This PDF file contains the front matter associated with SPIE Proceedings Volume 9669 including the Title Page, Copyright information, Table of Contents, Introduction, and Conference Committee listing.

Truss structure is usually adopted as the main structure form for spacecrafts due to its high efficiency in supporting concentrated loads. Light-weight design is now becoming the primary concern during conceptual design of spacecrafts. Implementation of light-weight design on truss structure always goes through three processes: topology optimization, size optimization and composites optimization. During each optimization process, appropriate algorithm such as the traditional optimality criterion method, mathematical programming method and the intelligent algorithms which simulate the growth and evolution processes in nature will be selected. According to the practical processes and algorithms, combined with engineering practice and commercial software, summary is made for the implementation of light-weight design on truss structure for spacecrafts.

An integrated compact airborne multispectral imaging system using embedded computer based control system was developed for small aircraft multispectral imaging application. The multispectral imaging system integrates CMOS camera, filter wheel with eight filters, two-axis stabilized platform, miniature POS (position and orientation system) and embedded computer. The embedded computer has excellent universality and expansibility, and has advantages in volume and weight for airborne platform, so it can meet the requirements of control system of the integrated airborne multispectral imaging system. The embedded computer controls the camera parameters setting, filter wheel and stabilized platform working, image and POS data acquisition, and stores the image and data. The airborne multispectral imaging system can connect peripheral device use the ports of the embedded computer, so the system operation and the stored image data management are easy. This airborne multispectral imaging system has advantages of small volume, multi-function, and good expansibility. The imaging experiment results show that this system has potential for multispectral remote sensing in applications such as resource investigation and environmental monitoring.

Global Navigation Satellite System can not only positioning but also emit microwaves of L band to the earth surface. With the reflection signals, we can obtain information about the sea surface. The Delay Doppler Map calculated from the received power recently play a key role in building relationships with the sea surface parameters. In this paper, the basics of Delay-Doppler Map are introduced and conducted by employing a set of new released TechDemoSat-1 GNSS-R data. In addition, sea surface parameters retrieved by using Delay-Doppler Maps are presented as a prospect.

This paper establishes a geometric model of multi-band mosaic imaging from the same orbit by agile satellites, and introduces a self-write simulation software. Geometric parameters of each band are calculated based on the attitude control ability of the satellite and the mission requirements. Considering the different ground resolution and the imaging angle of each band, two new concepts, Gradient Entropy and Structure Similarity Parameter are presented. These two values are used to evaluate the change of image quality caused by agility, and help to estimate the effect of the mission. By building the geometric model and calculating the agile information with the program, we propose a new approach of forward analysis of agile imaging, which helps users evaluate the image degradation.

Owing to relatively simplistic domestic hardware technology and a lack of on-orbit geometric calibration, particularly interior calibration, the positioning accuracies of Optical-1 HR satellites can vary greatly depending on the presence of ground control points (GCPs). Without GCPs, accuracies are typically lower than 100 pixels, whereas when GCPs are plentiful, accuracy is higher than one pixel, demonstrating the potential for a large discrepancy between international optical satellites with the same image resolution. This study investigated a new method of geometric calibration for Optical-1 HR satellites. Experiments were conducted to demonstrate the positive effects on positioning accuracy achieved by the calibration method. After calibration by our method, a positioning accuracy of higher than one pixel was obtained with only a small number of GCPs, which is equivalent to the accuracy of advanced international optical satellites with the same image resolution.

Soil moisture can be estimated from point measurements, hydrologic models, and remote sensing. Many researches indicated that the most promising approach for soil moisture is the integration of remote sensing surface soil moisture data and computational modeling. Although many researches were conducted using passive microwave remote sensing data in soil moisture assimilation with coarse spatial resolution, few researches were carried out using active microwave remote sensing observation.

