwater light environment of rivers
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Artisanal and Small-scale Gold Mining (ASGM) within the Amazon region has created several environmental impacts, such as mercury contamination and changes in water quality due to increased siltation. This paper describes the effects of water siltation on the underwater light environment of rivers under different levels of gold mining activities in the Tapajós River Basin. Furthermore, it investigates possible impacts on the phytoplankton community. Two field campaigns were conducted in the Tapajós River Basin, during high water level and during low water level seasons, to measure Inherent and Apparent Optical Properties (IOPs, AOPs), including scattering (b) and absorption (a) coefficients and biogeochemical data (sediment content, pigments, and phytoplankton quantification). The biogeochemical data was separated into five classes according to the concentration of total suspended solids (TSS) ranging from 1.8 mg·L−1 to 113.6 mg·L−1. The in-water light environment varied among those classes due to a wide range of concentrations of inorganic TSS originated from different levels of mining activities. For tributaries with low or no influence of mining tailings (TSS up to 6.8 mg·L−1), waters are relatively more absorbent with b:a ratio of 0.8 at 440 nm and b660 magnitude of 2.1 m−1. With increased TSS loadings from mining operations (TSS over 100 mg·L−1), the scattering process prevails over absorption (b:a ratio of 10.0 at 440 nm), and b660 increases to 20.8 m−1. Non-impacted tributaries presented a critical depth for phytoplankton productivity of up to 6.0 m with available light evenly distributed throughout the spectra. Whereas for greatly impacted waters, attenuation of light was faster, reducing the critical depth to about 1.7 m, with most of the available light comprising of red wavelengths. Overall, a dominance of diatoms was observed for the upstream rivers, whereas cyanobacteria prevailed in the low section of the Tapajós River. The results suggest that the spatial and temporal distribution of phytoplankton in the Tapajós River Basin is not only a function of light availability, but rather depends on the interplay of factors, including flood pulse, water velocity, nutrient availability, and seasonal variation of incoming irradiance. Ongoing research indicates that the effects of mining tailings on the aquatic environment, described here, are occurring in several rivers within the Amazon River Basin. Full article
(This article belongs to the Special Issue Remote Sensing of Water Quality)
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Open AccessArticle Wave Height Estimation from Shadowing Based on the Acquired X-Band Marine Radar Images in Coastal Area
Remote Sens. 2017, 9(8), 859; https://doi.org/10.3390/rs9080859
Received: 27 June 2017 / Revised: 15 August 2017 / Accepted: 17 August 2017 / Published: 21 August 2017
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Abstract
In this paper, the retrieving significant wave height from X-band marine radar images based on shadow statistics is investigated, since the retrieving accuracy can not be seriously affected by environmental factors and the method has the advantage of without any external reference to
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In this paper, the retrieving significant wave height from X-band marine radar images based on shadow statistics is investigated, since the retrieving accuracy can not be seriously affected by environmental factors and the method has the advantage of without any external reference to calibrate. However, the accuracy of the significant wave height estimated from the radar image acquired at the near-shore area is not ideal. To solve this problem, the effect of water depth is considered in the theoretical derivation of estimated wave height based on the sea surface slope. And then, an improved retrieving algorithm which is suitable for both in deep water area and shallow water area is developed. In addition, the radar data are sparsely processed in advance in order to achieve high quality edge image for the requirement of shadow statistic algorithm, since the high resolution radar images will lead to angle-blurred for the image edge detection and time-consuming in the estimation of sea surface slope. The data acquired from Pingtan Test Base in Fujian Province were used to verify the effectiveness of the proposed algorithm. The experimental results demonstrate that the improved method which takes into account the water depth is more efficient and effective and has better performance for retrieving significant wave height in the shallow water area, compared to the in situ buoy data as the ground truth and that of the existing shadow statistic method. Full article
(This article belongs to the Section Ocean Remote Sensing)
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Open AccessArticle Contextual Region-Based Convolutional Neural Network with Multilayer Fusion for SAR Ship Detection
Remote Sens. 2017, 9(8), 860; https://doi.org/10.3390/rs9080860
Received: 21 July 2017 / Revised: 9 August 2017 / Accepted: 9 August 2017 / Published: 20 August 2017
Cited by 9 | PDF Full-text (6002 KB) | HTML Full-text | XML Full-text
Abstract
Synthetic aperture radar (SAR) ship detection has been playing an increasingly essential role in marine monitoring in recent years. The lack of detailed information about ships in wide swath SAR imagery poses difficulty for traditional methods in exploring effective features for ship discrimination.
