High-resolution Earth Observation System Research Articles

Land Surface Temperature Retrieval From GF5-01A Based on Split- Window Algorithm

Abstract. Land Surface temperature (LST) is a core parameter in the energy exchange between the surface and the atmosphere, and the use of thermal infrared remote sensing can realize the wide-range, fast, and accurate acquisition of surface temperature. GF5-01A is an important part and the final satellite of the major special project on high-resolution Earth observation system which is equipped with a wide-area thermal infrared imager with a resolution of 100 meters and a width of 1,500 kilometers. In this paper, based on the GF5-01A WTI spectral response function, combined with the TIGR2000 atmospheric profiles data and the ASTER spectral library, the data simulation was carried out by using the atmospheric radiative transfer model MODTRAN 5.2, and then constructed the split-window algorithm. Then, the method proposed in this paper was validated and evaluated using Landsat 8/9 temperature products and measured surface temperature data from SURFRAD sites acquired on the same day. The results show that the RMSE between the GF5-01A retrieved LST and the Landsat8/9 retrieved LST is between 1.27–2.24K, and the Bias is between −2.08–1.12K. The RMSE is between 0.68–2.64K and the bias is between −0.68–1.49K compared to the measured surface temperature. The split-window algorithm of GF5-01A proposed in this paper can meet the requirements of thermal infrared remote sensing monitoring and has enormous potential and value.

Multisource remote sensing image fusion processing in plateau seismic region feature information extraction and application analysis – An example of the Menyuan Ms6.9 earthquake on January 8, 2022

Abstract A 6.9 magnitude earthquake hit Menyuan County, Haibei Prefecture, Qinghai Province, at 01:45 PM Beijing time on January 8, 2022 (17:45 PM GMT time on January 7, 2022). To explore the magnitude of the earthquake deformation and the affected area, this work combined optical remote sensing interpretation, interferometric synthetic aperture radar (InSAR) coseismic deformation extraction, and field surveys for research and analysis. Relying on the high-resolution Earth observation system of the Qinghai Remote Sensing Center for Natural Resources, high-resolution GF1D, GF2, and TRIPLESAT optical remote sensing images were acquired immediately after the earthquake. The airborne triangulation encryption method was used to carry out orthographic correction, fusion, and mosaic processing of digital orthophoto map (DOM) and digital surface model (DSM) images, and first-hand optical remote sensing images of the disaster areas were obtained. Based on differential InSAR (D-InSAR), small baseline subset InSAR (SBAS-InSAR) and lifting rail fusion methods, the coseismic deformation field and deformation rate of the lifting rail direction were obtained by using Sentinel-1A data processing before and after the earthquake. Combined with optical interpretation, InSAR deformation, and field investigation, the results show that the deformation trend of the line of sight (LOS) images to the north and south of the ascending and descending orbits show an obvious opposite trend. The surface shape variables are −50 to 45 cm and −65 to 72 cm, respectively, and the deformation rate before the earthquake reached 25 cm/year. The deformation field characteristics show that the earthquake was mainly due to thrust, and the coseismic deformation field fractured along the WNW‒ESE direction with a length of approximately 33 km. The areas affected by 10 mm, 20 cm, and 50 cm deformation magnitudes in the whole earthquake area were 975.14, 321.10, and 38.55 km2, respectively. Within 20 km, there were two main affected townships, namely, Sujitan Township and Huangcheng Mongolian Township. Within 50 km, there were four main affected towns and townships, namely, Sujitan Township, Mongolian Township of the Imperial city, Qingshizui town, and Haomen town.

Decoupling Semantic and Edge Representations for Building Footprint Extraction From Remote Sensing Images

