Monthly Archive: July 2015


UAV data processing

UAV data processing

This write-up intends to present birds eye view of fully automated and accurate mapping solutions based on ultra-light UAV imagery. We showcase interesting observations in the field of UAV mapping, the steps to analyze the accuracy of the automated processing on several datasets. The software used to process is one of the leading and evolving software in the UAV data processing domain.
The accuracy highly depends on the ground resolution (GSD) of the input imagery. When chosen appropriately this mapping solution can compete with traditional mapping solutions that capture fewer high-resolution images from airplanes and that rely on highly accurate orientation and positioning sensors on board. Due to the advancement of computing practices and processing prowess of computers and careful integration with recent computer vision techniques, the result is robust and fully automatic and can deal with inaccurate position and orientation information which are typically problematic with traditional techniques.
SBL’s geospatial team is one of the first in the region to process such images. Processing of UAV images has its own challenges. SBL used to receive post-processed UAV images along with IMU and GCPs as input. Aerial Triangulation is the first step performed. During this stage Ground Control Point (GCP) and Actual Check Point (ACP) reports has been generated. This is an iterative step till we get desired accuracy. The following will explain in brief some of the critical steps in the processing of UAV data.

  1. The software examines for matching points by analyzing all images. The software used here an improved version of the binary descriptors, which are very powerful to match image points quickly and accurately.
  2. Those matching points as well as estimated values of the image position and orientation provided by the UAV autopilot are used in a bundle block adjustment to reconstruct the exact position and orientation of the camera for every acquired image.
  3. Based on this re-establishment the matching points are corroborated and their 3D coordinates calculated. The geo-reference system is WGS84, in this case, based on GPS measurements from the UAV autopilot during the flight.
  4. Those 3D points are interpolated to form a triangulated irregular network in order to obtain a DEM. At this stage, construction of a dense 3D model increases the spatial resolution of the triangulated data.
  5. This DEM is used to project every image pixel and to calculate the geo-referenced ortho-mosaic. The ortho image will be devoid of positional and terrain displacement inaccuracies.
  6. Picture1
    One of the major application for which UAV images used are for agriculture. UAV images are ideal for small size farms. Plant counts such as corn counting will give an idea of yield from those plants. Plant health monitoring, differentiating species of agricultural farms/plants and plantation estimation are the major task performed for agriculture. Growth stages of the farms can also be monitored using ortho images acquired through UAV process. UAV image processing is also helpful for the site selection for solar farms.
    In case of forestry, UAV images are very helpful in species identification. SBL’s interpreters have identified forest species and enabled the client to map forest land parcels. It is a tool to monitor de-forestation as well as afforestation. Golf courses are another field where UAV data sets are highly useful. Golf course features can be mapped with their actual heights through this process.
    Mining industry is the most benefitted in the usage of UAV technological advancement. As most of the mines are spread over small areas, UAV data acquiring and processing is very cost effective. Along with other data processing, SBL has got the expertise in generating contours and mining related features to very minute levels of detail.

Photogrammetry and Remote sensing

SBL is in the fore front of using the latest of mapping technologies such as Photogrammetry and Remote sensing to cater to the GIS services demands in the world-wide industry. As a leading GIS Services provider SBL has executed several complex Aerial Photogrammetry projects towards the fulfilment of Photogrammetry mapping demands.

The following is a brief introduction to Photogrammetry and Remote Sensing for those who are new with the technology.



Photogrammetry, as its name implies, is a 3-dimensional coordinate measuring technique that uses photographs as the fundamental medium for metrology (or measurement). The fundamental principle used by Photogrammetry is triangulation or more specifically called Aerial Triangulation. By taking photographs from at least two different locations, so-called “lines of sight” can be developed from each camera to points on the object. These lines of sight (sometimes called rays owing to their optical nature) are mathematically intersected to produce the 3-dimensional coordinates of the points of interest.
The expression Photogrammetry was first used by the Prussian architect Albrecht Meydenbauer in 1867 who fashioned some of the earliest topographic maps and elevation drawings. Photogrammetry services in topographic mapping is well established but in recent years the technique has been widely applied in the fields of architecture, industry, engineering, forensic, underwater, medicine, geology and many others for the production of precise 3D data.
Branches of photogrammetry

There are two broad based branches in Photogrammetry

  • Metric Photogrammetry : Deals with the precise measurements and computations on photographs regarding the size, shape, and position of photographic features and/or obtaining other information such as relative locations (coordinates) of features, areas, volumes, These photographs are taken using a metric camera and  is mostly used in the engineering fields e.g. surveying etc
  • Interpretive Photogrammetry: Deals with recognition and identification of the photographic features on a photograph such as shape, size, shadow, pattern etc to add value and intelligence to information seen on the photograph (annotation).,

Remote Sensing

Remote Sensing is a closely aligned technology to Photogrammetry in that it also collects information from imagery. The term is derived from the fact that information about objects and features is collected without coming into contact with them. Where remote sensing differs from Photogrammetry is in the type of information collected, which tends to be based on differences in color, so land use and land cover is one of the primary output of remote sensing processing. Remote sensing was originally conceptualized to exploit the large number of color bands in satellite imagery to create 2D data primarily for GIS. Nowadays remote sensing tools are used with all types of imagery to assist in 2D data collection and derivation, such as slope. Software tools today tend to hold a much wider range of image technologies such as image mosaicing, 3D visualisation, GIS, radar as well as softcopy Photogrammetry.

Key concepts:

  • Spatial resolution.
  • Radiometric resolution. 
  • Spectral resolution. 
  • Temporal resolution
    • Spatial resolution describes the ability of a sensor to identify the smallest size detail of a pattern on an image. In other words, the distance between distinguishable patterns or objects in an image that can be separated from each other and is often expressed in meters.
    • Spectral resolution is the sensitivity of a sensor to respond to a specific frequency range (mostly for satellite and airborne sensors). The frequency ranges covered often include not only visible light but also non-visible light and electromagnetic radiation. Objects on the ground can be identified by the different wavelengths reflected (interpreted as different colours) but the sensor used must be able to detect these wavelengths in order to see these features.
    • Radiometric resolution is often called contrast. It describes the ability of the sensor to measure the signal strength (acoustic reflectance) or brightness of objects. The more sensitive a sensor is to the reflectance of an object as compared to its surroundings, the smaller an object that can be detected and identified.
    • Temporal resolution depends on several factors–how long it takes for a satellite to return to (approximately) the same location in space, the swath of the sensor (related to its ‘footprint’), and whether or not the sensor can be directed off-nadir. This is more formally known as the ‘revisit period’

Offshore Services Provider SBL Opens North American Headquarters in South Florida

SBL is proud to announce our expansion into North America with our operations center in Palm Beach Gardens, Florida. As we service our current clients and build new relationships in the US and Canada, we understand the need to provide an immediate line-of-sight view of our capabilities through a local presence. This U.S. operations center will execute all SBL business development, marketing communications, customer service and PR activities in North America.

The SBL USA office is managed by Paul Dudley, VP of North American Business Development and a Florida native with a comprehensive background in global sales, marcom, PR and customer service. With a combined 50 years’ experience in fulfilling sales and marketing requirements for international enterprises, the SBL USA team is working to effectively acquire and deliver business in North America. SBL US(1)