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Synthetic aperture radar (SAR) ship detection has been playing an increasingly essential role in marine monitoring in recent years. The lack of detailed information about ships in wide swath SAR imagery poses difficulty for traditional methods in exploring effective features for ship discrimination. Being capable of feature representation, deep neural networks have achieved dramatic progress in object detection recently. However, most of them suffer from the missing detection of small-sized targets, which means that few of them are able to be employed directly in SAR ship detection tasks. This paper discloses an elaborately designed deep hierarchical network, namely a contextual region-based convolutional neural network with multilayer fusion, for SAR ship detection, which is composed of a region proposal network (RPN) with high network resolution and an object detection network with contextual features. Instead of using low-resolution feature maps from a single layer for proposal generation in a RPN, the proposed method employs an intermediate layer combined with a downscaled shallow layer and an up-sampled deep layer to produce region proposals. In the object detection network, the region proposals are projected onto multiple layers with region of interest (ROI) pooling to extract the corresponding ROI features and contextual features around the ROI. After normalization and rescaling, they are subsequently concatenated into an integrated feature vector for final outputs. The proposed framework fuses the deep semantic and shallow high-resolution features, improving the detection performance for small-sized ships. The additional contextual features provide complementary information for classification and help to rule out false alarms. Experiments based on the Sentinel-1 dataset, which contains twenty-seven SAR images with 7986 labeled ships, verify that the proposed method achieves an excellent performance in SAR ship detection. Full article
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Open AccessArticle Deriving Hourly PM2.5 Concentrations from Himawari-8 AODs over Beijing–Tianjin–Hebei in China
Remote Sens. 2017, 9(8), 858; https://doi.org/10.3390/rs9080858
Received: 10 June 2017 / Revised: 14 August 2017 / Accepted: 16 August 2017 / Published: 19 August 2017
Cited by 5 | PDF Full-text (3859 KB) | HTML Full-text | XML Full-text
Abstract
Monitoring fine particulate matter with diameters of less than 2.5 μm (PM2.5) is a critical endeavor in the Beijing–Tianjin–Hebei (BTH) region, which is one of the most polluted areas in China. Polar orbit satellites are limited by observation frequency, which is insufficient for
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Monitoring fine particulate matter with diameters of less than 2.5 μm (PM2.5) is a critical endeavor in the Beijing–Tianjin–Hebei (BTH) region, which is one of the most polluted areas in China. Polar orbit satellites are limited by observation frequency, which is insufficient for understanding PM2.5 evolution. As a geostationary satellite, Himawari-8 can obtain hourly optical depths (AODs) and overcome the estimated PM2.5 concentrations with low time resolution. In this study, the evaluation of Himawari-8 AODs by comparing with Aerosol Robotic Network (AERONET) measurements showed Himawari-8 retrievals (Level 3) with a mild underestimate of about −0.06 and approximately 57% of AODs falling within the expected error established by the Moderate-resolution Imaging Spectroradiometer (MODIS) (±(0.05 + 0.15AOD)). Furthermore, the improved linear mixed-effect model was proposed to derive the surface hourly PM2.5 from Himawari-8 AODs from July 2015 to March 2017. The estimated hourly PM2.5 concentrations agreed well with the surface PM2.5 measurements with high R2 (0.86) and low RMSE (24.5 μg/m3). The average estimated PM2.5 in the BTH region during the study time range was about 55 μg/m3. The estimated hourly PM2.5 concentrations ranged extensively from 35.2 ± 26.9 μg/m3 (1600 local time) to 65.5 ± 54.6 μg/m3 (1100 local time) at different hours. Full article
(This article belongs to the Special Issue Remote Sensing of Atmospheric Pollution) Printed Edition available
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Open AccessArticle A Robust Algorithm for Estimating Surface Fractional Vegetation Cover from Landsat Data
Remote Sens. 2017, 9(8), 857; https://doi.org/10.3390/rs9080857
Received: 7 July 2017 / Revised: 10 August 2017 / Accepted: 10 August 2017 / Published: 19 August 2017
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Abstract