Very high-resolution (VHR) earth observation systems provide an ideal data source for man-made structure detection such as building footprint extraction. Manually delineating building footprints from the remotely sensed VHR images, however, is laborious and time-intensive; thus, automation is needed in the building extraction process to increase productivity. Recently, many researchers have focused on developing building extraction algorithms based on the encoder-decoder architecture of convolutional neural networks. However, we observe that this widely adopted architecture cannot well preserve the precise boundaries and integrity of the extracted buildings. Moreover, features obtained by shallow convolutional layers contain irrelevant background noises that degrade building feature representations. This paper addresses these problems by presenting a feature decoupling network (FD-Net) that exploits two essential building information from the input image, including semantic information that concerns building integrity and edge information that improves building boundaries. The proposed FD-Net improves the existing encoder-decoder framework by decoupling image features into the edge subspace and the semantic subspace; the decoupled features are then integrated by a supervision-guided fusion process considering the heterogeneity between edge and semantic features. Furthermore, a lightweight and effective global context attention module is introduced to capture contextual building information and thus enhance feature representations. Comprehensive experimental results on three real-world datasets confirm the effectiveness of FD-Net in large-scale building mapping. We applied the proposed method to various encoder-decoder variants to verify the generalizability of the proposed framework. Experimental results show remarkable accuracy improvements with less computational cost.

Development of China’s spaceborne SAR satellite, processing strategy, and application: take Gaofen-3 series as an example

ABSTRACT After mastering the key technologies of manufacturing spaceborne Synthetic Aperture Radar (SAR), China’s SAR satellites have been successfully launched into space. As the only civil microwave satellite listed in the “National High-resolution Earth Observation System Major Project,” the Gaofen-3 (GF-3) 01 satellite is the first C-band multi-polarization SAR satellite with a resolution of 1 m. GF-3 series satellites stand out among civil SAR satellites worldwide because of their high resolution, wide swath, high geometric radiation quality, multiple imaging modes, and long operation time. Taking GF-3 series satellites as an example, this study introduces the development of China’s civil SAR satellites, as well as their processing strategies and applications. The success of the GF-3 series satellites shows that China’s SAR remote sensing technology has stepped into a new era of high-quality and high-precision Earth observations.

CHINA’S HIGH-RESOLUTION EARTH OBSERVATION SYSTEM (CHEOS): ADVANCES AND PERSPECTIVES

Abstract. From the end of the "Eleventh Five-Year Plan", China had implemented a major project for a high-resolution Earth observation system, and completed all satellite launch tasks by 2020. CHEOS aims to build an Earth observation system with high spatial, temporal and spectral resolution in an all-round way, and actively meet the major national strategic needs of China's national defense, natural resource survey, disaster response and prevention. CHEOS has promoted the continuous maturity of high-resolution Earth observation technology in China and the wide application of high-resolution images. This paper reviews the development history of CHEOS, analyzes the technical characteristics and application status of the GF (means high resolution in Chinese) series of civil satellites, discusses the current problems and future development trends of CHEOS, and provides a reference for the subsequent promotion and application of the GF series satellites.

Multi-Oriented Object Detection in High-Resolution Remote Sensing Imagery Based on Convolutional Neural Networks with Adaptive Object Orientation Features

In high-resolution earth observation systems, object detection in high spatial resolution remote sensing images (HSRIs) is the key technology for automatic extraction, analysis and understanding of image information. With respect to the multi-angle features of object orientation in HSRIs object detection, this paper presents a novel HSRIs object detection method based on convolutional neural networks (CNN) with adaptive object orientation features. First, an adaptive object orientation regression method is proposed to obtain object regions in any direction. In the adaptive object orientation regression method, five coordinate parameters are used to regress the object region with any direction. Then, a CNN framework for object detection of HSRIs is designed using the adaptive object orientation regression method. Using multiple object detection datasets, the proposed method is compared with some state-of-the-art object detection methods. The experimental results show that the proposed method can more accurately detect objects with large aspect ratios and densely distributed objects than some state-of-the-art object detection methods using a horizontal bounding box, and obtain better object detection results for HSRIs.

An Introduction to the Chinese High-Resolution Earth Observation System: Gaofen-1~7 Civilian Satellites

The Chinese High-resolution Earth Observation System (CHEOS) program has successfully launched 7 civilian satellites since 2010. These satellites are named by Gaofen (meaning high resolution in Chinese, hereafter noted as GF). To combine the advantages of high temporal and comparably high spatial resolution, diverse sensors are deployed to each satellite. GF-1 and GF-6 carry both high-resolution cameras (2 m resolution panchromatic and 8 m resolution multispectral camera), providing high spatial imaging for land use monitoring; GF-3 is equipped with a C-band multipolarization synthetic aperture radar with a spatial resolution of up to 1 meter, mostly monitoring marine targets; GF-5 carried 6 sensors including hyperspectral camera and directional polarization camera, dedicated to environmental remote sensing and climate research, such as aerosol, clouds, and greenhouse gas monitoring; and GF-7 laser altimeter system payload enables a three-dimensional surveying and mapping of natural resource and land surveying, facilitating the accumulation of basic geographic information. This study provides an overview of GF civilian series satellites, especially their missions, sensors, and applications.

