Experimental study on the variation of optical remote sensing imaging characteristics of internal solitary waves with wind speed

Optical remote sensing has been widely used to study internal solitary waves(ISWs).Wind speed has an important effect on ISW imaging of optical remote sensing.The light and dark bands of ISWs cannot be observed by optical remote sensing when the wind is too strong.The relationship between the characteristics of ISWs bands in optical remote sensing images and the wind speed is still unclear.The influence of wind speeds on the characteristics of the ISWs bands is investigated based on the physical simulation experiments with the wind speeds of 1.6,3.1,3.5,3.8,and 3.9 m/s.The experimental results show that when the wind speed is 3.9 m/s,the ISWs bands cannot be observed in optical remote sensing images with the stratification of h_(1)∶h_(2)=7∶58,ρ_(1)∶ρ_(2)=1∶1.04.When the wind speeds are 3.1,3.5,and 3.8 m/s,which is lower than 3.9 m/s,the ISWs bands can be obtained in the simulated optical remote sensing image.The location of the band’s dark and light extremum and the band’s peak-to-peak spacing are almost not affected by wind speed.More-significant wind speeds can cause a greater gray difference of the light-dark bands.This provided a scientific basis for further understanding of ISW optical remote sensing imaging.

Optical remote sensing image characteristics of large amplitude convex mode-2 internal solitary waves:an experimental study

A series of experiments are designed to propose a new method to study the characteristics of convex mode-2internal solitary waves(ISWs)in optical remote sensing images using a laboratory-based optical remote sensing simulation platform.The corresponding wave parameters of large-amplitude convex mode-2 ISWs under smooth surfaces are investigated along with the optical remote sensing characteristic parameters.The mode-2 ISWs in the experimentally obtained optical remote sensing image are produced by their overall modulation effect on the water surface,and the extreme points of the gray value of the profile curve of bright-dark stripes appear at the same location as the real optical remote sensing image.The present data extend to a larger range than previous studies,and for the characteristics of large amplitude convex mode-2 ISWs,the experimental results show a second-order dependence of wavelength on amplitude.There is a close relationship between optical remote sensing characteristic parameters and wave parameters of mode-2 ISWs,in which there is a positive linear relationship between the bright-dark spacing and wavelength and a nonlinear relationship with the amplitude,especially when the amplitude is very large,there is a significant increase in bright-dark spacing.

An Intelligent Detection Method for Optical Remote Sensing Images Based on Improved YOLOv7

To address the issue of imbalanced detection performance and detection speed in current mainstream object detection algorithms for optical remote sensing images,this paper proposes a multi-scale object detection model for remote sensing images on complex backgrounds,called DI-YOLO,based on You Only Look Once v7-tiny(YOLOv7-tiny).Firstly,to enhance the model’s ability to capture irregular-shaped objects and deformation features,as well as to extract high-level semantic information,deformable convolutions are used to replace standard convolutions in the original model.Secondly,a Content Coordination Attention Feature Pyramid Network(CCA-FPN)structure is designed to replace the Neck part of the original model,which can further perceive relationships between different pixels,reduce feature loss in remote sensing images,and improve the overall model’s ability to detect multi-scale objects.Thirdly,an Implicitly Efficient Decoupled Head(IEDH)is proposed to increase the model’s flexibility,making it more adaptable to complex detection tasks in various scenarios.Finally,the Smoothed Intersection over Union(SIoU)loss function replaces the Complete Intersection over Union(CIoU)loss function in the original model,resulting in more accurate prediction of bounding boxes and continuous model optimization.Experimental results on the High-Resolution Remote Sensing Detection(HRRSD)dataset demonstrate that the proposed DI-YOLO model outperforms mainstream target detection algorithms in terms of mean Average Precision(mAP)for optical remote sensing image detection.Furthermore,it achieves Frames Per Second(FPS)of 138.9,meeting fast and accurate detection requirements.

