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In this paper, in order to de-noise a chaotic signal, we compare the time-frequency deconvolution method with the wavelets method. We apply our results on different dynamical systems and show the capability of wavelets' method to reconstruct the attractor of a chaotic time series. Then, we de-noise different data sets in order to re-built their attractor using the wavelets method. The applications concern temperatures and wind fluctuations, electricity spot prices and financial data sets.

Over the last two decades, researchers investigated various approaches for detection and classification of targets in forward looking infrared (FLIR) imagery using correlation based techniques. In this paper, a novel technique is proposed to recognize and track single as well as multiple identical and/or dissimilar targets in real life FLIR sequences using a combination of extended maximum average correlation height (EMACH) and polynomial distance classifier correlation filter (PDCCF). The EMACH filters are used for the detection stage and PDCCF filter is used for the classification stage for improving the detection and discrimination capability. The EMACH and PDCCF filters are trained a priori using target images with expected size and orientation variations. In the first step, the input scene is correlated with all the detection filters (one for each desired or expected target class) and the resulting correlation outputs are combined. The regions of interest (ROI) are selected from the input scene based on the regions with higher correlation peak values in the combined correlation output. In the second step, PDCCF filter is applied to these ROIs to identify target types and reject clutters/backgrounds based on a distance measure and a threshold. Moving target detection and tracking is accomplished by applying this technique independently to all incoming image frames. Independent tracking of target(s) from one frame to the other allows the system to handle complicated situations such as a target disappearing in few frames and then reappearing in later frames. This method has been found to yield robust performance for challenging FLIR imagery in terms of faster and accurate detection and classification as well as tracking of the targets.

The Urban Recon Advanced Concepts Technology Demonstration (ACTD) has integrated a high rate 3-D laser scanner with an Inertial Navigation System (INS). The moving vehicle LIDAR sensors capture the entire street-level scene in 3-D including people, cars, building windows, doorways, and the entire gamut of urban clutter. The vehicle can travel at speeds of 20-30 mph collecting 5cm - 10cm resolution 3-D point cloud data of the street-level scene. The system collects below-the-roofline data as a vehicle moves down the street at a rate of 250,000 points per second. Data must not only be collected at a high rate of speed, but the data must rapidly be processed and ready for visualization and analysis to be useful. Software has been created, extended or integrated to support the collection, processing and visualization of these data. The hardware and software which Urban Recon has assembled and integrated will be examined.© (2005) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

A unified way of detecting and tracking moving targets with a SAR radar called SAR-MTI is presented. SAR-MTI differs from STAP or DPCA in that it is a generalization of SAR processing and can work with only a single phase center. SAR-MTI requires formation of a series of images assuming different sensor ground speeds, from vs-vtmax to vs+vtmax, where vs is the actual sensor ground speed and vtmax is the maximum target speed of interest. Each image will capture a different set of target velocities, and the complete set of images will focus all target speeds less than a desired maximum speed regardless of direction and target location. Thus the 2-dimensional SAR image is generalized to a 3-dimensional cube or stack of images. All linear moving targets less than the desired speed will be focused somewhere in the cube. The third dimension represents the along track velocity of the mover which is a piece of information not available to standard airborne MTI. A mover will remain focused at the same place within the cube as long as the motion of the mover and the sensor remain linear. Because stationary targets also focus within the detection cube, move-stop-move targets are handled smoothly and without changing waveforms or modes. Another result of this fact is that SAR-MTI has no minimum detectable velocity. SAR-MTI has an inherent ambiguity because the four-dimensions of target parameters (two dimensions in both velocity and position) are mapped into a three-dimensional detection space. This ambiguity is characterized and methods for resolving the ambiguity for geolocation are discussed. The point spread function in the detection cube is also described.

Information about atmospheric variables, such as presence and characteristics of supended particulates (aerosols), concentration of chemical species, humidity, temperature, etc., can be obtained remotely in an energetic-efficient manner from radiation produced by optical sources and backscattered by the atmosphere, drawing on the relatively strong interaction between electromagnetic radiation at optical wavelengths and particulates and molecules in the atmosphere. Atmospheric probing systems based on laser sources can provide information as a function of range and, being their operating principle essentially the same as that of radar (except for the fact that in atmospheric probing the target is by definiti8on the atmosphere zone illuminated by the laser, instead of a hard target), they are usually called laser radars or lidars (from Light Detection And Ranging). In addition to their ability to perform remote measurements- a characteristic that they share with other systems-lidars feature the availability of quasi-real-time data, high spatial resolution and a relative convenience to perform three-dimensional scans of the atmospheric volume under study. Basic lidar principles and capabilities are discussed in this work.

As the Internet continues to expand into new application domains, there is growing demand for differentiated services that provide varying degrees of Quality of Service (QoS). Until recently, the approach for service differentiation in the Internet has focused on providing QoS guarantees to individual traffic flows. This per-flow model has not been widely embraced, largely due to the vast amount of state information that needs to be maintained in the network. As a consequence, the network community is currently redefining the notion of QoS for the Internet, leading to a new service model where guarantees are made to aggregate flows, rather than to individual flows. The notions of aggregate QoS which are being defined, e.g., by the Differentiated Services (DiffServ) working group in the IETF, are very different from traditional networking concepts and require novel algorithms for traffic control and provisioning. In our work in this area we seek to derive fundamental insights into the nature and delivery of QoS to traffic aggregates, i.e. aggregate QoS. A central feature of service models for aggregate QoS is that there is no requirement for users to specify the composition or the destination of traffic. This significantly complicates the provisioning of network resources and suggests that new, feedback-intensive traffic control algorithms must be developed to mitigate uncertainty about network loading. At present there is little or no theory available to guide the development of such algorithms. In this paper we lay out a research agenda for illuminating the fundamental issues associated with QoS for aggregate traffic. By providing a theoretical reference frame for aggregate QoS, one which provides tools and techniques for congestion control, capacity provisioning, and admission control, we complement ongoing standardization efforts for differentiated services in the Internet.