Research on YOLOv3 target detection model in the field of remote sensing

The target recognition problem of high-resolution remote sensing images is marine traffic monitoring, disaster reduction emergency search and rescue, unmanned autonomous systems (UAS) (such as unmanned aerial vehicles, unmanned vehicles, unmanned submersibles, unmanned surface craft and other autonomous robots) and other civilian applications. The core technology of the system is also the key technology of military automatic target recognition (ATR) systems such as military reconnaissance, precision guidance, and maritime monitoring. With the development of high-resolution earth observation systems, more and more industrial applications require the extraction of more valuable target details from high-resolution remote sensing images. Then, due to the complex background of high-resolution remote sensing images and the difference in image quality (factors such as size, resolution, noise, etc.), low target detection accuracy and slow speed are the main bottlenecks that affect the combination of artificial intelligence and remote sensing. In order to solve the technical bottleneck of target detection, this paper focuses on exploring the detection performance of the channel attention module SElayer and YOLOv3 model in the field of high-resolution remote sensing images, providing basic technical support for related research.

Simulation and Assessment of the Capabilities of Orbita Hyperspectral (OHS) Imagery for Remotely Monitoring Chlorophyll-a in Eutrophic Plateau Lakes

The chlorophyll-a (Chl-a) concentration of eutrophic lakes fluctuates significantly due to the disturbance of wind and anthropogenic activities on the water body. Consequently, estimation of the Chl-a concentration has become an immense challenge. Due to urgent demand and rapid development in high-resolution earth observation systems, it has become crucial to assess hyperspectral satellite imagery capabilities on inland water monitoring. The Orbita hyperspectral (OHS) satellite is the latest hyperspectral sensor with both high spectral and spatial resolution (2.5 nm and 10 m, respectively), which could provide great potential for remotely estimating the concentration of Chl-a for inland waters. However, there are still some deficiencies that are mainly manifested in the Chl-a concentration remote sensing retrieval model assessment and accuracy validation, as well as signal-to-noise ratio (SNR) estimation of OHS imagery for inland waters. Therefore, the radiometric performance of OHS imagery for water quality monitoring is evaluated in this study by comparing different atmospheric correction models and the SNR with several remote sensing images. Several crucial findings can be drawn: (1) the three-band model ((1/B15-1/B17)B19) developed by OHS imagery is most suitable for estimating the Chl-a concentration in Dianchi Lake, with the root-mean-square error (RMSE) and the mean absolute percentage error (MAPE) of 15.55 µg/L and 16.31%, respectively; (2) the applicability of the FLAASH (Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes) atmospheric correction model for OHS imagery in a eutrophic plateau lake (Dianchi Lake) was better than the 6S (Second Simulation of Satellite Signal in the Solar Spectrum) model, and QUAC (Quick Atmospheric Correction) model, as well as the dark pixel method; (3) the SNR of the OHS imagery was similar to that of Hyperion imagery and was significantly higher than SNR of the HSI imagery; (4) the spatial resolution showed slight influence on the SNR of the OHS imagery. The results show that OHS imagery could be applied to remote sensing retrieval of Chl-a in eutrophic plateau lakes and presents a new tool for dynamic hyperspectral monitoring of water quality.

