Study on remote sensing images is becoming an important target for geological survey activities, mineral exploration, industrial investment, etc. A detailed study and observation about an image is essential to classify every object in the image. This work aims to classify water bodies and road networks from satellite images. Satellite image analysis provides a statistically superior method of sampling that is not possible via conventional ground "grab sampling" methods. Satellite imaging can analyze the entire body of water and identify the areas that need treatment or can be used for planning purposes. However, heavy cloud obstruction or vegetation coverage will impact the ability to view and analyze water or land. Also, remotely sensed images may be severely affected due to different kinds of noise. In order to overcome this, initially, the image is checked for noise and is removed, if necessary using an iterative filtering algorithm for impulse noise removal. The denoising is followed by segmentation after which feature extraction or classification can be performed to identify the objects. After classification, the information is to be stored for future reference. The last step is to store back the information in the form of a QR code. Previously, barcodes were used for storing such information. However with the advent of smartphone technology, people are always connected to the internet and the new technology of QR codes is more suitable for this purpose. The information can be used f or various purposes like geographical surveys, mapping of regions and can also be used to find out the author or the person who has processed the image for any future clarifications.
M. Sarma, Somanath, A., and K. Raghesh Krishnan, “Processing and Interpreting Water bodies and Road Networks from Satellite Images and storing them as QR Codes”, International Journal of Scientific & Engineering Research, vol. 5, no. 5, pp. 1285 – 1291, 2014.