Several terms are used to determine the optical properties of paper and board. Brightness, whiteness, diffuse reflectance factor, diffuse blue reflectance factor, specular gloss, spectral reflectivity and whiteness and yellowness are the used terms. The mainly used term is brightness of paper, an important property in many specifications, but this property has no visual perceptual foundation. Instead it is based on the filter chosen to measure the reflectance of pulp in the region most sensitive to the effects, of bleaching. Therefore an increasing demand for a specification of whiteness can be observed. Belonging to the concept of color, whiteness has to be measured and interpreted in the scope of CIE (Commission Internationale l'Eclairage) based on colorimetri tristimulus data transformed to agree with the perceptual assessment of whiteness. Human observers differentiate between the whiteness of the object being viewed and its brightness. A special index of whiteness is needed. No general agreement was so far reached regarding the best index which could and should be adopted universally among the two recommended by the CIE. Nearly every white sample is a little colored, having bluish, greenish or redish tint. In the post no numerical measure of tint was assigned to any of the whiteness indexes. Further problems arise that fluorescent whiteness agents are added to paper and board to increase their whiteness. This requires not only instrumental design and calibration but also the illuminant to be adopted for such measurements.

Earlier studies of the human vision system suggest that a complex object may be recognized from the qualitative features associated with its boundary. Since the human vision system uses qualitative features for object recognition, it is extremely robust to deformation caused by noise, obstruction, scale change, or optical distortion. We considered various qualitative descriptors for digital images. The descriptors developed in this research are based on the estimated curvature function from digital boundaries and can discriminate features such as straightness of a contour segment, perpendicularity of two contour segments, parallelness of two straight contour segments, parallelness of two curved contour segments in a global sense, and the direction of convergence if two straight contour segments are not parallel. We demonstrated that the qualitative descriptors developed in this paper can be applied for identification of elementary shapes, such as cylinders, bricks, cones, etc. We also discussed the recognition of more complex shapes after decomposing them into simpler components.

On the basis of the hydrochemical data available for the period of 1996-2019, the changes in the chemical composition of snowmelt and river waters on the southeast coast of Lake Baikal, that were under the long-term influence of industrial emission impact, are assessed. The study shows that the closure of the Baikalsk Pulp and Paper Mill in 2013 resulted in the decrease in the concentrations of metal cations in snow cover and in their neutralization capacity. Snow water pH value decreased, and the relative composition of major ions changed as well. In the catchment area of the Khara-Murin and Pereemnaya Rivers, hydrogen became the dominant cation during snowmelt. Further reduction of river water (especially low mineralized river water) resistance to acid components is registered.

At present, commonly endmember extraction of hyperspectral is mainly concentrated in the spectral region. Because the spatial information is not used enough, the endmember extraction is not precise which can lead to a bad result of mixed pixel decomposition and hyperspectral target detection. Actually, the distribution of endmembers in space has a certain shape and aggregation. By making use of these information we can extract more precise endmembers. Automatic morphological endmember extraction technology can make full use of abundant spectral information and spatial information. This paper based on the existed automatic morphological algorithm, presents a method in combination with maximum distance for morphological endmember extraction to solve the influence of spectral variations, which effectively extracts different classes of endmember curves. Based on the theory of orthogonal subspace projection, the authors propose an improved constrained energy minimization (CEM) algorithm, achieve better hyperspectral target detection results.

The problem of deorbiting debris is studied in this paper. As a feasible measure, a disposable satellite would be launched, attach to debris, and deorbit the space debris using a technology named electrodynamic tether (EDT). In order to deorbit multiple debris as many as possible, a suboptimal but feasible and efficient trajectory set has been designed to allow a deorbiter satellite tour the LEO small bodies per one mission. Finally a simulation given by this paper showed that a 600 kg satellite is capable of deorbiting 6 debris objects in about 230 days.

To meet the requirement of autonomous orbit determination, this paper proposes a fast curve fitting method based on earth ultraviolet features to obtain accurate earth vector direction, in order to achieve the high precision autonomous navigation. Firstly, combining the stable characters of earth ultraviolet radiance and the use of transmission model software of atmospheric radiation, the paper simulates earth ultraviolet radiation model on different time and chooses the proper observation band. Then the fast improved edge extracting method combined Sobel operator and local binary pattern (LBP) is utilized, which can both eliminate noises efficiently and extract earth ultraviolet limb features accurately. And earth’s centroid locations on simulated images are estimated via the least square fitting method using part of the limb edges. Taken advantage of the estimated earth vector direction and earth distance, Extended Kalman Filter (EKF) is applied to realize the autonomous navigation finally. Experiment results indicate the proposed method can achieve a sub-pixel earth centroid location estimation and extremely enhance autonomous celestial navigation precision.

Sars-CoV-2 is spread through contact between people and an understanding of where people are in contact with each other is necessary to prevent its spread. In this paper, the residential building density of Bulawayo was considered a proxy for high density of people. OpenStreetMap (OSM) building data was downloaded and converted from polygon to point data for use in the analysis. World View 2 data was used to visually map those areas where data was missing in OSM. More automated methods were attempted using eCognition however the short turnaround time of the project limited the success of this approach and work in this regard in ongoing. Land use attribute data was joined to the building shape file in order to select only those building which were designated as residential in nature. The residential building density was calculated per hectare and a hot spot analysis of the residential building density determined statistically significant clusters of high density residential buildings. The high density areas are mostly located in the west of the City, where new settlements are being created to accommodate new arrivals to the city. The East is typified by low density housing, largely a legacy of the City’s colonial past. A series of maps which could be printed on A3 paper were produced for the City. The maps displayed both the results of the hot spot analysis and land use and these have been made available to City officials to help in planning their response to the COVID-19 pandemic.

Based on the data of Landsat remote sensing images in 2005 and 2015 in Weihai City, this paper referenced China coastal zone land use classification system and used the Arcgis10.2 software to construct the land use database by visual interpretation , and then analyzed the spatial and temporal changes of land use in Weihai City for 10 years. The results showed that： (1) The total area of the land use in Weihai City had been enlarged, mainly by mariculture expansion with land area of 11.3 thousand hectares.(2) The land use changing rates were fast, among which the unused land attitude was the largest at 24.85%.(3) The area of these land uses change was increased, which are forest land, urban construction land and the independent industrial and mining and traffic construction land. While the arable land and grass land areas decreased. (4) The main driving forces were its economic development and its national economic and social development planning. May this paper provide some references to its regional land use sustainable development.

The Giant Magellan Telescope (GMT), one of three next-generation extremely large telescopes (ELTs), will have a 25.4- meter diameter effective aperture, and will be located on the summit of Cerro Las Campanas in Chile. Developing a new observatory for cutting-edge science operations and a 50-year lifespan poses challenges that have resulted in competing design concepts. This paper discusses the concepts that have been adopted in the GMT site master plan, including designs for the site infrastructure, telescope enclosure, and facilities. The GMTO site has been in active construction since 2015, and in the past two years has completed important steps in site development including completion of residential and office facilities, road improvements, and other necessary infrastructure to support upcoming work. This paper concludes with an overview on managing design and construction simultaneously.