Extracting Information on Wetlands in Northeast China Based on Time Series FY3/MERSI Data

Using Fengyun Medium Resolution Spectral Imager (FY3/MERSI) data from 2011 to 2018, this paper analyzed the characteristics of original spectra and the normalized difference vegetation index (NDVI) of lakes, reservoirs, rivers, paddy fields and swamps in Northeast China, A large-scale wetland extraction model was built using the decision tree classification method, and accuracy was verified in typical sites using China High-resolution Earth Observation System (CHEOS-1) data. The results showed that there were differences in the spectral curves and NDVI of different wetland types, especially the swamps, paddy fields and other landforms that showed a growth cycle with more significant differences in spectral curves and index curves than those of lakes, reservoirs and rivers. The total area of wetlands in Northeast China was 79,123.4 km2, including 5262.3 k m2 of lake and reservoir wetlands, 6514.7 km2 of river wetlands, 15284.7 k m2 of swamp wetlands and 52061.7 km2 of paddy fields. The accuracy verification showed that the area accuracy of wetlands was over 91.8%, the overall classification accuracy of wetlands was over 84.8% and the Kappa coefficient was over 0.6446.

Research Progress on Models, Algorithms, and Systems for Remote Sensing Spatial-Temporal Big Data Processing

With the rapid development of high-resolution earth observation systems, the data processing, algorithm design, and system development of remote sensing spatial-temporal big data (RS-STBD) have gradually become the bottleneck problems in the application and development of earth observation system. The research on the model, algorithm, and system of RS-STBD processing involves complex scientific problems, technical bottlenecks, and inconstant requirements of engineering applications. This article summarizes the data type and processing theory model of RS-STBD, the high-performance algorithm design based on cloud service and intelligent computing, and the architecture design and engineering development methods of the complex remote sensing application system. Furthermore, the existing problems in the current research are analyzed, and the related solutions are given. Finally, the future development trend of scientific exploration, technical research, and application development of RS-STBD has prospected.

Research on Generic Optical Remote Sensing Products: A Review of Scientific Exploration, Technology Research, and Engineering Application

With the initial establishment of global earth observation system in various countries, more and more high-resolution remote sensing data of multisource, multitemporal, multiscale, and different types of satellites are obtained. It is urgent to explore the advanced basic theory of remote sensing information science, design high-performance generic key technologies of remote sensing information system and global positioning system, and study complex engineering system of remote sensing applications and geographic information system. In this article, the basic theory exploration, inversion technology research, and engineering application design and development of generic optical remote sensing product (ORSP) are systematically reviewed. We classify the ORSP scientifically, review the main algorithms and application scope of 16 kinds of generic ORSP, and expound the validation and quality evaluation methods of ORSP in engineering application. Furthermore, we analyze the current core problems and solutions, and prospects for the state-of-the-art research and the future development trend of generic ORSP. This will provide valuable reference for scientific research and construction of high-resolution earth observation system.

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

ABSTRACT 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.

A Novel NIR–Red Spectral Domain Evapotranspiration Model From the Chinese GF-1 Satellite: Application to the Huailai Agricultural Region of China

The Chinese GF-1 satellite, the first satellite of the China High-resolution Earth Observation System launched in 2013, can be used to help estimate evapotranspiration (LE), which is important for myriad hydroclimatic and ecosystem science and applications. We propose a novel approach to use the GF-1 visible and near-infrared (VNIR) measurements at 16 m and 4-day resolutions to estimate LE. The NIR (near-infrared)–red spectral-domain (NRSD) model is coupled to a perpendicular soil moisture index (PSI) and a perpendicular vegetation index (PVI). We applied the model to the Huailai agricultural region of China with 55 scenes of GF-1 imagery during 2013–2017 and validated using ground measurements with footprint models for two eddy-covariance (EC) flux tower sites and one large aperture scintillometer (LAS) site. The results illustrate that the terrestrial daily LE can be estimated with squared correlation coefficients ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$R^{2}$ </tex-math></inline-formula> ) of 0.77–0.84 ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$p &lt; 0.01$ </tex-math></inline-formula> ) and root-mean-square error (RMSE) values of 17.9–21.5 W/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> among all three sites. The site-calibrated statistics are improved by 0.14–0.25 for <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$R^{2}$ </tex-math></inline-formula> and decreased by 4.2–8.3 W/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> for RMSE as compared to the commonly used universal PT-JPL model. A satisfactory performance is achieved across all experimental conditions, encouraging the application of the NRSD model to estimate LE for other broad regions.