This research developed and tested an operational approach of assimilation for soil moisture prediction using active microwave remote sensing data ASAR (Advanced Synthetic Aperture Radar) in Heihe Watershed. The assimilation was based on ensemble Kalman filter (EnKF), a forward radiative transfer model and the Distributed Hydrology Soil Vegetation Model (DHSVM). The forward radiative transfer model, as a semi-empirical backscattering model, was used to eliminate the effect of surface roughness and vegetation cover on the backscatter coefficient. The impact of topography on soil water movement and the vertical and lateral exchange of soil water were considered. We conducted experiments to assimilate active microwave remote sensing data (ASAR) observation into a hydrologic model at two field sites, which had different underlying conditions. The soil moisture ground-truth data were collected through the field Time Domain Reflectometry (TDR) tools, and were used to assess the assimilation method. The temporal evolution of soil moisture measured at point-based monitoring locations were compared with EnKF based model predictions. The results indicated that the estimate of soil moisture was improved through assimilation with ASAR observation and the soil moisture based on data assimilation can be monitored in moderate spatial resolution.

UAV remote sensing platform can obtain images of target regions quickly. It has distinct advantages in the detection of oil spill, the emergency response of searching and rescuing, the survey of coastal regions, etc. However, the existing UAV images are difficult to meet the needs of rapid processing, because the amount of their data is too large and the traditional processing methods take too much time in the matching stage. This paper designs a speed-up matching algorithm which utilizes navigation data in UAV to get the elements of exterior orientation. The algorithm is based on the collinearity equation with the flat terrain in the coastal regions. Those elements can be used to compute the original homography matrix and overlapping regions. After extracting interest points by SURF algorithm, the matching method only chooses some points in overlapping regions for matching stage. The algorithm can improve the matching speed , and also can decrease mismatching to improve the accuracy.

The bidirectional oversampling imaging detection system solved the difficulties of infrared point target detection under the traditional single sampling system, effectively improved the image target detection SNR and reduced the false alarm, and improved the target detection performance. The mathematical modeling and system simulation of both traditional line scanning method and bidirectional oversampling scanning system proved that under the same conditions, the detection rate of bidirectional oversampling system is higher than conventional sampling, while the false alarm rate of bidirectional oversampling system is less than conventional sampling.

Wavelet transform is a kind of effective image-scale transformation method, which can achieve multi-scale transformation by distinguishing the low-frequency information and the high-frequency information. Hyperspectral remote sensing data combining image with spectrum has almost continuous spectrum that is the important premise of extracting hyperspectral image information, while scale transformation will inevitably lead to the change of image and spectra. Therefore, it is important to study the image and spectral fidelity after wavelet transform. In this paper, the Proba CHRIS hyperspectral remote sensing image of Yellow River Estuary Wetland is used to investigate the image and spectral fidelity of image transformed by wavelet which remained the low-frequency information. The level 1-3 of up-scale images are obtained and then compared with the original. Then image and spectral fidelity is quantitatively analyzed. The results show that the image fidelity is slightly reduced by up-scale transformation, but near-infrared images have a larger distortion than other bands. With the increasing scaling up, the distortion of spectrum is more and more great, but spectral fidelity is overall well. For the typical wetland objects, Phragmites austrialis has the best spectral correlation, Spartina has a small spectra change, and aquaculture water spectral distortion is most remarkable.

Hyper Spectral Imager (HSI) is the first Chinese space-borne hyperspectral sensor aboard the HJ-1A satellite. We have developed a data preprocess flow for HSI images, which includes destriping, atmospheric correction and spectral filtering. In this paper, the product level of HSI image was introduced in the beginning, and a destriping method for HSI level 2 images was proposed. Then an atmospheric correction method based on radiative transfer mechanism was summarized to retrieve ground reflectance from HSI image. Furthermore, a new spectral filter method for ground reflectance spectra after atmospheric correction was proposed based on reference ground spectral database. Lastly, a HSI image acquired over Lake Dali in Inner Mongolia was used to evaluate the effect of the preprocess method. The HSI image after destriping was compared with the original HSI image, which shows that the stripe noise has been removed effectively. Both un-smoothed reflectance spectra and smoothed spectra using the preprocess method proposed in this paper are compared with the reflectance spectral derived with the well-known FLAASH method. The results show that the spectra become much smoother after the application of the spectral filtered algorithm. It was also found that the spectra using this new preprocessing method have similar results as that of the FLAASH method.