Mapping the bathymetry of shallow coastal water using singleframe fine-resolution optical remote sensing imagery

This paper presents a bathymetry inversion method using single-frame fine-resolution optical remote sensing imagery based on ocean-wave refraction and shallow-water wave theory. First, the relationship among water depth, wavelength and wave radian frequency in shallow water was deduced based on shallow-water wave theory. Considering the complex wave distribution in the optical remote sensing imagery, Fast Fourier Transform （FFT） and spatial profile measurements were applied for measuring the wavelengths. Then, the wave radian frequency was calculated by analyzing the long-distance fluctuation in the wavelength, which solved a key problem in obtaining the wave radian frequency in a single-frame image. A case study was conducted for Sanya Bay of Hainan Island, China. Single-flame fine-resolution optical remote sensing imagery from QuickBird satellite was used to invert the bathymetry without external input parameters. The result of the digital elevation model （DEM） was evaluated against a sea chart with a scale of 1：25 000. The root-mean-square error of the inverted bathymetry was 1.07 m, and the relative error was 16.2%. Therefore, the proposed method has the advantages including no requirement for true depths and environmental parameters, and is feasible for mapping the bathymetry of shallow coastal water.

Influences of Atmospheric Turbulence on Image Resolution of Airborne and Space-Borne Optical Remote Sensing System

A new way is proposed to evaluate the influence of atmospheric turbulence on image resolution of airborne and space-borne optical remote sensing system, which is called as arrival angle-method. Applying this method, some engineering examples are selected to analyze the turbulence influences on image resolution based on three different atmospheric turbulence models quantificationally, for the airborne remote sensing system, the resolution errors caused by the atmospheric turbulence are less than 1 cm, and for the space-borne remote sensing system, the errors are around 1 cm. The results are similar to that obtained by the previous Friedmethod. Compared with the Fried-method, the arrival angle-method is rather simple and can be easily used in engineering fields.

China’s high-resolution optical remote sensing satellites and their mapping applications

Since the beginning of the twenty-first century,several countries have made great efforts to develop space remote sensing for building a high-resolution earth observation system.Under the great attention of the government and the guidance of the major scientific and technological project of the high-resolution earth observation system,China has made continuous breakthroughs and progress in high-resolution remote sensing imaging technology.The development of domestic high-resolution remote sensing satellites shows a vigorous trend,and consequently,a relatively stable and perfect high-resolution earth observation system has been formed.The development of high-resolution remote sensing satellites has greatly promoted and enriched modern mapping technologies and methods.In this paper,the development status,along with mapping modes and applications of China’s high-resolution remote sensing satellites are reviewed,and the development trend in high-resolution earth observation system for global and ground control-free mapping is discussed,providing a reference for the subsequent development of high-resolution remote sensing satellites in China.

Progress in monitoring high-temperature damage to rice through satellite and ground-based optical remote sensing

The occurrence of rice high-temperature damage (HTD) has increased with global warming. Cultivation of rice is seriously affected by the HTD in the middle and lower reaches of the Yangtze River, which directly affects food security in this region and in the whole of China. It is important to monitor and assess crop HTD using satellite remote sensing information. This paper reviews the recent development of monitoring rice HTD using optical remote sensing information. It includes the use of optical remote sensing information to obtain the regional spatial distribution of high temperatures, mixed-surface temperature retrieval for rice fields based on mixed decomposition information, the development of field and thermal infrared testing and modeling, and the satellite/ground-based remote sensing coupled method for monitoring rice HTD. Finally, the prospects for monitoring crop HTD based on remote sensing information are summarized.

Ship detection and classification from optical remote sensing images: A survey

Considering the important applications in the military and the civilian domain, ship detection and classification based on optical remote sensing images raise considerable attention in the sea surface remote sensing filed. This article collects the methods of ship detection and classification for practically testing in optical remote sensing images, and provides their corresponding feature extraction strategies and statistical data. Basic feature extraction strategies and algorithms are analyzed associated with their performance and application in ship detection and classification.Furthermore, publicly available datasets that can be applied as the benchmarks to verify the effectiveness and the objectiveness of ship detection and classification methods are summarized in this paper. Based on the analysis, the remaining problems and future development trends are provided for ship detection and classification methods based on optical remote sensing images.

Optical Remote-sensing Monitoring and Forecasting of Atmospheric Pollution in Huaibei Area, China

Huaibei is an energy city. Coal as the primary energy consumption brings a large number of regional pollution in Huaibei area. Differential optical absorption spectroscopy （DOAS） as optical remote sensing technology has been applied to monitor regional average concen- trations and inventory of nitrogen dioxide, sulfur dioxide and ozone. DOAS system was set up and applied to monitor the main air pollutants in Huaibei area. Monitoring data were obtained from 7 to 28 August, 2011. Monitoring results show measurements in controlling pollution are effective, and emissions of pollutants are up to the national standard in Huaibei area. Prediction model was also created to track changing trend of pollutions. These will provide raw data support for effective evaluation of environmental quality in Huaibei area.