Vicarious Calibration for the AHSI Instrument of Gaofen-5 With Reference to the CRCS Dunhuang Test Site

The visible-shortwave infrared Advanced Hyperspectral Imager (AHSI) is a payload onboard the Gaofen-5 satellite, which is China’s first hyperspectral satellite and is part of the Chinese High-Resolution Earth Observation System. As a supplement to the onboard radiometric calibration of the AHSI instrument, vicarious calibration is also required, which is independent of the instrument-based calibration. In this article, a reflectance-based vicarious calibration approach is presented, which takes surface reflectance data, aerosol data, and atmospheric water vapor data into account. The Dunhuang test site, which is one of the China Radiometric Calibration Sites (CRCS) for the vicarious calibration of spaceborne sensors, possesses stable, uniform, and measurable surface objects, so it was chosen as the radiation source to replace the laboratory and onboard calibrators. A Spectra Vista Corporation (SVC) spectral radiometer and a CE318 sun photometer were utilized for the measurement of the surface reflectance and the condition of the aerosol, respectively. The radiance at the entrance pupil at the top of atmosphere was then obtained through the MODerate resolution atmospheric TRANsmission (MODTRAN) atmospheric transmission model. The surface reflectance was obtained using the Fast Line-of-sight Atmospheric Analysis of Hypercubes (FLAASH) atmospheric model for validation. The results show that, with regard to the calibration coefficients, the calibrated AHSI instrument presents a stable radiometric performance among different land-cover types. The ratios on all the bands are between 0.8 and 1.2 and are consistent with the reflectance data from the Dunhuang test site. The <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${R} ^{{2}}$ </tex-math></inline-formula> values are all greater than 0.95 and the spectral angle is all less than 2°. The standard deviations of the ratios are less than 3% for each chosen band, which proves that the calibrated data have a high consistency with the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in situ</i> measurements. When compared with Landsat 8 and Sentinel-2, the mean errors of the surface reflectance are all under 0.06, which further demonstrates that the calibrated reflectance has a high accuracy.

Evaluating the feasibility of GF-1 remote sensing comparison with hyperspectral data for soil organic carbon prediction and mapping

High-resolution remote sensing data play a very important role in agriculture. However, the major sources of high-resolution images are not owned by China. The Chinese “High Resolution Earth Observation Systems” was deployed in 2010, and several major projects have been implemented. The present study focused on assessing the feasibility of Gaofen (GF) multi-spectral data for monitoring bare soil organic carbon (SOC) at field and regional scales. The data sources are hyperspectra measured under laboratory conditions and simulated multi-spectral data from GF-1 remote sensing images. Partial least squares regression (PLSR) was used to estimate SOC. At the field scale, the SOC hyperspectral prediction model produced better R2=0.9688, RMSE=0.3818, and RPD=5.6393 than the simulated multi-spectral SOC prediction model (R2=0.8179, RMSE=0.9913, RPD=2.3401). At a regional scale, the SOC hyperspectral prediction model also produced a better R2=0.9319, RMSE=1.097, and RPD=3.8758 than the simulated multi-spectral SOC predicted model (R2=0.8445, RMSE=1.6574, RPD=2.4228). For the simulated GF-1 multi-spectra model, regional scale predications had advantages over field scale predictions. The spatial distribution characteristics of SOC measurements and predictions from hyperspectral data and simulated GF-1 multi-spectral data were similar. Thus, satisfactory performance of the predictive and calibrated models validates the feasibility of these methods for rapid large-scale SOC monitoring.

A New Quick View Retrieval Method for GF-6 Remote Sensing Images

As of November 3, 2019, all seven satellites for project of the high-resolution earth observation system have been launched. We have ushered in an era of massive remote sensing data. When we do large-scale forest land mapping, such as national and provincial scale, we need to retrieve images that meet the image quality requirements, are as cloudless as possible, cover the research area, and have as few images as possible. The conventional practice is that the application unit searches the web page provided by the remote sensing data provider for the remote sensing data it needs, and orders to obtain the data. It is often time-consuming and laborious to query data that meet the needs. This paper puts forward a new selection process method of GF-6, GF-1 and GF-2 satellite data. This is a step-by-step method. First, the cloud coverage, time and area are selected according to the metadatas provided by the data provider to meet the requirements. Then, according to the image quality and cloud amount set by the program, it is selected again. Finally, in this image set, the final selected image is given according to the requirements of maximum coverage of the research area and minimum overlap of the image itself. The method can greatly save the time for manually selecting images and improve the working efficiency. This paper designs a program, which can be used conveniently by users. The paper introduces the idea of program design in detail.