In recent years, the landslide disasters occurred frequently because of the seismic activity. It brings great harm to people's life. It has caused high attention of the state and the extensive concern of society. In the field of geological disaster, landslide information extraction based on remote sensing has been controversial, but high resolution remote sensing image can improve the accuracy of information extraction effectively with its rich texture and geometry information. Therefore, it is feasible to extract the information of earthquake- triggered landslides with serious surface damage and large scale. Taking the Wenchuan county as the study area, this paper uses multi-scale segmentation method to extract the landslide image object through domestic GF-1 images and DEM data, which uses the estimation of scale parameter tool to determine the optimal segmentation scale; After analyzing the characteristics of landslide high-resolution image comprehensively and selecting spectrum feature, texture feature, geometric features and landform characteristics of the image, we can establish the extracting rules to extract landslide disaster information. The extraction results show that there are 20 landslide whose total area is 521279.31 ㎡.Compared with visual interpretation results, the extraction accuracy is 72.22%. This study indicates its efficient and feasible to extract earthquake landslide disaster information based on high resolution remote sensing and it provides important technical support for post-disaster emergency investigation and disaster assessment.

How the different transformation models take effects on the registration accuracy based-on implicit similarity between the remote sensing images is the key point of this paper. For registration between SAR and optical imagery, analyze the imaging characteristic of push-broom optical satellite image and SAR image according to their imaging models; study the impacts taken by terrain fluctuation and different transformation models. The DEM and image pairs are simulated in the experiment, the results show: in region of bigger relief, the larger the registration image size, the greater impacts are taken by different transformation models on registration accuracy. Considering the polynomial transformation model leads to the low searching efficiency, affine transformation model regards as the best model for registration, but it has low accuracy and just applies to small images(such as 200x200). For large image (such as 800x800), 8-parameters transformation model is the best choice (balance accuracy and efficiency), but adding the parameters of transformation model (such as 12-parameters) again cannot significantly improve the registration accuracy.

Satellite observations and model simulations are of two important data sources to study atmospheric carbon dioxide concentration. For analyzing and evaluating the bias correction method of ACOS dry-air column averaged CO₂ (Xco₂) product, the GEOS-Chem Xco₂ simulations are selected according to observing time and locations of the ACOS product. The GEOS-Chem simulations of CO₂ profiles are transformed to Xco₂ data by convolving with satellite averaging kernels and pressure weighting functions. The GEOS-Chem Xco₂ data are then compared with both bias uncorrected and bias corrected satellite retrievals of ACOS. The comparisons show that the bias uncorrected ACOS retrievals are on average 1.12ppm higher than the model Xco₂ data, while the corrected ACOS retrievals are only on average 0.06ppm lower than the model Xco₂ data. By assuming consistency between model Xco₂ simulations and true atmospheric Xco₂, this study indicates that the bias can be obvious decreased through the bias correction method, and the correction is effective and necessary for satellite Xco₂ retrievals.

To solve the geometric distortion problem of large FOV linear array whiskbroom image, a model of multi center central projection collinearity equation was founded considering its whiskbroom and linear CCD imaging feature, and the principle of distortion was analyzed. Based on the rectification method with POS, we introduced the angular position sensor data of the servo system, and restored the geometric imaging process exactly. An indirect rectification scheme aiming at linear array imaging with best scanline searching method was adopted, matrixes for calculating the exterior orientation elements was redesigned. We improved two iterative algorithms for this device, and did comparison and analysis. The rectification for the images of airborne imaging experiment showed ideal effect.

The airborne LiDAR (Light Detection And Ranging) technology is a new type of aerial earth observation method which can be used to produce high-precision DEM (Digital Elevation Model) quickly and reflect ground surface information directly. Fault structure is one of the key forms of crustal movement, and its quantitative description is the key to the research of crustal movement. The airborne LiDAR point-cloud data is used to detect and extract fault structures automatically based on linear extension, elevation mutation and slope abnormal characteristics. Firstly, the LiDAR point-cloud data is processed to filter out buildings, vegetation and other non-surface information with the TIN (Triangulated Irregular Network) filtering method and Burman model calibration method. TIN and DEM are made from the processed data sequentially. Secondly, linear fault structures are extracted based on dual-threshold method. Finally, high-precision DOM (Digital Orthophoto Map) and other geological knowledge are used to check the accuracy of fault structure extraction. An experiment is carried out in Beiya Village of Yunnan Province, China. With LiDAR technology, results reveal that: the airborne LiDAR point-cloud data can be utilized to extract linear fault structures accurately and automatically, measure information such as height, width and slope of fault structures with high precision, and detect faults in areas with vegetation coverage effectively.

