Home Articles Precision product generation using satellite data and GPS based ground control points

Precision product generation using satellite data and GPS based ground control points

S. S Srinivasa Rao
S. S Srinivasa Rao, D. S. Prakasa Rao,
D. S. Pandit, C. K. Rajender & Y. V. N. Krishna Murthy

Regional Remote Sensing
Service Centre
ISRO, Nagpur
[email protected]
[email protected]

Remote Sensing data is being extensively used for natural resources mapping, management, monitoring and developmental planning. The geometric accuracy of the satellite output plays a major role in generating precise thematic maps. Conventionally, the satellite data is georeferenced using ground control points acquired from topographical maps. This process involves establishment of two transformation models i.e., map-to-digitizer model and image-to-map model, Each of these steps is highly interactive and contributes significantly in the precision of the output. In addition to this, the inherent locational accuracy of the topographical maps and the projection induced errors at the macro level also contribute significantly to the total error budget. With the availability of high resolution satellite data, the corresponding large scale reference maps are not available for georefencing of this data. Apart from this, the latest changes in the infrastructure are not reflected in the topographical maps. This has necessitated the search for alternate ways for precision product generation. The Global Positioning System (GPS) provides the well-equipped user an accurate coordinate at any point on the earth surface, at any time, in any weather conditions. A study has been carried out for collecting Ground Control Points using single frequency GPS receivers. The GPS data was processed in differential mode. The satellite data was rectified using these GCP’s and the second order polynomial transformation model. The total error budget achieved using GPS based GCPs was much lower than the Map-based GCPs. The precision product thus generated can be effectively utilized for thematic and cartographic mapping.

In several parts of the country, improper utilization and management of natural resources especially land and water have set in degradation process through loss of soil productivity, decreasing storages in the reservoirs, rising stream beds, frequent floods, water logging, salinisation and desertification. Associated with these problems, increasing demand on the land resources is gradually leading to land scarce. Large areas of the prime agriculture are being lost due to urbanization and other developmental activities. Forests are being intruded for agricultural activities and exposed relentlessly to grazing pressures.

Sectoral developmental efforts of past years with out concern for complementary and other associated resources have caused more harm than benefits. Excessive use of chemical fertilizers, pesticides and insecticides have undoubtedly increased crop production at the cost of fertility of soils. Water pollution through various sources made the problem still worse. In order to optimize and sustain outputs from primary systems to meet the growing demands of the increasing population, developmental plans with integrated approach has been accepted through out the world. This approach helps in optimal management and better utilization of natural resources for improving living conditions of the people.

Remote sensing is a multi disciplinary activity which deals with the inventory, monitoring and assessment of natural resources through the analysis of data obtained by observations from the remote platform. The observations are synoptic, provide repetitive coverage of large areas and the data is quantifiable. Satellite remote sensing which meets the requirements of reliability, cost-effectiveness and timeliness is an ideal tool for generating spatial information for natural resources management.

Global Positioning System (GPS)
Global Positioning System has revolutionized positioning concepts, though it is started primarily as a navigation system. It works on the principle of space resection. It has wide range geodetic, geophysical, navigational marine, military and social applications.

The NAVSTAR GPS (NAVigation Satellite Timing And Ranging Global Positioning System) is a satellite based radio navigation system providing precise three dimensional position, navigation and time information to suitably equipped users every where on a continuous basis. It is primarily a military system with limited access to civilian users. GPS receivers have been developed which observe signals transmitted by satellites and achieve sub-meter accuracy in point positioning and a few centimeters in relative positioning. It has the following advantages over the classical methods.

  • Inter-visibility between points is not required
  • All weather operation
  • Day and night operation
  • Distances up to thousands of kilometers can be measured
  • Fast and economical.

Geometric Distortions in Satellite Images
Remotely sensed images are not maps. Frequently information extracted from remotely sensed images is to be integrated with the map data in Geographical Information Systems (GIS). There are various sources of errors, which degrade the geometric fidelity of the satellite data. The geometric distortion is an error on an image, between the actual image coordinates and the ideal image coordinates, which would be projected theoretically with an ideal sensor and under ideal conditions.

Geometric distortions are classified in to internal distortion resulting from the geometry of the sensor, and external distortions resulting from the attitude of the sensor or the shape of the object. The internal distortions are radial distortion of lens, tangential distortion of lens, error of focal length, tilt of projection plane, non flatness of projection plane, alignment error of CCD array, variation of sampling rate, timing error of sampling and variation of mirror velocity. The external distortions are planimetric error of platform, altitude error of platform, motion of orbital position, altitude of platform, variation of attitude, rotation of the earth, earth curvature, terrain relief and atmospheric refraction.

The geometric correction is undertaken to avoid geometric distortions from a distorted image, and is achieved by establishing the relationship between the image coordinate system and the geographic coordinate system using calibration data of the sensor, measured data of position and attitude, Ground control Points (GCPs), atmospheric conditions etc.