Fractional vegetation cover (FVC) is an essential land surface parameter for Earth surface process simulations and global change studies. The currently existing FVC products are mostly obtained from low or medium resolution remotely sensed data, while many applications require the fine spatial resolution
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Fractional vegetation cover (FVC) is an essential land surface parameter for Earth surface process simulations and global change studies. The currently existing FVC products are mostly obtained from low or medium resolution remotely sensed data, while many applications require the fine spatial resolution FVC product. The availability of well-calibrated coverage of Landsat imagery over large areas offers an opportunity for the production of FVC at fine spatial resolution. Therefore, the objective of this study is to develop a general and reliable land surface FVC estimation algorithm for Landsat surface reflectance data under various land surface conditions. Two machine learning methods multivariate adaptive regression splines (MARS) model and back-propagation neural networks (BPNNs) were trained using samples from PROSPECT leaf optical properties model and the scattering by arbitrarily inclined leaves (SAIL) model simulations, which included Landsat reflectance and corresponding FVC values, and evaluated to choose the method which had better performance. Thereafter, the MARS model, which had better performance in the independent validation, was evaluated using ground FVC measurements from two case study areas. The direct validation of the FVC estimated using the proposed algorithm (Heihe: R2 = 0.8825, RMSE = 0.097; Chengde using Landsat 7 ETM+: R2 = 0.8571, RMSE = 0.078, Chengde using Landsat 8 OLI: R2 = 0.8598, RMSE = 0.078) showed the proposed method had good performance. Spatial-temporal assessment of the estimated FVC from Landsat 7 ETM+ and Landsat 8 OLI data confirmed the robustness and consistency of the proposed method. All these results indicated that the proposed algorithm could obtain satisfactory accuracy and had the potential for the production of high-quality FVC estimates from Landsat surface reflectance data. Full article
(This article belongs to the Section Remote Sensing in Agriculture and Vegetation)
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Open AccessArticle A Hierarchical Extension of General Four-Component Scattering Power Decomposition
Remote Sens. 2017, 9(8), 856; https://doi.org/10.3390/rs9080856
Received: 16 July 2017 / Revised: 16 August 2017 / Accepted: 16 August 2017 / Published: 18 August 2017
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Abstract
The overestimation of volume scattering (OVS) is an intrinsic drawback in model-based polarimetric synthetic aperture radar (PolSAR) target decomposition. It severely impacts the accuracy measurement of scattering power and leads to scattering mechanism ambiguity. In this paper, a hierarchical extended general four-component scattering
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The overestimation of volume scattering (OVS) is an intrinsic drawback in model-based polarimetric synthetic aperture radar (PolSAR) target decomposition. It severely impacts the accuracy measurement of scattering power and leads to scattering mechanism ambiguity. In this paper, a hierarchical extended general four-component scattering power decomposition method (G4U) is presented. The conventional G4U is first proposed by Singh et al. and it has advantages in full use of information and volume scattering characterization. However, the OVS still exists in the G4U and it causes a scattering mechanism ambiguity in some oriented urban areas. In the proposed method, matrix rotations by the orientation angle and the helix angle are applied. Afterwards, the transformed coherency matrix is applied to the four-component decomposition scheme with two refined models. Moreover, the branch condition applied in the G4U is substituted by the ratio of correlation coefficient (RCC), which is used as a criterion for hierarchically implementing the decomposition. The performance of this approach is demonstrated and evaluated with the Airborne Synthetic Aperture Radar (AIRSAR), Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR), Radarsat-2, and the Advanced Land Observing Satellite (ALOS) Phased Array type L-band Synthetic Aperture Radar (PALSAR) fully polarimetric data over different test sites. Comparison studies are carried out and demonstrated that the proposed method exhibits promising improvements in the OVS and scattering mechanism characterization. Full article
(This article belongs to the Section Remote Sensing Image Processing)
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