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Mapping from High Resolution Indian SatellitesP.V. Radhadevi Head Digital Mapping & Modeling Division Advanced data Processing Research Institute Department of Space India Adrin_radhadevi@yahoo.co.in Archana Mahapatra Advanced data Processing Research Institute Department of Space India V.Nagasubramanian Advanced data Processing Research Institute Department of Space India M.V Jyothi & S.S Solanki Advanced data Processing Research Institute Department of Space India J. Saibaba & Geeta Varadan Advanced data Processing Research Institute Department of Space India 1. Introduction The major thrust of the current and future technologies are on generation of spatial information on large scale. In order to transfer the spatial components from images to map, it is essential to have accurate base maps on large scale. The current availability is restricted to 1 : 50000 / 1 : 25000 scale from the published topographical maps. In view of this constraint, it is a necessary prerequisite to generate large scale base maps for use in various thematic applications. The large-scale map (LSM) ,being generated using high-resolution space borne imagery, is conceived as creation of Digital Topographical Database comprising both vector and raster layers representing the topography of a given area. It is proposed to generate products both in digital and various analog formats and media to meet the requirements of diverse user community for undertaking developmental activities. Indian space programme witnessed several major accomplishments and scaled newer heights in mastering space technology during the last few years. It is significant to note that the remarkable successes were the result of well-orchestrated programmes undertaken by the department aimed at achieving total self-reliance in this cutting edge technology solely through indigenous efforts and utilizing the expertise available within the organization. Development over four and a half decades of Indian Space programme are shown in Figure 1. The definition of "Large Scale" is not fixed and the limits are specified depending upon the local conditions. In some countries the map scale 1:1000 is used as limit, while in other countries 1:5000 or even 1 : 10 000 is belonging to large scale depending upon the available topographic maps, the requirements and the financial situation.With the higher spatial resolution satellite images available on these days, it is possible to prepare accurate base maps larger than 1:10,000 scale. Cartosat series of satellites with stereo mapping capabilities have become main stay towards large scale mapping for urban and rural applications. This paper summarizes the results of some pilot studies done at ADRIN towards testing the mapping potential of Cartosat-1 Cartosat-2 and Cartosat-2A satellites.Figure 1 shows four and a half decades of Indian Space Programme  Figure 1: Four and a Half Decades of Indian Space Programme (Source: ISRO) 2. Mapping from Satellite imagery Capability for mapping from satellite imagery depends upon the following factors. 2.1. Resolution of the Image Spatial as well as spectral resolution of the image is important. The ground sampling distance (GSD) the distance of the pixel centres on the ground must not be the same like the size of the physical pixels projected to the ground. We do have the influence of the optics, the actual situation of the atmosphere and a numerical over or under sampling. In addition the image quality, especially the contrast, is depending upon the grey value range which goes from 6 bit or 64 different grey values to 12 bit or 4096 different grey values. Object identification is easier in multi-spectral images compared to panchromatic images. 2.2.Viewing Geometry/Agility of Satellite High resolution images can be acquired by different methods like step and stare technology, TDI, asynchronous imaging, synchronous imaging etc. Stability of the platform is very important in deciding the final achievable accuracy. Along-track stereo images taken by fixed camera system with a good base to- height ratio is preferred for mapping compared to images taken by agile satellites using a single camera. To acquire stereo along the pass with a single camera system, the satellite body has to be tilted with high oblique angles. Figure 2 shows the strong influence of the sun elevation to the object identification. With a sun elevation of 41° it is difficult to identify the streets located in the building shadows. The building roofs are still in sun shine, so the mapping of the buildings is not a problem, but the building rows are close together, so sometimes it is difficult to decide if within between it is a street or backyards. Not only the sun elevation is important - the third image has just 46° sun elevation against 41° for the second, also the sun azimuth plays a role in relation to the street azimuth like visible in the third image in relation to the second. With the third image the mapping of streets will be quite easier.  Figure 2: IKONOS images with different sun elevation (Source: Reference2) 2.3.On-orbit Calibration of the Sensors Precise On-orbit calibration of sensors during the commissioning phase of the satellite becomes very important with high resolution agile satellites especially when we talk about direct georeferencing. The primary challenge in alignment calibration is the need to estimate the underlying alignment trend for each sensor from a series of precision correction solutions, which measure a combination of orbit, attitude and alignment errors. Correlation between the physical parameters of the camera and the bore sight misalignment parameters between the payload and body is very significant. 