The attitude information acquisition of unresolved space objects, such as micro-nano satellites and GEO objects under the way of ground-based optical observations, is a challenge to space surveillance. In this paper, a useful method is proposed to estimate the SO attitude state according to the simulation analysis of photometric data in different attitude states. The object shape model was established and the parameters of the BRDF model were determined, then the space object photometric model was established. Furthermore, the photometric data of space objects in different states are analyzed by simulation and the regular characteristics of the photometric curves are summarized. The simulation results show that the photometric characteristics are useful for attitude inversion in a unique way. Thus, a new idea is provided for space object identification in this paper.

Water body is one of the most important natural elements in coastal zone. Water bodies in coast are subdivided into offshore sea, aquaculture ponds, inland water bodies, river and so on. Remote sensing is an effective tool to obtain coastal typical objects with high spatial resolution imageries. This paper aims at existing problems of object-based image analysis application to monitor resources and environment in coastal area. For object-based recognition for water body types, relevant works have been carried out by adding spatial semantic features to the extraction process. Through analyzing the spectral, spatial and texture features of water body, the rule set for extracting water body type is established based on the topological and contextual relationship between segments. The recognition method of water body types proposed in this paper gets rid of the traditional object-based classifications based on statistical law. Using prior knowledge to construct knowledge rules with spatial semantic information makes spatial distribution characteristics in coastal zone effective in improving the accuracy of type identification.

Whilst recent years have witnessed the development and exploitation of operational Earth Observation (EO) satellite constellation data, the valorisation of historical archives has been a challenge. The European Space Agency (ESA) Landsat Multi Spectral Scanner (MSS) products cover Greenland, Iceland, Continental Europe and North Africa represent an archive of over 600,000 processed Level 1 (L1) scenes that will accompany around 1 million ESA Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) products already available. ESA began acquiring MSS data in 1975 and it is well known that this dataset can be degraded due to missing data and a loss in accuracy. For these reasons, the content of the product format has been reviewed and the ESA Landsat processing baseline significantly updated to ensure products are fit for user purposes. This paper presents the new MSS product format including the updated metadata parameters for error traceability, and the specification of the Quality Assurance Band (BQA) engineered to allow the best pixel selection and also the application of image restoration techniques. This paper also discusses major improvements applied to the radiometric and geometric processing. For the benefits of the community, ESA is now able to maximize the number of L1 MSS products that can potentially be generated from the raw Level 0 (L0) data and ensure the highest possible data quality is reached. Also, by improving product format, processing and adding a pixel based quality band, the MSS archive becomes interoperable with recently reprocessed Landsat data and that from live missions by way of assuring product quality on a pixel basis.

Satellite remote sensing technology has become one of the most effective methods for land surface monitoring in recent years, due to its advantages such as short period, large scale and rich information. Meanwhile, road extraction is an important field in the applications of high resolution remote sensing images. An intelligent and automatic road extraction algorithm with high precision has great significance for transportation, road network updating and urban planning. The fuzzy c-means (FCM) clustering segmentation algorithms have been used in road extraction, but the traditional algorithms did not consider spatial information. An improved fuzzy C-means clustering algorithm combined with spatial information (SFCM) is proposed in this paper, which is proved to be effective for noisy image segmentation. Firstly, the image is segmented using the SFCM. Secondly, the segmentation result is processed by mathematical morphology to remover the joint region. Thirdly, the road centerlines are extracted by morphology thinning and burr trimming. The average integrity of the centerline extraction algorithm is 97.98%, the average accuracy is 95.36% and the average quality is 93.59%. Experimental results show that the proposed method in this paper is effective for road centerline extraction.

The paper proposes a method for fuzzy interactive enhancement of objects identification in the image which allows identifying hidden or no defined details and objects in the images. The application of the method and its difference from other image enhancement techniques are shown. The paper presents the algorithm and describes the basic processing procedures (sampling, scaling, convolution, contrast). The main processing parameters (increasing and reduction of dimensions, convolutions, brightness, and thresholds contrast) are demonstrated. The results from the applied algorithm are explained on an example related to landfill Kutchino in the Moscow region, on the satellite images with low and high spatial resolution.

Understanding the 3D structure of the environment is advantageous for many tasks in the field of robotics and autonomous vehicles. From the robot’s point of view, 3D perception is often formulated as a depth image reconstruction problem. In the literature, dense depth images are often recovered deterministically from stereo image disparities. Other systems use an expensive LiDAR sensor to produce accurate, but semi-sparse depth images. With the advent of deep learning there have also been attempts to estimate depth by only using monocular images. In this paper we combine the best of the two worlds, focusing on a combination of monocular images and low cost LiDAR point clouds. We explore the idea that very sparse depth information accurately captures the global scene structure while variations in image patches can be used to reconstruct local depth to a high resolution. The main contribution of this paper is a supervised learning depth reconstruction system based on a deep convolutional neural network. The network is trained on RGB image patches reinforced with sparse depth information and the output is a depth estimate for each pixel. Using image and point cloud data from the KITTI vision dataset we are able to learn a correspondence between local RGB information and local depth, while at the same time preserving the global scene structure. Our results are evaluated on sequences from the KITTI dataset and our own recordings using a low cost camera and LiDAR setup.

This paper presents the achievements for the last 10 years of the Remote Sensing and Geo-Environment Laboratory of the Cyprus University of Technology in the Earth observation through the ERATOSTHENES Research Centre. Over the past 10 years, the Centre has secured competitive research funding from various sources, such as the European Commission, the Cyprus Research Promotion Foundation, as well as industrial partners, having participated either as a coordinator or as a partner in more than 60 research projects. The research activities of the Centre encompass remote sensing and GIS applications in the fields of Cultural Heritage, Agriculture, Water Resource Management, Environment, Infrastructure, Marine Spatial Planning, Atmospheric, Air Pollution and Coastal Applications, Natural Resource Management and Hazard Assessment. The aim of this paper is to map the existing activities and identify the future trends and goals of the Eratosthenes Research Centre for the next 15 years.