An internal-external optimized convolutional neural network for arbitrary orientated object detection from optical remote sensing images

Due to the bird’s eye view of remote sensing sensors,the orientational information of an object is a key factor that has to be considered in object detection.To obtain rotating bounding boxes,existing studies either rely on rotated anchoring schemes or adding complex rotating ROI transfer layers,leading to increased computational demand and reduced detection speeds.In this study,we propose a novel internal-external optimized convolutional neural network for arbitrary orientated object detection in optical remote sensing images.For the internal opti-mization,we designed an anchor-based single-shot head detector that adopts the concept of coarse-to-fine detection for two-stage object detection networks.The refined rotating anchors are generated from the coarse detection head module and fed into the refining detection head module with a link of an embedded deformable convolutional layer.For the external optimiza-tion,we propose an IOU balanced loss that addresses the regression challenges related to arbitrary orientated bounding boxes.Experimental results on the DOTA and HRSC2016 bench-mark datasets show that our proposed method outperforms selected methods.

Modified optical remote sensing algorithms for the Pearl River Estuary

This study aims to develop new algorithms to retrieve sea surface parameters including concentrations of Chlorophyll a （Chl a） and Suspended Particulate Matter （SPM）, and absorbance of Colored Dissolved Organic Matter （aCDOM） by incorporating the contribution of red bands to make them more adaptable to case 2 waters. Optical remote sensing algorithms have demonstrated efficient retrieval of Chl a, SPM, and aCDOM, yet they are not very accurate especially for coastal areas. It has also been found that the default algorithm has overestimated Chl a in the Pearl River Estuary, and shown poor correlation for CDOM absorbance. By incorporating the red band ratios into the algorithm, a correction effect has been shown, which improves the accuracy of quantifying the actual concentration. Modeling and data fitting of the algorithm have been done based on 61 data samples collected in the Pearl River estuary during a cruise from 3 to 11 May 2014. The study also attempts to modify the aerosol correction bands used in SeaDAS to prevent saturation of these bands. The modified algorithms showed an R-Square value of 0.7289 for Chl a fitting, and 0.7338 for CDOM fitting, and corrected overestimation of Chl a concentration in the Pearl River estuary.

Changes in area fraction of sediment-laden sea ice in the Arctic Ocean during 2000 to 2021

Sediment-laden sea ice plays an important role in Arctic sediment transport and biogeochemical cycles,as well as the shortwave radiation budget and melt onset of ice surface.However,at present,there is a lack of efficient observation approach from both space and in situ for the coverage of Arctic sediment-laden sea ice.Thus,both spatial distribution and long-term changes in area fraction of such ice floes are still unclear.This study proposes a new classification method to extract Arctic sediment-laden sea ice on the basic of the difference in spectral characteristics between sediment-laden sea ice and clean sea ice in the visible band using the MOD09A1 data with the resolution of 500 m,and obtains its area fraction over the pan Arctic Ocean during 2000−2021.Compared with Landsat-8 true color verification images with a resolution of 30 m,the overall accuracy of our classification method is 92.3%,and the Kappa coefficient is 0.84.The impact of clouds on the results of recognition and spatiotemporal changes of sediment-laden sea ice is relatively small from June to July,compared to that in May or August.Spatially,sediment-laden sea ice mostly appears over the marginal seas of the Arctic Ocean,especially the continental shelf of Chukchi Sea and the Siberian seas.Associated with the retreat of Arctic sea ice extent,the total area of sediment-laden sea ice in June-July also shows a significant decreasing trend of 8.99×10^(4) km^(2) per year.The occurrence of sediment-laden sea ice over the Arctic Ocean in June-July leads to the reduce of surface albedo over the ice-covered ocean by 14.1%.This study will help thoroughly understanding of the role of sediment-laden sea ice in the evolution of Arctic climate system and marine ecological environment,as well as the heat budget and mass balance of sea ice itself.

