Introduction

The image segmentation model can be used to extract real-world objects from images, blur backgrounds, create self-driving automobiles, and perform other image processing tasks. The goal of this research is to create a mask that shows floodwater in a given location based on Sentinal-1 (a dual-polarization synthetic-aperture radar (SAR) system) images or features.

Dataset

Our dataset comprises with satellite images taken from the Sentinel-2 satellite. It has 15 stacks of folders with waterbody labels. Each of these folders contains waterbody labels and a fuzzy mask. Note that these masks are derived from a few sources (amplitude, coherence, Sentinel-2, Landsat-8, and OpenStreetMap). They also contain rslc/*.rslc.notopo files, which are RSLCs with the topography pre-removed. Each image is about 10820 x 11361 pixels. Below is an example of our dataset:

• Water: 1

• NON-Water: 0

• unlabeled: 255

Model

The architecture of our proposed MIMONET model is a multi-input, multi-output model. The model has two inputs and two outputs. Each input takes an identical-sized feature image and mask. In our Encoder-Decoder architecture, we do not utilize any pre-trained model weights. However, our augmentation module enhances our model’s performance.