Several problems of spectral photometry analysis of handwritten documents have been studied like combined reflection from the both ink and paper material impregnated by ink, the illumination spatial non-uniformity with small light intensity, flexibility and instability of paper sheets. The images acquired at different wavelengths were spatially matched using developed computer algorithm and then regions of interests, i.e. ink lines were extracted. Then reflection spectra obtained at all pixels of ink lines were composed in vector representation, and comparison of the vectors was conducted. As the result, degree of correspondence between different fragments of text has been evaluated. The optical setup and its calibration procedures are considered in detail as well as results of image processing, matching, segmentation and spectral photometry evaluation of ink. Experimental results of different text fragments matching and estimates of text fragments spectral correspondence degree are presented and discussed.

A presence of an accurate dataset is the key requirement for a successful development of an optical flow estimation algorithm. A large number of freely available optical flow datasets were developed in recent years and gave rise for many powerful algorithms. However most of the datasets include only images captured in the visible spectrum. This paper is focused on the creation of a multispectral optical flow dataset with an accurate ground truth. The generation of an accurate ground truth optical flow is a rather complex problem, as no device for error-free optical flow measurement was developed to date. Existing methods for ground truth optical flow estimation are based on hidden textures, 3D modelling or laser scanning. Such techniques are either work only with a synthetic optical flow or provide a sparse ground truth optical flow. In this paper a new photogrammetric method for generation of an accurate ground truth optical flow is proposed. The method combines the benefits of the accuracy and density of a synthetic optical flow datasets with the flexibility of laser scanning based techniques. A multispectral dataset including various image sequences was generated using the developed method. The dataset is freely available on the accompanying web site.

A new methodology for 3D change detection which can support effective robot sensing and navigation in a reconstructed indoor environment is presented in this paper. We register the RGB-D images acquired with an untracked camera into a globally consistent and accurate point-cloud model. This paper introduces a robust system that detects camera position for multiple RGB video frames by using both photo-metric error and feature based method. It utilizes the iterative closest point (ICP) algorithm to establish geometric constraints between the point-cloud as they become aligned. For the change detection part, a bag-of-word (DBoW) model is used to match the current frame with the previous key frames based on RGB images with Oriented FAST and Rotated BRIEF (ORB) feature. Then combine the key-frame translation and ICP to align the current point-cloud with reconstructed 3D scene to localize the robot position. Meanwhile, camera position and orientation are used to aid robot navigation. After preprocessing the data, we create an Octomap Model to detect the scene change measurements. The experimental evaluations performed to evaluate the capability of our algorithm show that the robot's location and orientation are accurately determined and provide promising results for change detection indicating all the object changes with very limited false alarm rate.

Pesticide residues in the fruits, vegetables and agricultural commodities are harmful to humans and are becoming a health concern nowadays. Detection of pesticide residues on various commodities in an open environment is a challenging task. Hyperspectral sensing is one of the recent technologies used to detect the pesticide residues. This paper addresses the problem of detection of pesticide residues of Cyantraniliprole on grapes in open fields using multi temporal hyperspectral remote sensing data. The re ectance data of 686 samples of grapes with no, single and double dose application of Cyantraniliprole has been collected by handheld spectroradiometer (MS- 720) with a wavelength ranging from 350 nm to 1052 nm. The data collection was carried out over a large feature set of 213 spectral bands during the period of March to May 2015. This large feature set may cause model over-fitting problem as well as increase the computational time, so in order to get the most relevant features, various feature selection techniques viz Principle Component Analysis (PCA), LASSO and Elastic Net regularization have been used. Using this selected features, we evaluate the performance of various classifiers such as Artificial Neural Networks (ANN), Support Vector Machine (SVM), Random Forest (RF) and Extreme Gradient Boosting (XGBoost) to classify the grape sample with no, single or double application of Cyantraniliprole. The key finding of this paper is; most of the features selected by the LASSO varies between 350-373nm and 940-990nm consistently for all days. Experimental results also shows that, by using the relevant features selected by LASSO, SVM performs better with average prediction accuracy of 91.98 % among all classifiers, for all days.

Tracking deep-field objects across a wide field of view requires the use of high resolution image sensors. This imposes a burden on processing systems which must detect and extract features in an image. Deep-field objects have limited spatial support within a wide field of view image and accordingly much of the recorded scene contains superfluous information about the environment. This paper explores the generation of a foveal scale space using the log-polar transform. Foveal scale space is the scale space representation of an input scene where the spatial support of the image at each scale increases with scale and the number of pixels remains constant across each slice of scale space. This paper reports the formulation of a transformation consisting of a peripheral region defined by the log-polar transform and a foveal region where resolution is constant. A method for performing diffusion in this domain is shown and the generation of the foveal scale space is presented.

The paper presents a passive technique for real-time altitude above ground level estimation for aerial vehicles using a monocular camera, a GPS receiver and an inertial measurement unit. The paper discusses a robust method for featureless registration of successive images through phase correlation using Gram polynomial decimation. Altitude is estimated by formulating the shift in pixels between the images in terms of distance travelled, calculated using corresponding GPS latitudes and longitudes. Resultant value is compensated for changes in pitch before being passed through Savitzky-Golay filter. The system can generate results every 300ms on a lowcost commercial digital signal processor with mean error of 2m and standard deviation of 13m. The proposed system is suitable for speeds up to 300m/s and altitudes up to 3000m.

For the reconstruction of the cloud base height a method was developed based on taking pictures of the sky by a pair of digital photo cameras from the ground and subsequent processing of the obtained sequence of stereo frames. Since the directions of the optical axes of the stereo cameras are not exactly known, a procedure of adjusting of obtained frames was developed which use photographs of the night starry sky. In the second step, the method of the morphological analysis of images is used to determine the relative shift of the coordinates of some fragment of cloud. The shift is used to estimate the searched cloud base height. The proposed method can be used for automatic processing of stereo data and getting the cloud base height. The earlier paper described a mathematical model of stereophotography measurement, poses and solves the problem of adjusting of optical axes of the cameras in paraxial (first-order geometric optics) approximation and was applied for the central part of the sky frames. This paper describes the model of experiment which takes into account lens distortion in Seidel approximation (depending on the third order of the distance from optical axis). Based on this model a procedure of simultaneous camera position adjusting and estimation of parameters of lens distortion in Seidel approximation was developed. The first experimental results of its application are shown.