PCA-based sea-ice image fusion of optical data by HIS transform and SAR data by wavelet transform

Sea ice as a disaster has recently attracted a great deal of attention in China. Its monitoring has become a routine task for the maritime sector. Remote sensing, which depends mainly on SAR and optical sensors, has become the primary means for sea-ice research. Optical images contain abundant sea-ice multi-spectral in-formation, whereas SAR images contain rich sea-ice texture information. If the characteristic advantages of SAR and optical images could be combined for sea-ice study, the ability of sea-ice monitoring would be im-proved. In this study, in accordance with the characteristics of sea-ice SAR and optical images, the transfor-mation and fusion methods for these images were chosen. Also, a fusion method of optical and SAR images was proposed in order to improve sea-ice identification. Texture information can play an important role in sea-ice classification. Haar wavelet transformation was found to be suitable for the sea-ice SAR images, and the texture information of the sea-ice SAR image from Advanced Synthetic Aperture Radar （ASAR） loaded on ENVISAT was documented. The results of our studies showed that, the optical images in the hue-intensi-ty-saturation （HIS） space could reflect the spectral characteristics of the sea-ice types more efficiently than in the red-green-blue （RGB） space, and the optical image from the China-Brazil Earth Resources Satellite （CBERS-02B） was transferred from the RGB space to the HIS space. The principal component analysis （PCA） method could potentially contain the maximum information of the sea-ice images by fusing the HIS and texture images. The fusion image was obtained by a PCA method, which included the advantages of both the sea-ice SAR image and the optical image. To validate the fusion method, three methods were used to evaluate the fused image, i.e., objective, subjective, and comprehensive evaluations. It was concluded that the fusion method proposed could improve the ability of image interpretation and sea-ice identification.

The decrease of salinity in lakes on the Tibetan Plateau between 2000 and 2019 based on remote sensing model inversions

Salinity is an essential factor of lake water environments and aquatic systems.It is also sensitive to climatic changes and human activities based on concentration variations of solved minerals.However,there are few consecutively temporal studies on lake salinity variations on the Tibetan Plateau because the harsh environmental conditions make it diffcult to carry out in-situ observations for several lakes.In this study.we constructed a remote sensing retrieval model for lake salinity based on 87 in-situ lake investigations;moreover,interannual lake salinity and associated variations from 152 lakes larger than 50 km2 were analyzed on the Tibetan Plateau.A significant decreasing trend in lake salinity was observed between 2000 and 2019(p<0.01).The spatial variation of lake salinity was negatively correlated with lake area changes,and the optical characteristics of salt mineral solutions were generally positively correlated with mineral concentration based on the absorption coefficients of ionic solutions.The decreasing trend of lake salinity was not directly affected by the.precipitation,but was,potentially dominated by the expanding lake water volume.This study improves the understanding of regional water environmental changes and management efficacy of water resources.

Determining oil slick thickness using hyperspectral remote sensing in the Bohai Sea of China

Determining oil slick thickness plays an important role in assessing oil spill volume and its environmental impacts on the ocean.In this study,we used a Hyperion image of an oil spill accident area and seawater and fresh crude oil samples collected in the Bohai Sea of China.A well-controlled laboratory experiment was designed to simulate spectral responses to different oil slick thicknesses.Spectral resampling and normalization methods were used to reduce the differences in spectral reflectances between the experimental background seawater sample and real background seawater.Fitting the analysis with laboratory experimental data results showed a linear relationship between normalized oil slick reflectance and normalized oil slick thickness[20th band(R^(2)-0.92938,n=49,pB0.01),26th band(R^(2)=0.93806,n=49,pB0.01),29th band(R^(2)=0.93288,n=49,pB0.01)].By using these statistical models,we successfully determined the normalized oil slick thickness with the Hyperion image.Our results indicate that hyperspectral remote sensing technology is an effective method to monitor oil spills on water.The spectral ranges of visible green and red light were the optimal bands for estimating oil slick thickness in case 2 water.The high,stabilized spectral reflectance of background seawater will be helpful in oil slick thickness inversion.

