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Deep Distillation Recursive Network for Remote Sensing Imagery Super-Resolution
摘要： Deep convolutional neural networks (CNNs) have been widely used and achieved state-of-the-art performance in many image or video processing and analysis tasks. In particular, for image super-resolution (SR) processing, previous CNN-based methods have led to significant improvements, when compared with shallow learning-based methods. However, previous CNN-based algorithms with simple direct or skip connections are of poor performance when applied to remote sensing satellite images SR. In this study, a simple but effective CNN framework, namely deep distillation recursive network (DDRN), is presented for video satellite image SR. DDRN includes a group of ultra-dense residual blocks (UDB), a multi-scale purification unit (MSPU), and a reconstruction module. In particular, through the addition of rich interactive links in and between multiple-path units in each UDB, features extracted from multiple parallel convolution layers can be shared effectively. Compared with classical dense-connection-based models, DDRN possesses the following main properties. (1) DDRN contains more linking nodes with the same convolution layers. (2) A distillation and compensation mechanism, which performs feature distillation and compensation in different stages of the network, is also constructed. In particular, the high-frequency components lost during information propagation can be compensated in MSPU. (3) The final SR image can benefit from the feature maps extracted from UDB and the compensated components obtained from MSPU. Experiments on Kaggle Open Source Dataset and Jilin-1 video satellite images illustrate that DDRN outperforms the conventional CNN-based baselines and some state-of-the-art feature extraction approaches.
关键词： feature distillation,compensation unit,ultra-dense connection,super-resolution,video satellite,remote sensing imagery
更新于2025-09-23 15:22:29
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Ocean Dynamics Observed by VIIRS Day/Night Band Satellite Observations
摘要： Three cases of Day/Night Band (DNB) observations of the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi National Polar-orbiting Partnership (SNPP) are explored for applications to assess the ocean environment and monitor ocean dynamics. An approach to use the ratio between the target radiance and the reference radiance was developed in order to better assess the ocean diurnal and short-term environmental changes with VIIRS DNB observations. In the La Plata River Estuary, the sediment fronts showed 20–25 km diurnal inshore-offshore movements on 13 March 2017. In the waters off the coast of Argentina in the South Atlantic, VIIRS DNB measurements provided both daytime and nighttime observations and monitoring of the algal bloom development and migration between 24 and 26 March 2016. This algal bloom generally kept the same spatial patterns, but moved nearly 20 km eastward in the three-day period. In the Yangtze River Estuary and Hangzhou Bay region along China’s east coast, VIIRS DNB observations also revealed complicated coastal dynamic changes between 12 and 14 April 2017. Even though there are still some challenges and limitations for monitoring the ocean environment with VIIRS DNB observations, this study shows that satellite DNB observations can provide additional data sources for ocean observations, especially observations during the nighttime.
关键词： satellite remote sensing,VIIRS,DNB observation,ocean color,nocturnal study,ocean dynamics
更新于2025-09-23 15:21:01
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Can Multispectral Information Improve Remotely Sensed Estimates of Total Suspended Solids? A Statistical Study in Chesapeake Bay
摘要： Total suspended solids (TSS) is an important environmental parameter to monitor in the Chesapeake Bay due to its effects on submerged aquatic vegetation, pathogen abundance, and habitat damage for other aquatic life. Chesapeake Bay is home to an extensive and continuous network of in situ water quality monitoring stations that include TSS measurements. Satellite remote sensing can address the limited spatial and temporal extent of in situ sampling and has proven to be a valuable tool for monitoring water quality in estuarine systems. Most algorithms that derive TSS concentration in estuarine environments from satellite ocean color sensors utilize only the red and near-infrared bands due to the observed correlation with TSS concentration. In this study, we investigate whether utilizing additional wavelengths from the Moderate Resolution Imaging Spectroradiometer (MODIS) as inputs to various statistical and machine learning models can improve satellite-derived TSS estimates in the Chesapeake Bay. After optimizing the best performing multispectral model, a Random Forest regression, we compare its results to those from a widely used single-band algorithm for the Chesapeake Bay. We find that the Random Forest model modestly outperforms the single-band algorithm on a holdout cross-validation dataset and offers particular advantages under high TSS conditions. We also find that both methods are similarly generalizable throughout various partitions of space and time. The multispectral Random Forest model is, however, more data intensive than the single band algorithm, so the objectives of the application will ultimately determine which method is more appropriate.