Yacht has become a fashionable way for entertainment. However, to obtain the precise location of a yacht docked at a port has become one of the concerns of a yacht manager. To deal with this issue, we adopt a positioning method based on the principle of binocular parallax and background difference in this paper. Binocular parallax uses cameras to get multi-dimensional perspective of the yacht based on geometric principle of imaging. In order to simplify the yacht localization problem, we install LED light indicator as the key point on a yacht. And let it flash at a certain frequency during day time and night time. After getting the distance between the LED and the cameras, locating the yacht is easy. Compared with other traditional positioning methods, this method is simpler and easier to implement. In this paper, we study the yacht positioning method using the LED indicator. Simulation experiment is done for a yacht model in the distance of 3 meters. The experimental result shows that our method is feasible and easy to implement with a small 15% positioning error.

Every year thousands of people are displaced by conflicts or natural disasters and often gather in large camps. Knowing how many people have been gathered is crucial for an efficient relief operation. However, it is often difficult to collect exact information on the total number of the population. This paper presents the improved morphological methodology for the estimation of dwellings structures located in several Internally Displaced Persons (IDPs) Camps, based on Very High Resolution (VHR) multispectral satellite imagery with pixel sizes of 1 meter or less including GeoEye-1, WorldView-2, QuickBird-2, Ikonos-2, Pleiades-A and Pleiades-B. The main topic of this paper is the approach enhancement with selection of feature extraction algorithm, the improvement and automation of pre-processing and results verification. For the informal and temporary dwellings extraction purpose the high quality of data has to be ensured. The pre-processing has been extended by including the input data hierarchy level assignment and data fusion method selection and evaluation. The feature extraction algorithm follows the procedure presented in Jenerowicz, M., Kemper, T., 2011. Optical data are analysed in a cyclic approach comprising image segmentation, geometrical, textural and spectral class modeling aiming at camp area identification. The successive steps of morphological processing have been combined in a one stand-alone application for automatic dwellings detection and enumeration. Actively implemented, these approaches can provide a reliable and consistent results, independent of the imaging satellite type and different study sites location, providing decision support in emergency response for the humanitarian community like United Nations, European Union and Non-Governmental relief organizations.

Over the last 2-3 decades, the economy of many countries around the world has been developed rapidly but it was unbalanced development because of expecting on economic growth only. Meanwhile it lacked of effective planning in the use of natural resources. This can significantly induce climate change which is major cause of natural disaster. Hereby, Thailand has also suffered from natural disaster for ages. Especially, the flood which is most hazardous disaster in Thailand can annually result in the great loss of life and property, environment and economy. Since the flood management of country is inadequate efficiency. It is unable to support the flood analysis comprehensively. This paper applied Geographic Information System and Multi-Criteria Decision Making to create flood risk model at regional scale. Angthong province in Thailand was used as the study area. In practical process, Fuzzy logic technique has been used to improve specialist’s assessment by implementing with Fuzzy membership because human decision is flawed under uncertainty then AHP technique was processed orderly. The hierarchy structure in this paper was categorized the spatial flood factors into two levels as following: 6 criteria (Meteorology, Geology, Topography, Hydrology, Human and Flood history) and 8 factors (Average Rainfall, Distance from Stream, Soil drainage capability, Slope, Elevation, Land use, Distance from road and Flooded area in the past). The validity of the pair-wise comparison in AHP was shown as C.R. value which indicated that the specialist judgment was reasonably consistent. FAHP computation result has shown that the first priority of criteria was Meteorology. In addition, the Rainfall was the most influencing factor for flooding. Finally, the output was displayed in thematic map of Angthong province with flood risk level processed by GIS tools. The map was classified into: High Risk, Moderate Risk and Low Risk (13.20%, 75.58%, and 11.22% of total area).

During the last ten years, the availability of images acquired from unmanned aerial vehicles (UAVs) has been continuously increasing due to the improvements and economic success of flight and sensor systems. From our point of view, reliable and automatic image-based change detection may contribute to overcoming several challenging problems in military reconnaissance, civil security, and disaster management. Changes within a scene can be caused by functional activities, i.e., footprints or skid marks, excavations, or humidity penetration; these might be recognizable in aerial images, but are almost overlooked when change detection is executed manually. With respect to the circumstances, these kinds of changes may be an indication of sabotage, terroristic activity, or threatening natural disasters. Although image-based change detection is possible from both ground and aerial perspectives, in this paper we primarily address the latter. We have applied an extended approach to change detection as described by Saur and Kruger,1 and Saur et al.2 and have built upon the ideas of Saur and Bartelsen.3 The commercial simulation environment Virtual Battle Space 3 (VBS3) is used to simulate aerial "before" and "after" image acquisition concerning flight path, weather conditions and objects within the scene and to obtain synthetic videos. Video frames, which depict the same part of the scene, including "before" and "after" changes and not necessarily from the same perspective, are registered pixel-wise against each other by a photogrammetric concept, which is based on a homography. The pixel-wise registration is used to apply an automatic difference analysis, which, to a limited extent, is able to suppress typical errors caused by imprecise frame registration, sensor noise, vegetation and especially parallax effects. The primary concern of this paper is to seriously evaluate the possibilities and limitations of our current approach for image-based change detection with respect to the flight path, viewpoint change and parametrization. Hence, based on synthetic "before" and "after" videos of a simulated scene, we estimated the precision and recall of automatically detected changes. In addition and based on our approach, we illustrate the results showing the change detection in short, but real video sequences. Future work will improve the photogrammetric approach for frame registration, and extensive real video material, capable of change detection, will be acquired.

This paper presents a simulation method of hyper-spectral dynamic scene and image sequence for hyper-spectral equipment evaluation and target detection algorithm. Because of high spectral resolution, strong band continuity, anti-interference and other advantages, in recent years, hyper-spectral imaging technology has been rapidly developed and is widely used in many areas such as optoelectronic target detection, military defense and remote sensing systems. Digital imaging simulation, as a crucial part of hardware in loop simulation, can be applied to testing and evaluation hyper-spectral imaging equipment with lower development cost and shorter development period. Meanwhile, visual simulation can produce a lot of original image data under various conditions for hyper-spectral image feature extraction and classification algorithm. Based on radiation physic model and material characteristic parameters this paper proposes a generation method of digital scene. By building multiple sensor models under different bands and different bandwidths, hyper-spectral scenes in visible, MWIR, LWIR band, with spectral resolution 0.01μm, 0.05μm and 0.1μm have been simulated in this paper. The final dynamic scenes have high real-time and realistic, with frequency up to 100 HZ. By means of saving all the scene gray data in the same viewpoint image sequence is obtained. The analysis results show whether in the infrared band or the visible band, the grayscale variations of simulated hyper-spectral images are consistent with the theoretical analysis results.