The satellite data recorded at the earth station is corrected to various levels of processing. They are

Level 0 – Uncorrected (raw data)
Level 1 – Radiometrically corrected and geometrically corrected only for earth rotation (browse product)
Level 2 – Both radiometrically and geometrically corrected (standard product)

Need for Precision Products
Definitive orbit and attitude information are used for standard products generation. The overall accuracy of the standard products comes to + 1.5 km. For the digital database generation in GIS environment the most significant aspect lies in the geometric accuracy. The information derived from different sources should have geometric compatibility to establish one-to-one correspondence. This will facilitate overlaying, merging, integrating various information and form the basis for further analysis. The inherent error in the standard product unless corrected will create problems in seamless integration of digital database and in turn generation of precise developmental action plans.
 Table No. 1 Error Budget Analysis in Generating Precision Products

S.No Parameter Digitizer based Scanner based GPS based
1 Inherent map accuracy 12.5 12.5
2 Projection error 2.0 2.0 2.0
3 Map-digitizer transformation error 25.0
4 Image-map transformation error 30.0 15.0
5 Error in the tiling of reference maps 6.0
6 GPS receiver positioning at the GCP 2.0
7 Baseline accuracy 1.0
8 Image-GCP transformation error 12.0
  Total error in meters (Approximate) 65 – 75 35 – 45 15 – 20

Methods of Generating Precision Products The desired accuracy required at 1:50000 scale may be of the order of 1mm of the map units i.e. less than 50 meters on ground. Using precise ground control points, the desired geometric accuracy of the satellite data products can be achieved. The number and distribution of ground control points and the order of transformation model will influence the accuracy of the geometric correction.

The three basic methods adopted for acquiring GCPs are Digitizer based Method, Scanner Based Method and GPS Based Method. Digitizer based method is a two-step approach. At the first level map-digitizer model is established and this will facilitate transformation of digitizer coordinates to map coordinates. At the second level, image-map model is established to facilitate one-to-one correspondence between image and ground coordinates.

Similarly, scanner based method is also a two step approach. At the first level map-gratic model is established and this will facilitate tiling of maps with respect to well defined graticules. At the second level, image-map model is established to facilitate one-to-one correspondence between image and ground coordinates.

In contrast to the above two methods, GPS based method is a single step approach, wherein a transformation model is established between the post processed coordinates of the GCPs and image coordinates.

Study Area & Data used
The study area is located in and around Bhisi, Chimur Taluk, Chandrapur district, Maharashtra. The study area is 70 km away from Nagpur and lies in between 79 0 15′ and 79 0 30′ Eastern longitudes and 20 0 30′ and 20 0 45′ Northern latitudes. The IRS 1C LISS III (100-58) and PAN (100-58-A) data of the corresponding area are used for generating precision products. The corresponding reference maps of 55P/6 on 1:50000 and 1:25000 scale are also used. Single frequency geodetic GPS receivers are used for GPS observations.

The three methods discussed above are used to generate precision products. In the first method 1:25000 reference maps are mounted on the digitizer one at a time and individual map-digitizer models are established. All the graticule intersections are used as reference points and a second order transformation model is established. This serves as a basis for establishing image-map transformation model using well-defined and well-distributed GCPs

In the second method, the reference maps are scanned and tiled with reference to the predefined graticule base using a transformation model. This serves as a basis for establishing image-map transformation model using well defined and well distributed GCPs With the advent of scanner technology, this method has become more popular and widely used globally.

In the method using GPS, the first task will be establishment of a reference point with accurate coordinates. A permanent monument is constructed with well-defined marking on a brass plate. The ornamentation point is observed for a continuous 72 hours using dual frequency geodetic GPS receiver. Using Bernese software, the observations are processed in differential mode using post-processing data. The precise coordinate of the ornamentation point is arrived at with reference to the five permanent stations of WGS 84 datum with long baselines of the order of 2000 kms. The accuracy achieved for this coordinate is of the order of 0.02 ppm.

Around 16 well-defined and well-distributed GCPs are identified on the image as well as on the map. Reconnaissance survey is carried out to identify the local reference station. The study area is around 70 km away from this reference station. A reference point is established in the centre of the study area by taking continuous observations for a period of eight hours in differential mode with reference to monumenation point.

The baseline vector is computed and the absolute coordinates of the reference point are arrived at. At each of the GCP locations, 2-hour GPS observation is carried out. The GPS data is post processed using SKI L1 software in differential mode to arrive at the absolute coordinates of the GCPs.

A transformation model is established between these coordinates and the corresponding image coordinates. Using this model, satellite data is georeferenced.

Error Budget Analysis
The errors involved in each of the above-discussed methods of generating precision products are as follows.

Digitizer based Method

  • Inherent map accuracy
  • Projection error
  • Map-digitizer transformation error
  • Image-map transformation error

Scanner based method

  • Inherent map error
  • Projection error
  • Error in the tiling of reference maps
  • Image-map transformation error

GPS based method

  • GPS receiver positioning at the GCP
  • Projection error
  • Baseline accuracy
  • Image-GCP transformation error

In the study, the IRS 1C LISS III data, with a spatial resolution of 23 meters, is used to generate precision products on 1:50,000 scale. The error budget calculated in the study is presented in the Table No.1

Advantages of GPS based GCPs
From the error budget analysis, it is clear that using GPS based GCP’s will generate precision products with higher accuracy. The GPS based GPC’s will be a direct input in image processing & GIS packages. The GPS will also provide height information for each GCP.

The limitation of non availability of large scale reference maps for generating precision products using high resolution satellite data is easily overcome by using GPS based GCP’s. In addition to this, the latest changes in the infrastructure are not reflected in the reference maps which can be taken care by GPS.

The digitizer-based method is replaced by the scanner-based method of georeferencing satellite data. The scanned reference map and the satellite image can be directly accessed on to the screen side by side. This will improve the identification of GCP’s more precisely.

The GPS technology which is emerged as powerful tool in point positioning is readily adopted in Image processing & GIS techniques for acquiring GCP’s. It will provide precise coordinates of GCP’s. However, the coordinate information will be on WGS-84 datum for latlongs and ellipsoidal-based height measurements. This coordinates has to be transformed into Everest ellipsoid using datum transformation parameters. This task is not within the preview of the present study.


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