Performance evaluation of operational atmospheric correction algorithms over the East China Seas

To acquire high-quality operational data products for Chinese in-orbit and scheduled ocean color sensors, the performances of two operational atmospheric correction(AC) algorithms(ESA MEGS 7.4.1 and NASA Sea DAS 6.1) were evaluated over the East China Seas(ECS) using MERIS data. The spectral remote sensing reflectance R_(rs)(λ), aerosol optical thickness(AOT), and ?ngstr?m exponent(α) retrieved using the two algorithms were validated using in situ measurements obtained between May 2002 and October 2009. Match-ups of R_(rs), AOT, and α between the in situ and MERIS data were obtained through strict exclusion criteria. Statistical analysis of R_(rs)(λ) showed a mean percentage difference(MPD) of 9%–13% in the 490–560 nm spectral range, and significant overestimation was observed at 413 nm(MPD>72%). The AOTs were overestimated(MPD>32%), and although the ESA algorithm outperformed the NASA algorithm in the blue-green bands, the situation was reversed in the red-near-infrared bands. The value of α was obviously underestimated by the ESA algorithm(MPD=41%) but not by the NASA algorithm(MPD=35%). To clarify why the NASA algorithm performed better in the retrieval of α, scatter plots of the α single scattering albedo(SSA) density were prepared. These α-SSA density scatter plots showed that the applicability of the aerosol models used by the NASA algorithm over the ECS is better than that used by the ESA algorithm, although neither aerosol model is suitable for the ECS region. The results of this study provide a reference to both data users and data agencies regarding the use of operational data products and the investigation into the improvement of current AC schemes over the ECS.

Unsupervised GRNN flood mapping approach combined with uncertainty analysis using bi-temporal Sentinel-2 MSI imageries

Floods occur frequently worldwide.The timely,accurate mapping of the flooded areas is an important task.Therefore,an unsupervised approach is proposed for automated flooded area mapping from bitemporal Sentinel-2 multispectral images in this paper.First,spatial–spectral features of the images before and after the flood are extracted to construct the change magnitude image(CMI).Then,the certain flood pixels and non-flood pixels are obtained by performing uncertainty analysis on the CMI,which are considered reliable classification samples.Next,Generalized Regression Neural Network(GRNN)is used as the core classifier to generate the initial flood map.Finally,an easy-toimplement two-stage post-processing is proposed to reduce the mapping error of the initial flood map,and generate the final flood map.Different from other methods based on machine learning,GRNN is used as the classifier,but the proposed approach is automated and unsupervised because it uses samples automatically generated in uncertainty analysis for model training.Results of comparative experiments in the three sub-regions of the Poyang Lake Basin demonstrate the effectiveness and superiority of the proposed approach.Moreover,its superiority in dealing with uncertain pixels is further proven by comparing the classification accuracy of different methods on uncertain pixels.

A new semi-empirical model for soil moisture content retrieval by ASAR and TM data in vegetation-covered areas

Active microwave and passive optical remote sensing data have demonstrated their respective advantages in inversion of surface soil moisture content. A new semi-empirical model is presented for soil moisture content retrieval in vegetation-covered areas, using ENVISAT-ASAR and LANDSAT-TM data collaboratively. Derivation of the algorithm is based on simplification of the Michigan Microwave Canopy Scattering Model （MIMICS）. In the model, the ground surface is divided into a canopy layer and a soil layer, and empirical relationships simulated among vegetation water mass We, the backscatter coefficient σpq1, the bidirectional scattering coefficient σpq2 and the extinction coefficient τp. The key input parameters of the semi-empirical model are reduced to only the leaf area index （LAI）, which can be easily inverted by the optical model PROSAIL, allowing coupling of the microwave and optical models to be achieved. Also, vegetation RMS height （Svcg） is introduced to correct for the radar-shadow effect caused by over-laying vegetation. Analysis of the parameter sensitivity of the semi-empirical model showed that when the regional Leaf Area Index is small （LAI≤3）, the model is more applicable. Soil moisture distribution in the study area was mapped using the semi-empirical model and field ground measurements used for model validation. This showed that, after correction of the radar-shadow effect, the average relative error （Er） between ground-measured and semi-empirical model-derived estimates of soil moisture decreased from 17.6% to 10.4%, while the RMS reduced from 0.055 to 0.031 g cm^-3. The accuracy of soil moisture estimates from the semi-empirical model is much better than for the MIMICS model （Er = 22.7%, RMS = 0.068 g cm^-3）, showing that the semi-empirical model is efficient at obtaining regional surface soil moisture contents when LAI is small.