While hyperspectral data shares rich spectrum information, it has numbers of bands with high correlation coefficients, causing great data redundancy. A reasonable band selection is important for subsequent processing. Bands with large amount of information and low correlation should be selected. On this basis, according to the needs of target detection applications, the spectral characteristics of the objects of interest are taken into consideration in this paper, and a new method based on spectrum difference is proposed. Firstly, according to the spectrum differences of targets of interest, a difference matrix which represents the different spectral reflectance of different targets in different bands is structured. By setting a threshold, the bands satisfying the conditions would be left, constituting a subset of bands. Then, the correlation coefficients between bands are calculated and correlation matrix is given. According to the size of the correlation coefficient, the bands can be set into several groups. At last, the conception of normalized variance is used on behalf of the information content of each band. The bands are sorted by the value of its normalized variance. Set needing number of bands, and the optimum band combination solution can be get by these three steps. This method retains the greatest degree of difference between the target of interest and is easy to achieve by computer automatically. Besides, false color image synthesis experiment is carried out using the bands selected by this method as well as other 3 methods to show the performance of method in this paper.

The detection of objects, or persons, is a common task in the fields of environment surveillance, object observation or danger defense. There are several approaches for automated detection with conventional imaging sensors as well as with LiDAR sensors, but for the latter the real-time detection is hampered by the scanning character and therefore by the data distortion of most LiDAR systems. The paper presents a solution for real-time data acquisition of a flash LiDAR sensor with synchronous raw data analysis, point cloud calculation, object detection, calculation of the next best view and steering of the pan-tilt head of the sensor. As a result the attention is always focused on the object, independent of the behavior of the object. Even for highly volatile and rapid changes in the direction of motion the object is kept in the field of view. The experimental setup used in this paper is realized with an elementary person detection algorithm in medium distances (20 m to 60 m) to show the efficiency of the system for objects with a high angular speed. It is easy to replace the detection part by any other object detection algorithm and thus it is easy to track nearly any object, for example a car or a boat or an UAV in various distances.

Sustained ocean color monitoring is vital to understanding the marine ecosystem. It has been identified as an Essential Climate Variable (ECV) and is a vital parameter in understanding long-term climate change. Furthermore, observations can be beneficial in observing oil spills, harmful algal blooms and the health of fisheries. Space-based remote sensing, through MERIS, SeaWiFS and MODIS instruments, have provided a means of observing the vast area covered by the ocean which would otherwise be impossible using ships alone. However, the large pixel size makes measurements of lakes, rivers, estuaries and coastal zones difficult. Furthermore, retirement of a number of widely used and relied upon ocean observation instruments, particularly MERIS and SeaWiFS, leaves a significant gap in ocean color observation opportunities This paper presents an overview of the SeaHawk mission, a collaborative effort between Clyde Space Ltd., the University of North Carolina Wilmington, Cloudland Instruments, and Goddard Spaceflight Center, funded by the Gordon and Betty Moore Foundation. The goal of the project is to enhance the ability to observe ocean color in high temporal and spatial resolution through use of a low-cost, next-generation ocean color sensor flown aboard a CubeSat. The final product will be 530 times smaller (0.0034 vs 1.81m 3 ) and 115 time less massive (3.4 vs 390.0kg) but with a ground resolution 10 times better whilst maintaining a signal/noise ratio 50% that of SeaWiFs. This paper will describe the objectives of the mission, outline the payload specification and the spacecraft platform to support it.

Terra MODIS has successfully operated for more than 16 years since its launch in December 1999. From its observations, many science data products have been generated in support of a broad range of research activities and remote sensing applications. Terra MODIS has operated in a number of configurations and experienced a few anomalies, including spacecraft and instrument related events. MODIS collects data in 36 spectral bands that are calibrated regularly by a set of on-board calibrators for their radiometric, spectral, and spatial performance. Periodic lunar observations and long-term radiometric trending over well-characterized ground targets are also used to support sensor on-orbit calibration. Dedicated efforts made by the MODIS Characterization Support Team (MCST) and continuing support from the MODIS Science Team have contributed to the mission success, enabling well-calibrated data products to be continuously generated and routinely delivered to users worldwide. This paper presents an overview of Terra MODIS mission operations, calibration activities, and instrument performance of the past 16 years. It illustrates and describes the results of key sensor performance parameters derived from on-orbit calibration and characterization, such as signal-to-noise ratio (SNR), noise equivalent temperature difference (NEdT), solar diffuser (SD) degradation, changes in sensor responses, center wavelengths, and band-to-band registration (BBR). Also discussed in this paper are the calibration approaches and strategies developed and implemented in support of MODIS Level 1B data production and re-processing, major challenging issues, and lessons learned. (2016) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.

Augmented reality(AR) technology is becoming the study focus, and the AR effect of the light field imaging makes the research of light field camera attractive. The micro array structure was adopted in most light field information acquisition system(LFIAS) since emergence of light field camera, micro lens array(MLA) and micro pinhole array(MPA) system mainly included. It is reviewed in this paper the structure of the LFIAS that the Light field camera commonly used in recent years. LFIAS has been analyzed based on the theory of geometrical optics. Meanwhile, this paper presents a novel LFIAS, plane grating system, we call it "micro aperture array(MAA." And the LFIAS are analyzed based on the knowledge of information optics; This paper proves that there is a little difference in the multiple image produced by the plane grating system. And the plane grating system can collect and record the amplitude and phase information of the field light.