Statistical methods to map water depth from medium-high resolution multispectral images were easier and more popular than wave spectrum bathymetry or water scattering-based implementation. However, less studies compared the effectiveness of the popular statistical methods for pelagic islands. This study used the Log ratio transform, primary component analysis and independent component analysis methods to retrieve water depth of Pratas Island,using one Landsat 8 Operational Land Imager (OLI) image. Results showed that the Log ratio transformation was not the best method as the proposer suggested. The first primary component and the second independent component are good predictors for absolute water depth ranging from 0 to 20m, while Log Ratio was more sensitive to water depth ranging from 0 to 5m, IC2 was sensitive to water depth between 5 and 10 m.

GEO task-oriented optical remote sensing satellite, is very suitable for long-term continuous monitoring and quick access to imaging. With the development of high resolution optical payload technology and satellite attitude control technology, GEO optical remote sensing satellites will become an important developing trend for aerospace remote sensing satellite in the near future. In the paper, we focused on GEO optical remote sensing satellite plane array stare imaging characteristics and real-time leading mission of earth observation mode, targeted on satisfying needs of the user with the minimum cost of maneuver, and put forward the optimal path planning algorithm centered on transformation from geographic coordinate space to Field of plane, and finally reduced the burden of the control system. In this algorithm, bounded irregular closed area on the ground would be transformed based on coordinate transformation relations in to the reference plane for field of the satellite payload, and then using the branch and bound method to search for feasible solutions, cutting off the non-feasible solution in the solution space based on pruning strategy; and finally trimming some suboptimal feasible solutions based on the optimization index until a feasible solution for the global optimum. Simulation and visualization presentation software testing results verified the feasibility and effectiveness of the strategy.

Remote sensing is one subject of the modern geomatics, with a high priority for practical applications in which cross time and space analysis is one of its significant features. Object recognition and/or parameter retrieval are normally the first step in remote sensing applications, whereas cross time and space change analysis of those surface objects and/or parameters will make remote sensing applications more valuable. Based on a short review on the historic evolution of remote sensing and its current classification system, the cross time and space features commonly existing in remote sensing applications were discussed. The paper, aiming at improving remote sensing applications and promoting development of the remote sensing subject from a new vision, proposed a methodology based subject classification approach for remote sensing and then suggest to establish the theory of cross time and space remote sensing applications. The authors believe that such a new cross time and space concept meets the demand for new theories and new ideas from remote sensing subject and is of practical help to future remote sensing applications.

Regional river basins, transboundary rivers in particular, are shared water resources among multiple users. The tempo-spatial distribution and utilization potentials of water resources in these river basins have a great influence on the economic layout and the social development of all the interested parties in these basins. However, due to the characteristics of cross borders and multi-users in these regions, especially across border regions, basic data is relatively scarce and inconsistent, which bring difficulties in basin water resources management. Facing the basic data requirements in regional river management, the overall technical framework for remote sensing monitoring and data service system in China’s regional river basins was designed in the paper, with a remote sensing driven distributed basin hydrologic model developed and integrated within the frame. This prototype system is able to extract most of the model required land surface data by multi-sources and multi-temporal remote sensing images, to run a distributed basin hydrological simulation model, to carry out various scenario analysis, and to provide data services to decision makers.