2.4. Sensor Model Used for Ground to Image and Image to Ground Transformation The sensor model used is very crucial in deciding the final geometric accuracy of the product. Each image acquisition system produces unique geometric distortions in its raw images, which vary considerably with different factors, mainly the platform, its orbit, and the sensor. The geometric correction methodology has to take care of all these aspects. Different mathematical models used for establishing the relation between ground coordinates and image coordinates are polynomial model, rational function model and rigorous sensor model. Rational function model can be used as a replacement model if ephemeris and sensor parameters are not provided to the user. Rigorous sensor model based on the collinearity equation will reconstruct the imaging process. We have developed a methodology to rectify the satellite imagery with a rigorous sensor model and minimum GCPs. This model is used for mapping from Cartosat-1 and Cartosat-2/2A. 2.4.1 Description of the Sensor Model A rigorous sensor model for georeferencing of a wide class of linear CCD array sensors has been developed. The model is based on the photogrammetric collinearity equations. The information about the satellite ephemeris, attitude, look angle, time etc. is extracted from the Ancillary data file (for full-pass) during the pre-processing and stored. With these values, initial fitting of the trajectory is done. As the satellite sensors follow a smooth trajectory, the exterior orientation parameters can be modelled as polynomial function depending on time, including the physical properties of the satellite orbit and attitude as constraints. A generic polynomial model is developed so that by selecting the order of polynomial, it can be adapted for different types of sensors. This option allows the modelling of the sensor position and attitude with 3rd, 2nd or 1st order polynomials. Corrections up to 3rd order coefficients are done for agile satellites like Cartosat-2/2A.Series of transformations to convert a pixel location in to ground co-ordinates is shown in figure 3. Over a long pass, the variation in the attitude angles will not be a bias and therefore, time-dependent coefficients are also updated with GCPs. The model is very flexible and adaptable to a wide class of linear array sensors. Push broom sensors show different geometric characteristics in terms of optical systems, number of CCD lines, imaging mode, and stereoscopy. For each data set, specific information is available in terms of ephemeris, GPS/INS observations, calibration and other internal parameters. Therefore, the model depends on a certain number of parameters that change for each sensor. An advantage of this software is that it can easily integrate new push broom instruments, if the corresponding orbit and sensor parameters are known. The model has been successfully used for the orientation of single CCD lines, synchronous imaging, asynchronous imaging, along-track stereo and across-track stereo images. One of the highlighted features of the sensor model is the requirement of minimum input/control requirements. With this aim, the model was made suitable for different image configurations like full pass image rectification, combined CCD adjustment and multiple camera adjustment. This model is a part of VAPS (Value Added Product Generation System), which handles full-pass data for operational generation of products from different satellites.  Figure 3: Series of transformations to convert a pixel location in the image into ground co-ordinates 2.5. Ground Control Points When the spatial resolution of the camera increases, the capability of topographic data capture also increases. But geometric fidelity may not increase in the same proportion especially due to the difficulty in transferring GCPs into high resolution images. For making ortho-adjustment of high resolution data, it is most important to assure a high precision GCPs identification, interpretation and measurement in terrain and on the image. Variability of viewing angles within an image, if not respected properly, affect the accuracy potential and become very sensitive to the number and distribution of control points. 3.Utilization of Cartosat-1/Cartosat2/2A Data for Mapping For large scale mapping, two major criteria to be considered are capability for topographic data capture and achievable Geometric accuracy. When the resolution of the satellite increases, the capability of topographic data capture also increases. Many of the feature types that are required for 1:10,000 scale mapping can be satisfactorily identified and captured in Cartosat-1,2,and 2A images. This is shown in figure 4.In some cases, features required for larger than 1:10,000 scale mapping (e.g roads and woodland boundaries at 1:2500 scale) could also be captured in all the images. Major exceptions to this are transmission lines, walls, fences and hedges, which are generally impossible to distinguish even in satellite imagery with 0.4m resolution. A combination of panchromatic and multispectral imagery can help to differentiate between hedges and walls. Cartosat-1 is very stable satellite meant for mapping applications. ADRIN is routinely generating DEM and Orthoimage from Cartosat-1 data using in-house developed sensor model and DEM generation methodology. To check the achievable accuracies from Cartosat-1, we have used 5 data sets. Only a single GCP is used for correction in all the cases. Area covered in each data is 30kmX30km. Distributed Check points are identified. GCPs and check points are of 70 cm accurate. Errors obtained for different datasets are plotted in figure5. GCP requirement is minimized by using the ephemeris and attitude data and precise payload geometry in to the rigorous sensor model. It is clear from this figure that Cartosat-1 data is capable of giving 3-4m accuracy just with 1 GCP. DEM and ortho images are generated from these datasets. Color coded DEM generated over a terrain with medium height variation and the corresponding DEM generated from aerial images are shown in figure6. Mapping potential of Cartosat-2/2A imagery was evaluated over two areas where Cartosat-1 accuracy evaluation was done. Over these areas, single strip images were available. Therefore, heights of individual points are used for computing the planimetric accuracy using the rigorous sensor model. RMS errors after correcting with 2 GCPs are shown in the figure 7. Information content in Cartosat-2/2A is very high and therefore it is suitable for mapping at scales better than 1:10,000. Figure 8 shows DEM and corresponding orthoimage generated from multi-view images of Cartosat-2A. The national mapping accuracy standards in Great Britian indicate RMSE of 1.1m for 1:2500 scale data and 3.4m for 1:10,000 scale data. It is clear from the accuracies and information content that Cartosat-1 is suitable for 1:10,000 scale mapping. The advantage of Cartosat-1 satellite over other two is the stability. Satellite is not changing its view while imaging and image is not acquired in very high oblique look angles. This is an important criteria for mapping. When taken together the results for the feature capture and the geometric accuracy indicate that Cartosat-2/2A shows potential as a data source for 1:10 000 scale mapping at the current specification, and could be used to derive topographic data up to scales as large as 1:7000.  Figure 4: Feature detectability from Cartosat-1 and cartosat-2A images at 1:10,000 scale Conclusions
 Figure 5: Achievable accuracies from Cartosat-1  Figure 6: DEM generated from Cartosat-1 and original reference DEM References
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