关键词： water quality,total suspended solids,ocean color,satellite remote sensing,statistical analysis,Random Forest,Chesapeake Bay,multispectral,machine learning
更新于2025-09-19 17:15:36
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[IEEE 2019 Photonics North (PN) - Quebec City, QC, Canada (2019.5.21-2019.5.23)] 2019 Photonics North (PN) - Real-time Optical Beam Steering for Laser-Powered Epiretinal Prostheses
摘要： We propose a new deterministic approach for remote sensing retrieval, called modified total least squares (MTLS), built upon the total least squares (TLS) technique. MTLS implicitly determines the optimal regularization strength to be applied to the normal equation first-order Newtonian retrieval using all of the noise terms embedded in the residual vector. The TLS technique does not include any constraint to prevent noise enhancement in the state space parameters from the existing noise in measurement space for an inversion with an ill-conditioned Jacobian. To stabilize the noise propagation into parameter space, we introduce an additional empirically derived regularization proportional to the logarithm of the condition number of the Jacobian and inversely proportional to the L2-norm of the residual vector. The derivation, operational advantages and use of the MTLS method are demonstrated by retrieving sea surface temperature from GOES-13 satellite measurements. An analytic equation is derived for the total retrieval error, and is shown to agree well with the observed error. This can also serve as a quality indicator for pixel-level retrievals. We also introduce additional tests from the MTLS solutions to identify contaminated pixels due to residual clouds, error in the water vapor profile and aerosols. Comparison of the performances of our new and other methods, namely, optimal estimation and regression-based retrieval, is performed to understand the relative prospects and problems associated with these methods. This was done using operational match-ups for 42 months of data, and demonstrates a relatively superior temporally consistent performance of the MTLS technique.
关键词： ill-conditioned inverse methods,regularization,total error,total least squares (TLS),sea surface temperature (SST),Condition number of matrix,satellite remote sensing
更新于2025-09-19 17:13:59
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An Enhanced One-Port Waveguide Method for Sheet Resistance Extraction
摘要： We propose a new deterministic approach for remote sensing retrieval, called modified total least squares (MTLS), built upon the total least squares (TLS) technique. MTLS implicitly determines the optimal regularization strength to be applied to the normal equation first-order Newtonian retrieval using all of the noise terms embedded in the residual vector. The TLS technique does not include any constraint to prevent noise enhancement in the state space parameters from the existing noise in measurement space for an inversion with an ill-conditioned Jacobian. To stabilize the noise propagation into parameter space, we introduce an additional empirically derived regularization proportional to the logarithm of the condition number of the Jacobian and inversely proportional to the L2-norm of the residual vector. The derivation, operational advantages and use of the MTLS method are demonstrated by retrieving sea surface temperature from GOES-13 satellite measurements. An analytic equation is derived for the total retrieval error, and is shown to agree well with the observed error. This can also serve as a quality indicator for pixel-level retrievals. We also introduce additional tests from the MTLS solutions to identify contaminated pixels due to residual clouds, error in the water vapor profile and aerosols. Comparison of the performances of our new and other methods, namely, optimal estimation and regression-based retrieval, is performed to understand the relative prospects and problems associated with these methods. This was done using operational match-ups for 42 months of data, and demonstrates a relatively superior temporally consistent performance of the MTLS technique.
关键词： total error,sea surface temperature (SST),total least squares (TLS),Condition number of matrix,ill-conditioned inverse methods,regularization,satellite remote sensing
更新于2025-09-16 10:30:52
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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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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