Conventional D-InSAR (Differential SAR Interferometry) can only monitor 1-D surface deformation along LOS (line of sight) direction. In order to overcome this limitation and extract 3-D coseismic displacement, we combine the LOS displacement derived from D-InSAR technology, the OKADA elastic half space dislocation model theory, jointly the surface rupture distribution by field investigations and the fault model inverted by GPS, level data and gravity survey to retrieve the directions of surface co-seismic displacement, and then have got Wenchuan Ms8.0 Earthquake 3D displacement. Firstly, thirty six L-band PALSAR images of six adjacent ascending tracks are processed with D-InSAR technology to obtain the coseismic displacements along LOS direction. According to the OKADA model and the thrust fault movement model of the Long-Men-Shan Fault , we specify the three directions of surface coseismic displacements. And thus the 3D coseismic displacement field is then recovered by using LOS displacement and relevant geometric projection formulas, obviously including horizontal displacements field and vertical deformation contour maps. By comparing with the 3D displacement estimated from OKADA dislocation model and fault model, the displacement retrieved in this study can give more detail, and reflect seismic characteristics more truly.

Satellite remote sensing with a larger spatial coverage and high temporal resolution makes it possible to monitor precipitation distribution under extreme rainfall events. In this paper, the heavy rainstorm that occurred in Beijing on 21, July in 2012 was monitored using the TRMM and Fengyun precipitation data. Results indicate that: (1) these two kinds of satellite precipitation data are in good agreement with ground observed precipitation data, having a correlation coefficient of 0.9390 and 0.9846 and an underestimation of 14.42% and 19.86% respectively; (2) The moving track of this extreme rainstorm can be well detected, with the storm center and a heavy rain belt moving from southwest to northeast found; (3) 15 minutes interval between the two satellite data makes them complement each other, which enables the temporal frequency of the monitoring data further increased so as to get construction of the rainstorm processes improved.

The Tropical Rainfall Measuring Mission (TRMM) satellite rainfall data were assessed and calibrated using limited ground meteorological and hydrological data in Irrawaddy River basin, a watershed with complex terrain conditions but lack of data. A correction factor was determined to adjust TRMM data, taking basin water balance and terrain slopes into consideration. A distributed hydrological model SWAT was established and used to simulate the basin rainfall-runoff processes from 2001 to 2011, driven by the calibrated TRMM rainfall data series. Results show that, in a data scarce basin like Irrawaddy River basin, such a water balanced based TRMM data calibration method is suitable and reliable.

The purpose of using unmanned aerial vehicle (UAV) remote sensing application in Five-hundred-meter aperture spherical telescope (FAST) project is to dynamically record the construction process with high resolution image, monitor the environmental impact, and provide services for local environmental protection and the reserve immigrants. This paper introduces the use of UAV remote sensing system and the course design and implementation for the FAST site. Through the analysis of the time series data, we found that: (1) since the year 2012, the project has been widely carried out; (2) till 2013, the internal project begun to take shape;(3) engineering excavation scope was kept stable in 2014, and the initial scale of the FAST engineering construction has emerged as in the meantime, the vegetation recovery went well on the bare soil area; (4) in 2015, none environmental problems caused by engineering construction and other engineering geological disaster were found in the work area through the image interpretation of UAV images. This paper also suggested that the UAV technology need some improvements to fulfill the requirements of surveying and mapping specification., including a new data acquisition and processing measures assigned with the background of highly diverse elevation, usage of telephoto camera, hierarchical photography with different flying height, and adjustment with terrain using the joint empty three settlement method.

As part of the "High-Resolution Earth Observation System," many major projects are being implemented. The first optical satellite (GF-1) in the high-resolution satellite series has completed in-orbit tests and entered the stage of data acquisition. GF-1 owns high resolution and information of wide field view sensor (WFV sensor) and the panchromatic and multispectral sensor (PMS sensor). In this study, GF-1 WFV sensor data with a resolution of 16 m, integrated with Landsat-8 and RapidEye data were selected to recognize maize in Xuchang using Support Vector Machine (SVM) and Spectral Angle Mapper (SAM) method. The results showed that the precision of classification varies greatly among WFV sensors. In particular, WFV3 was of the highest accuracy to identify crops and planting area with accuracy higher than Landsat-8 and close to RapidEye. With regard to WFV1 and WFV4, the application effect was worse and less viable to identify species of complex autumn crops. In brief, the classification accuracy of SVM classifier is better than SAM classifier. It can be also concluded that SVM is more suitable for the identification of crops and planting area of extraction in the study area.

