5.17.1.A.i. Georeferencing

Raw remote sensing data is without any geographic coordinates, and has distortions, mainly caused by the sensor geometry. Therefore, these can’t be used as such for any quantitative measurements on them. Georeferencing is the conversion of image coordinates to ground coordinates by removing the distortions caused by the sensor geometry.

Georeferencing is a crucial step in processing images gathered from sensors, especially in remote sensing. When images are captured, they do not have accurate geographic coordinates, meaning we cannot pinpoint where they were taken on the Earth's surface. Additionally, the sensors used in capturing these images may cause distortions, resulting in inaccuracies. Therefore, georeferencing involves correcting these issues so that the image can be accurately mapped to real-world locations.

Think of georeferencing like correcting a misaligned map. If you had a paper map that was crumpled and stretched - it wouldn't accurately represent the terrain anymore. Georeferencing smooths out the wrinkles, straightening it so you can accurately pinpoint where you are on that map.

Georeferencing is important to deal with various images, create mosaicking and compare various scenes (e.g., change assessment). It is a process of locating an entity/object in real world coordinates, also called geo-rectification or geo-registration.

Georeferencing allows us to analyze images in a meaningful way. By transforming images to match real-world coordinates, we can create larger images by stitching (mosaicking) together smaller ones, which is helpful for broad area assessments. Additionally, it enables us to make comparisons over time, such as detecting changes in landscapes, urban development, or environmental shifts, by ensuring all images can be aligned to the same coordinate framework.

Imagine you’re piecing together a jigsaw puzzle. Each piece represents a different image. Without georeferencing, those pieces might not fit together or make sense. Once you get the pieces aligned correctly in their geographical space, the full picture reveals itself—much like how georeferencing allows for a complete understanding of an area.

The direction of satellite motion in the orbit and on-board sensors while taking images is not exactly north-south or west-east, respectively. In addition, there is a rotation of the Earth about its own axis while taking the images, so images are not perfect square but they have somewhat skewed shape.

Georeferencing isn't always straightforward; challenges arise from the movement of the satellite and the rotation of Earth. As the satellite moves in its orbit and captures images, these images may not perfectly align with the cardinal directions (north, south, east, and west). Movement can cause skewing, which means images captured may appear distorted rather than square or rectangular. This distortion must be corrected during the georeferencing process to ensure that images can be accurately overlaid with geographic data.

Consider taking a picture with your smartphone while you are moving on a moving train. The image will likely capture some distortion due to the movement, like a blurry background. You’d need to adjust and realign the image to see it clearly and accurately; georeferencing does the same, adjusting those distorted satellite images.

To do georeferencing, the exact locations of several known points, called Ground Control Points (GCPs), are required. These GCPs are normally selected as prominent objects whose geographical locations can be accurately determined either from the topographic maps or GPS survey. A minimum of four control points are required for georeferencing, however, additional control points would help increasing the accuracy of georeferencing.

Ground Control Points (GCPs) serve as critical reference points for georeferencing processes. They are specific locations on the ground with well-known coordinates. By identifying these points in both the image and from reliable geographic sources, we can effectively calibrate the images to match real-world coordinates. While a minimum of four GCPs are necessary for the process, more GCPs result in even greater accuracy, which is essential for reliable data analysis.

Picture a treasure map. The landmarks you recognize (like a big tree or a rock formation) are like GCPs. If you can pinpoint those landmarks accurately both on the map and on the actual terrain, you will have a better chance of finding the treasure. GCPs are essential to navigate and align our images accurately.

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