DSM Generation Using High Resolution Satellite Imagery: Dehradun City Sharma Piyush1, Siddiqui Asfa2, Jain Saloni3, P. Krishna Bhargavi 3, Kumar Dheeraj1 1
Indian School of Mines, Dhanbad;
[email protected] Indian Institute of Remote Sensing, Dehradun;
[email protected] 3 National institute of Technology, Warangal;
[email protected],
[email protected] 2
ABSTRACT The High Resolution Satellite Imagery (HRSI) systems, such as IKONOS have initiated a new era of Earth observation and digital mapping. This high resolution allows for accurate and reliable extraction and characterization of even more details of the earth surface. Digital Surface Models (DSMs) contain information about topographic surfaces and also about other objects higher than the surrounding topographic surface such as buildings, trees etc. and are ideal for analyzing urban and rural landscape. DSMs have many applications in urban and regional development such as Wind Modeling, Solar Radiation calculation, agriculture etc. In this study, DSM of Dehradun city is generated from IKONOS stereo imagery. The output DSMs are compared in context of identifying urban features. A qualitative and quantitative accuracy assessment of the generated DSM is done and measured in terms of planimetric & vertical shift. Key Words: DSM, High resolution satellite imagery
1. Introduction Digital Surface Models (DSMs) have large relevance in many engineering, land planning and environmental applications for a long time. At present, the data required for the DSMs generation can be acquired by several sensors/techniques, among which airborne LiDAR, aerial photogrammetry, optical and radar space borne sensors play the major role. In this respect, the availability of new high resolution optical space borne sensors offers new interesting potential for DSMs generation. High spatial resolution enables the discrimination of fine details like building and individual trees. The DSM extracted from the high resolution stereo images have several advantages over the conventional methods such as comparatively low cost (in comparison with aerial imagery), speed of
data acquisition is fast and processing and relaxed logistic requirements which is quite important for the areas where the organization of aerial flights can be difficult for variety of reasons. Due to development of high resolution and good radiometric quality of recent era of satellite imageries, it seems possible to extract DSMs comparable to middle scale aerial products; It has to be underlined that the DSM accuracy level is strictly related both to the quality of the stereo image orientation and to the effectiveness of the image matching strategy. Two different types of orientation models are usually adopted, rigorous model and the generalized sensor model. The former is based on linking the image and GCPs through collinear equation (Crespi et al. 2011) and the latter is
based on the rational polynomial function (RPC), which links the image and terrain points based on the rational polynomial coefficients (Capalado et al.2012). For matching, several strategies have been developed in the recent years, but widely it can classified into two types: Area based matching and the Feature based matching (Nascetti et al. 2009). The procedure for quantifying the accuracy of the generated DSM involves examination of the horizontal and vertical accuracy (Butler et al. 1998). The DSM vertical accuracy could be estimated by computing the height differences between independent height differences of the RPC computed check points and planimetrically corresponding DSM points.
DSM, as IKONOS is a having high spatial resolution 1 m in PAN and 4 m in MSS band. Ground Control Point (GCP) was collected through the DGPS survey. Base station was established over the roof of URSD department building at IIRS, Dehradun as the coordinates of the selected point was already known. 8 GCP’s were collected in such a way that they cover the maximum area of the imagery and is well distributed.
The study focusses on extracting the digital surface model from high resolution satellite stereo pair imagery and also quantifying the accuracy of the extracted DSM in terms of height and planimetric positions. 2. Study Area and Data used Dehradun is the capital city of Uttarakhand and has also been added to the Smart city planning list recently proposed by Govt. of India. The result is shown in Figure 1. Municipal Corporation of Dehradun divides the city into 60 wards and the area of the city under the Municipal Corporation is 40.72 km2.The population growth of the city is 39.73 % and 26.67 % for the years 2001 and 2011 respectively. Dehradun is located between 30° 15’ 58” N to 30° 24’ 16” N latitude and 77° 58’ 56” E to78° 06’ 05” E longitude. It is located at an altitude of 640 meters above sea level and is bordered by Rispana River and Bindal River from eastern and western part respectively. Data has been collected through various means such as field survey, satellite imagery and other web sources. IKONOS stereo pair imagery of the year 2010 was used for the generation of
Figure 1. Study area Technical specification of the data is given below in Table 1. 3. Methodology and Frame Work The presnt work followed the classical photogrammetric workflow available in ERDAS Leica Photogrammetric Suite (LPS) environment (ERDAS LPS, 2014) for the processing of the stereo images and DSM generation. The flow chart of the process is shown in figure 3. The processing was applied separately to each dataset described in flow chart. From the orientation point of view, the geometric model for space borne push-broom sensors based on RPC was used.
Table 1. Technical Information about the IKONOS stereo imagery TECHNICAL DATA 12pm (panchromatic)
1.
Pixel Size
2.
Ground resolution
3.
4.
Multispectral Bands Blue(4m) Green(4m) Red(4m) NIR(4m) Orbit Height
0.45 to 0.52 pm 0.52 to 0.60 pm 0.63 to 0.69 pm 0.63 to 0.69 pm 680 km
5.
Scene Coverage
11 krn by 11 krn
6.
Stereo Mode
7.
Revisit Rate
In-track and or cross-track stereo 3 days
0.82 at nadir (panchromatic)
After importing the images with their RPC and metadata information and generating the pyramids, common tie points in two or more images were measured in order to ensure the relative orientation between the two images of the same stereo pair and between different stereo pairs that overlaps along or across the flight direction. The aim was to link the images and get a stable block; therefore a minimum of 8 tie points for each pair were measured manually by the operator. The general rule for tie point measurement is to select points on well-defined and fixed/stable features on the terrain (i.e. crossing lines, road signs, etc.,) and get a homogeneous distribution in the images scenes In total a minimum of 20 points for Dehradun City project were generated. Automatic tie point detection modules available in commercial software packages were tested as well, but none of them did produce accurate and reliable results, due to evident mismatches and point extraction on non-fixed or moving objects, like shadows and cars.