The high spatial heterogeneity forms a major uncertainty in accurately monitoring of vegetation coverage. In this study, an optimal zoning approach with dividing the whole heterogeneous image into relatively homogeneously segments was proposed to reduce the effects of high heterogeneity on vegetation coverage estimation. With the combination of the spectral similarity of the adjacent pixels and spatial autocorrelation of the segments, the optimal zoning approach accounted for the intrasegment uniformity and intersegment disparity of improved image segmentation. In comparison, vegetation coverage in the highly heterogeneous karst environments tended to be underestimated by the normalized difference vegetation index (NDVI) and overestimated by the normalized difference vegetation index-spectral mixture analysis (NDVI-SMA) model. Hence, when applying remote sensing for highly heterogeneous environments, the influence of high heterogeneity should not be ignored. Our study indicates that the proposed model, using NDVI-SMA model with improved segmentation, is found to ameliorate the effects of the highly heterogeneous environments on the extraction of vegetation coverage from hyperspectral imagery. The proposed approach is useful for obtaining accurate estimations of vegetation coverage in not only karst environments but also other environments with high heterogeneity.

Based on the development of classification algorithm applied in monitoring spatio-temporal dynamic changes of coal-- mining areas, several improvements were made on feature space and classification model in this paper. There were two innovations in our study: 1) During building the feature spaces, a new index for extracting information about mining area was created, which can classify mining area and settlements efficiently; 2) a special ticket-voting SVM algorithm with wavelet kernel function was proposed, which provides higher classification accuracy than other traditional classifiers via the secondary classification. Here we took the northeast plain of Pei county in Xuzhou city as a studying region, applying the proposed method to implement the classification by using the image of multi-temporal TM/ETM from the year of 1987 to 2013. How to carry on deep analysis combined with various non-spatial data is much more significant. Then we studied the rules of dynamic changes of land use/cover and further analyzed their driving factors by combining RS interpretation with GIS spatial analysis techniques. In this study, image recognition technology was applied to the problems of environmental change in coal mining area. These explanations provide some valuable supports for human to recognize and deal with the conflicts between economic development and environmental protection in coal mining areas.

The study investigated wetland change in Wanning. For this purpose, three high resolution SPOT images recorded in 2002, 2007 and 2013, respectively, were classified. The results indicated that there were little change in wetland types during 2002 and 2013. The coastal waters, culture pond and river were the main wetland types. The natural wetland trended to decline. The ditch had the largest net increase and the reservoir shrank the most. There was a dramatic increase of culture pond plaques, which making the landscape more fragmentized. The coastal waters and the land had a lot change with other wetland types. The area change in Wanning was mainly composed of the transition between the land and culture pond.

CO₂ is one of the most important greenhouse gases due to its selective absorption of long wave radiation from the Earth’s surface. In this paper, we use the column average dry air mole fraction of CO₂ (XCO₂) data from the Japanese GOSAT satellite to conduct a comprehensive and systematic analysis of temporal and spatial distribution of XCO₂. This includes: (1) analysis of seasonal change characteristics of XCO₂ data; and (2) comparative analysis of the northern and southern hemispheres carbon dioxide concentration at different latitudes. The results show that (1) from 2010 to 2013, atmospheric XCO₂ significantly increased each year. The southern hemisphere's annual averages of XCO₂ from 2010 to 2012 were 385.2 ppm, 387.3 ppm, and 389.1 ppm, while the average annual values for the northern hemisphere from 2010 to 2012 were 387.8 ppm, 390.0 ppm, and 391.7 ppm. The annual XCO₂ in northern and southern hemispheres exhibited growth rates of 1-2 ppm per year. (2) The results show seasonal change trends: winter months displayed higher XCO₂. Regarding the global spatial distribution of XCO₂, the results show that the total XCO₂ in the northern hemisphere is higher than that in the southern hemisphere. (3) The growth of global XCO₂ in 2011 and 2012 was 1.9 ppm/yr and 2.1 ppm/yr. These values are in accordance with the growth rates of 1.9 ppm/yr and 2.2 ppm/yr reported in the World Meteorological Organization's greenhouse gas bulletin.