Discriminative Feature Learning Constrained Unsupervised Network for Cloud Detection in Remote Sensing Imagery

Cloud detection is a significant preprocessing step for increasing the exploitability of remote sensing imagery that faces various levels of difficulty due to the complexity of underlying surfaces, insufficient training data, and redundant information in high-dimensional data. To solve these problems, we propose an unsupervised network for cloud detection (UNCD) on multispectral (MS) and hyperspectral (HS) remote sensing images. The UNCD method enforces discriminative feature learning to obtain the residual error between the original input and the background in deep latent space, which is based on the observation that clouds are sparse and modeled as sparse outliers in remote sensing imagery. The UNCD enforces discriminative feature learning to obtain the residual error between the original input and the background in deep latent space, which is based on the observation that clouds are sparse and modeled as sparse outliers in remote sensing imagery. First, a compact representation of the original imagery is obtained by a latent adversarial learning constrained encoder. Meanwhile, the majority class with sufficient samples (i.e., background pixels) is more accurately reconstructed than the clouds with limited samples by the decoder. An image discriminator is used to prevent the generalization of out-of-class features caused by latent adversarial learning. To further highlight the background information in the deep latent space, a multivariate Gaussian distribution is introduced. In particular, the residual error with clouds highlighted and background samples suppressed is applied in the cloud detection in deep latent space. To evaluate the performance of the proposed UNCD method, experiments were conducted on both MS and HS datasets that were captured by various sensors over various scenes, and the results demonstrate its state-of-the-art performance. The sensors that captured the datasets include Landsat 8, GaoFen-1 (GF-1), and GaoFen-5 (GF-5). Landsat 8 was launched at Vandenberg Air Force Base in California on 11 February 2013, in a mission that was initially known as the Landsat Data Continuity Mission (LDCM). China launched the GF-1 satellite. The GF-5 satellite captures hyperspectral observations in the Chinese Key Projects of High-Resolution Earth Observation System. The overall accuracy (OA) values for Images I and II from the Landsat 8 dataset were 0.9526 and 0.9536, respectively, and the OA values for Images III and IV from the GF-1 wide field of view (WFV) dataset were 0.9957 and 0.9934, respectively. Hence, the proposed method outperformed the other considered methods.

The threshold determination methods of water body information extraction using GF-1 satellite image

The GF-1 satellite is the first satellite of China High-resolution Earth Observation System (referred to short as CHEOS) with the advantages of high spatial resolution, high temporal resolution and 4 multi-spectral channels, which can quickly extract inland water body information. At present, the methods of water body information extraction by remote sensing mainly include single-band threshold method, spectral relation method, normalized difference vegetation index (NDVI) method, normalized difference water body index (NDWI) method and shade water body index (SWI) method. The results of water body information extraction will vary with different methods; especially, the thresholds determination of each method is an important factor in the accuracy of water body information extraction. In this paper, the water body information of Qinghai Lake, Hongze Lake, and Hulun Lake were extracted by NDWI index, and the optimal thresholds of water body information extraction were determined by using Jeffries-Matusita (J-M) distance method and density slice method was in the range of -0.061~0.093. Finally, the accuracy of the extraction results was analyzed, and the overall accuracy is more than 88%.

Aberration effects in orbital imaging

ABSTRACTAberrations exist in any moving system that transmits or receives electromagnetic waves. In orbital imaging, these aberrations were ignored before the development of the high-resolution Earth observation system and the high-orbit Earth observation system. In this paper, a two-step model is presented to evaluate the difference between the hypothetical ‘stop-go’ geometry and the real geometry, which is continuously changing. This model is robust in dealing with both Earth ellipsoid and different Earth orbits. Simplified equations are also given for circular orbit and nadir looking. Two consequences of these aberrations, including pointing deviations and image distortions, are analysed. The simulated results indicate that the pointing deviation can exceed 1% of the footprint of the field of view, while the image distortion at a tilted attitude can achieve the same order of magnitude as the image resolution.