Figure 2. Tie points The rigorous photogrammetric processing for the orientation of the images require ground control information to orient the block in a given absolute ground system. In Figure 2 Areo Triangulation report is attached which show the error of 0.15 pixels which is acceptable. The DSM was generated in the ERDAS LPS module eATE (enhanced Automatic Terrain Extraction) with stereo image matching, which aims at finding dense and robust correspondences between stereo images and estimate their 3D coordinates in object space. The matching procedure in eATE is pyramid based, i.e. it starts from a high image pyramid and terrain range is initialized with a global DEM generated with 3-second SRTM DEM; at each pyramid level, terrain range updated from matches on higher pyramid is used to limit search range at current pyramid, and matching results from current pyramid will be used to update terrain range at next lower pyramid. In this way, the ambiguity of terrain variation is reduced at each pyramid level and search range should be reduced as well and converge to a small value, which is a function of terrain slope, accuracy, and pixel size (Xu et al., 2008). Seed points were not added manually as mass point to help the matching and improve the DSM. Taking into account the ground spatial resolution of the input images, the DSMs were generated with 1m, 2.5m, 5 m and 10m grid spacing. The
resultant of 5m grid spacing was chosen and showed best results (about 5 times the pixel size of the panchromatic channels). Block file generation
Add stereo Pair
Images Registration (R.P.C) 1. Interior orientation 2. Exterior orientation
Registered Image
Data Collection
Tie point Generation
G.C.Ps collected through Field Survey
Processing of GCP points
Aerial Triangulation using GCP and Tie points
eATE tool
DSM Generation
Figure 3. Methodology 4. Results and Discussion The extracted DSM is shown in figure 4. The plan metric positional accuracy was assessed using High resolution satellite imagery.
4.1 Accuracy assessment of the extracted DSM The planimetric positional accuracy was assessed through the ground control points collected through the field survey. The vertical profile were examined by calculating the difference between building heights observed using ground surveying techniques and those computed from the extracted DSM from IKONOS stereo image. Table 3 lists the testing results. Results show that the differences in the easting and northing coordinates of checkpoints have a RMSE of 0.95 and 0.15 per pixel, respectively. Table 4 shows that the differences in building heights between surveyed values and the ones extracted using the IKONOS model have difference or error of 4.828 m in urban areas while 1.82 m in the open areas, shown in Table 4. It should be noted that these building heights represent a relative measure for the extracted elevations as they were computed by subtracting two elevations at the building roof and base. The procedure for quantifying the vertical accuracy could be estimated by computing the height differences between independent height differences of the GCP and planimetrically corresponding DSM points. RMSE error of control point and check point is shown in Table 3. Table 3. Accuracy assessment for the plannimetric point
Figure 4. Digital surface Model
Table 4. Results from the comparison between generated DSM and Block file Generated by using RPC Stereo pair IKONOS
Study area
Mean (In meters)
Urban area
4.828
Open area
1.82
5. Conclusion A Digital Surface Model (DSM) can be prepared from IKONOS Stereo pair through the use of automatic image matching. The process of DSM generation using satellite imagery is faster and saves time and cost which is less than the conventional traditional aerial photogrammetry methods. RPC 1 model was used using 8 control points and the results showed very good accuracy in all the three dimensions. The mean deviation in zdirection is 4.828 m in urban areas and 1.82 m in open areas which can be improved using higher resolution satellite imageries like GeoEye-1, World View 2, etc. 6. Acknowledgement This work was supported by Dr. Poonam S. Tiwari and Dr. Hina Pande of IIRS, Dehradun and gave their continuous support and guidance in completing the study. References Crespi M., Fratarcangeli F., Giannone F., Pieralice F..Overview on models for high resolution satellites imagery orientation.Geospatial Technology for the earth sciences data,2009. J.B. Butler, S.N. Lane and J.H. Chandler. Assessmnet of DSM quality for characterizing surface roughness using close range digital photogrammetry.Photogram Rec. vol 16, 1998.
Manuel Angel Aguilar, Maria del Mar Saldaria and Fernando Aguilar.Generation and Quality Assessment of Stereo Extraxted DSm from GeoEye-1 and Worldview-2 Imagery. IEEE Transactions on Geosciences and Remote sensing, 2013. Nascetti A. A stereo image matching strategy based on corner detection and least suares refinement: algorithm implementation in IDL development environment and testing over high resolution satellite imagery. Degree Thesis 2009. Paola Capaldo, Mattia Crespi, Francesa Fratarcangeli, Andrea nascetti and Francesa pieralice, DSM generation from high resolution imagery: applications with WorldView-1 and Geoeye-1.Italian journal of Remote Sensig,2012. Ibrahim F. Shaker , Amr Abd-Elrahman , Ahmed K. Abdel-Gawad and Mohamed A. Sherief , Building Extraction from High Resolution Space Images in High Density Residential Areas in the Great Cairo Region, Remote Sens., 3, 781-791; doi:10.3390/rs3040781,2011. Xu, F. and Woodhoue N. and Xu, Z. and Marr, D. and Yang, X. and Wang, Y.,2008. Blunder elimination techniques in adaptive automatic terrain extraction. ISPRS J.,29 [3],21. P.1139 ff,ISPRS Congress, July,Beijing,2008.