Many remote sensing applications rely on simulated scenes to perform complex interaction and sensitivity studies that are not possible with real-world scenes. These applications include the development and validation of new and existing algorithms, understanding of the sensor's performance prior to launch, and trade studies to determine ideal sensor configurations. The accuracy of these applications is dependent on the realism of the modeled scenes and sensors. The Digital Image and Remote Sensing Image Generation (DIRSIG) tool has been used extensively to model the complex spectral and spatial texture variation expected in large city-scale scenes and natural biomes. In the past, material properties that were used to represent targets in the simulated scenes were often assumed to be Lambertian in the absence of hand-measured directional data. However, this assumption presents a limitation for new algorithms that need to recognize the anisotropic behavior of targets. We have developed a new method to model and simulate large-scale high-resolution terrestrial scenes by combining bi-directional reflectance distribution function (BRDF) products from Moderate Resolution Imaging Spectroradiometer (MODIS) data, high spatial resolution data, and hyperspectral data. The high spatial resolution data is used to separate materials and add textural variations to the scene, and the directional hemispherical reflectance from the hyperspectral data is used to adjust the magnitude of the MODIS BRDF. In this method, the shape of the BRDF is preserved since it changes very slowly, but its magnitude is varied based on the high resolution texture and hyperspectral data. In addition to the MODIS derived BRDF, target/class specific BRDF values or functions can also be applied to features of specific interest. The purpose of this paper is to discuss the techniques and the methodology used to model a forest region at a high resolution. The simulated scenes using this method for varying view angles show the expected variations in the reflectance due to the BRDF effects of the Harvard forest. The effectiveness of this technique to simulate real sensor data is evaluated by comparing the simulated data with the Landsat 8 Operational Land Image (OLI) data over the Harvard forest. Regions of interest were selected from the simulated and the real data for different targets and their Top-of-Atmospheric (TOA) radiance were compared. After adjusting for scaling correction due to the difference in atmospheric conditions between the simulated and the real data, the TOA radiance is found to agree within 5 % in the NIR band and 10 % in the visible bands for forest targets under similar illumination conditions. The technique presented in this paper can be extended for other biomes (e.g. desert regions and agricultural regions) by using the appropriate geographic regions. Since the entire scene is constructed in a simulated environment, parameters such as BRDF or its effects can be analyzed for general or target specific algorithm improvements. Also, the modeling and simulation techniques can be used as a baseline for the development and comparison of new sensor designs and to investigate the operational and environmental factors that affects the sensor constellations such as Sentinel and Landsat missions.

With the acceleration of urbanization in China, most rural areas formed a widespread phenomenon, i.e., destitute village, labor population loss, land abandonment and rural hollowing. And it formed a unique hollow village problem in China finally. The governance of hollow village was the objective need of the development of economic and social development in rural area for Chinese government, and the research on the evaluation method of rural hollowing was the premise and basis of the hollow village governance. In this paper, several evaluation methods were used to evaluate the rural hollowing based on the survey data, land use data, social and economic development data. And these evaluation indexes were the transition of homesteads, the development intensity of rural residential areas, the per capita housing construction area, the residential population proportion in rural area, and the average annual electricity consumption, which can reflect the rural hollowing degree from the land, population, and economy point of view, respectively. After that, spatial analysis method of GIS was used to analyze the evaluation result for each index. Based on spatial raster data generated by Kriging interpolation, we carried out re-classification of all the results. Using the fuzzy clustering method, the rural hollowing degree in Ningxia area was reclassified based on the two spatial scales of county and village. The results showed that the rural hollowing pattern in the Ningxia Hui Autonomous Region had a spatial distribution characteristics that the rural hollowing degree was obvious high in the middle of the study area but was low around the study area. On a county scale, the specific performances of the serious rural hollowing were the higher degree of extensive land use, and the lower level of rural economic development and population transfer concentration. On a village scale, the main performances of the rural hollowing were the rural population loss and idle land. The evaluation method of rural hollowing constructed in this paper can effectively carry out a comprehensive degree zoning of rural hollowing, which can make orderly decision support plans of hollow village governance for the government.

The Moderate Resolution Imaging Spectroradiometer (MODIS) is one of the key sensors among the suite of remote sensing instruments on board the Earth Observing System Terra and Aqua spacecrafts. For each MODIS spectral band, the sensor degradation has been measured using a set of on-board calibrators. MODIS also uses lunar observations from nearly monthly spacecraft maneuvers, which bring the Moon into view through the space-view port, helping to characterize the scan mirror degradation at a different angles of incidence. Throughout the Terra mission, contamination of the long-wave infrared photovoltaic band (LWIR PV, bands 27-30) signals has been observed in the form of electronic crosstalk, where signal from each of the detectors among the LWIR PV bands can leak to the other detectors, producing a false signal contribution. This contamination has had a noticeable effect on the MODIS science products since 2010 for band 27, and since 2012 for bands 28 and 29. Images of the Moon have been used effectively for determining the contaminating bands, and have also been used to derive correction coefficients for the crosstalk contamination. In this paper, we introduce an updated technique for characterizing the crosstalk contamination among the LWIR PV bands using data from lunar calibration events. This approach takes into account both the in-band and out-of-band contribution to the signal contamination for each detector in bands 27-30, which is not considered in previous works. The crosstalk coefficients can be derived for each lunar calibration event, providing the time dependence of the crosstalk contamination. Application of these coefficients to Earth-view image data results in a significant reduction in image contamination and a correction of the scene radiance for bands 27- 30. Also, this correction shows a significant improvement to certain threshold tests in the MODIS Level-2 Cloud Mask. In this paper, we will detail the methodology used to identify and correct the crosstalk contamination for the LWIR PV bands in Terra MODIS. The derived time-dependent crosstalk coefficients will also be discussed. Finally, the impact of the correction on the downstream data products will be analyzed.

The aim of this paper is to explore the concept of "smart" cities from the perspective of inclusive community participation and Geographical Information Systems (GIS).The concept of a smart city is critically analyzed, focusing on the power/knowledge implications of a "bottom-up" approach in planning and how GIS could encourage community participation in smart urban planning. The paper commences with a literature review of what it means for cities to be "smart". It draws supporting definitions and critical insights into smart cities with respect to the built environment and the human factor. The second part of the paper, analyzes the "bottom-up" approach in urban planning, focusing on community participation reviewing forms and expressions through good practices from European cities. The third part of the paper includes a debate on how smart urban cities policies and community participation interact and influence each other. Finally, the paper closes with a discussion of the insights that were found and offers recommendations on how this debate could be addressed by Information and Communication Technologies and GIS in particular.

