Waterbody Detection from Satellite SAT Images using Deep Learning

Presenter: Chao Huang Lin (Computer Science - Oral Presentations)
Faculty Mentor: Razvan Andonie
Abstract: Nowadays, Synthetic Aperture Radar (SAR) images have been widely used in the industry and the scientific community for different remote sensing applications. The main advantage of SAR technology is that it can acquire images from night time since it does not require sunlight. Additionally, it can penetrate the cloud which can capture images where the traditional optical sensor is limited. One of the remarkable applications of SAR image is water detection since the water body reflects off all the energy from the radar so it appears in a SAR image as dark pixels. The traditional way to mark out water from SAR image is using the threshold method where each pixel is classified as water when its value is below a certain threshold. This method works fine in a plain rural area but the complex features of urban areas make it more challenging, for example, highways and buildings shadows can be easily misclassified as water. To solve this problem, we propose a deep learning solution to detect water from SAR image. The implemented convolutional neural network will no only identify water by the intensity of each pixel, it also learns the spatial information of neighborhood pixels. To train the network we used so2sat dataset which is processed from Sentinel-1 satellite SAR images. After training, we tested the neural network in many real SAR images and it gave us promising results that are more clear and better than the thresholding method. Moreover, to speed up the proposed solution, we were able to update it with the convolutional sliding windows.
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[@silpalatha5039]

Please Send the link of waterbody sir

[@silpalatha5039]

Is this implmeted in matlab or python sir

[@aparnar2997]

Hii.Did you download the whole 51.5GB dataset? Or is there a way to download only the water classes?

[@Atranhago]

its possible donwload only the dataset that you need (the place)

[@Rey_ct]

@@Atranhago how its possible?