Announced in 2012, started in 2013 and completed in 2015, the ALMA high bandwidth communication system has become a key factor to achieve the operational and scientific goals of ALMA. This paper summarizes the technical, organizational, and operational goals of the ALMA Optical Link Project, focused in the creation and operation of an effective and sustainable communication infrastructure to connect the ALMA Operations Support Facility and Array Operations Site, both located in the Atacama Desert in the Northern region of Chile, with the point of presence of REUNA in Antofagasta, about 400km away, and from there to the Santiago Central Office in the Chilean capital through the optical infrastructure created by the EC-funded EVALSO project and now an integral part of the REUNA backbone. This new infrastructure completed in 2014 and now operated on behalf of ALMA by REUNA, the Chilean National Research and Education Network, uses state of the art technologies, like dark fiber from newly built cables and DWDM transmission, allowing extending the reach of high capacity communication to the remote region where the Observatory is located. The paper also reports on the results obtained during the first year and a half testing and operation period, where different operational set ups have been experienced for data transfer, remote collaboration, etc. Finally, the authors will present a forward look of the impact of it to both the future scientific development of the Chajnantor Plateau, where many installations area are (and will be) located, as well as the potential Chilean scientific backbone long term development.

This paper is based on a proposed unmanned aerial system platform that is to be outfitted with high-resolution sensors. The proposed system is to be tethered to a moveable ground station, which may be a research vessel or some form of ground vehicle (e.g., car, truck, or rover). The sensors include, at a minimum: camera, infrared sensor, thermal, normalized difference vegetation index (NDVI) camera, global positioning system (GPS), and a light-based radar (LIDAR). The purpose of this paper is to provide an overview of existing methods for pollution detection of failing septic systems, and to introduce the proposed system. Future work will look at the high-resolution data from the sensors and integrating the data through a process called information fusion. Typically, this process is done using the popular and well-published Kalman filter (or its nonlinear formulations, such as the extended Kalman filter). However, future work will look at using a new type of strategy based on variable structure estimation for the information fusion portion of the data processing. It is hypothesized that fusing data from the thermal and NDVI sensors will be more accurate and reliable for a multitude of applications, including the detection of pollution entering the Chesapeake Bay area.

For the reconstruction of some geometrical characteristics of clouds a method was developed based on taking pictures of the sky by a pair of digital photo cameras and subsequent processing of the obtained sequence of stereo frames to obtain the height of the cloud base. Since the directions of the optical axes of the stereo cameras are not exactly known, a procedure of adjusting of obtained frames was developed which use photographs of the night starry sky. In the second step, the method of the morphological analysis of images is used to determine the relative shift of the coordinates of some fragment of cloud. The shift is used to estimate the searched cloud base height. The proposed method can be used for automatic processing of stereo data and getting the cloud base height. The earlier paper described a mathematical model of stereophotography measurement, poses and solves the problem of adjusting of optical axes of the cameras in paraxial (first-order geometric optics) approximation and was applied for the central part of the sky frames. This paper describes the model of experiment which takes into account lens distortion in Seidel approximation (depending on the third order of the distance from optical axis). We developed procedure of simultaneous camera position calibration and estimation of parameters of lens distortion in Seidel approximation.

The polarized hyperspectral images (PHSI) include polarization, spectral, spatial and radiant features, which provide more information about objects and scenes than traditional intensity or spectrum ones. And polarization can suppress the background and highlight the object, leading to the high potential to improve camouflaged target detection. So polarized hyperspectral imaging technique has aroused extensive concern in the last few years. Nowadays, the detection methods are still not very mature, most of which are rooted in the detection of hyperspectral image. And before using these algorithms, Stokes vector is used to process the original four-dimensional polarized hyperspectral data firstly. However, when the data is large and complex, the amount of calculation and error will increase. In this paper, tensor is applied to reconstruct the original four-dimensional data into new three-dimensional data, then, the constraint energy minimization (CEM) is used to process the new data, which adds the polarization information to construct the polarized spectral filter operator and takes full advantages of spectral and polarized information. This way deals with the original data without extracting the Stokes vector, so as to reduce the computation and error greatly. The experimental results also show that the proposed method in this paper is more suitable for the target detection of the PHSI.

Resourcesat-2A is a follow-on mission of Resourcesat-2, belongs to Indian Remote Sensing Program. It is expected to be launched in 2016 and is dedicated mainly to agricultural applications. One of the payloads, LISS3* is a medium resolution (23.5 m) sensor having four multispectral bands from 450 to 1650 nm. These spectral bands are named as B2 (550 nm), B3 (650 nm), B4 (815 nm) and B5 (1625 nm) respectively covering Visible, Near Infrared (NIR) and Short Wave Infrared (SWIR) regions. In order to provide quality data to the user community for long term scientific applications pre-flight ground calibration is carried out. This paper describes pre-flight spectral and radiometric calibration of LISS3* payload and its performance evaluation. Since it is a continuity mission to Resourcesat-2, which was launched in April 2011 so for generating long-term data record and correlation with previous observations, its parameters are compared with Resourcesat-2 LISS3* payload. The main spectral parameters like central wavelength, and pass band is determined using system level spectral response and compared for both the mission and differences are outlined. The next important exercise is pre-flight radiometric calibration, which was carried out in laboratory using a standard integrating sphere traceable to NIST standards. This paper highlights the technique adopted during pre-flight calibration of the radiometric response and performance assessment of all 4 bands of LISS3* in terms of major electro-optical parameters like Signal to Noise Ratio (SNR), Saturation Radiance (SR) etc. The observed SR shows that the sensor can measure spectral radiance from Earth up to 100% albedo.

Complex morphology target, which is size-varying and shape-varying, is a great challenge for infrared surveillance system. In this paper, temporal low-rank and sparse decomposition model and spatial low-rank and sparse decomposition model are designed respectively. Subsequently, a joint spatial-temporal detection method of complex morphology target is presented. Firstly, initial background subspace is obtained based on training sequence which does not contain infrared target. Secondly, temporal target image is recovered by l1 minimization after projecting orthogonal to background subspace. Thirdly, original image is decomposed into background image and spatial target image using inexact augmented Lagrange multipliers approach. Fourthly, by fusing the two target images, the possible small targets can be extracted well. Finally, background subspace is updated based on incremental singular value decomposition algorithm. The experimental results show that our method is effective and robust to detect complex morphology infrared targets. In particular, the proposed method can extract targets accurately, which is important for target recognition. © (2016) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.

It is urgent to tracking and making a catalog for space debris, since they are such serious threats to spacecraft. Space-based detection possess a great of development potential for its low energy consumption, high precision and miniaturization and other features. This paper discussed the capability of space-based combined system that is laser ranging and imaging integrated communication system. With the diameter of 15cm of space debris, the limit distance of communication, ranging and imaging system are discussed. The result shows that the limit distance of communication and energy imaging is longer, and the main factor to limit the distance is ranging and diffraction limit of imaging system.