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Sentinel-2 Band Pan-Sharpening

Deploy the Sentinel-2 Band Enhancement model using Flask and Leaflet

Introduction

This is a state of the art Convolutional Neural Network algorithm to derive higher resolution images from existing lower resolution images using Sentinel-2 datasets as input. The code is adapted from https://github.com/lanha/DSen2 and is an extension for GUI interface and model deployment using Flask

Input: AOI

Output: Sentinel-2 Bands at 10m

Requirements

This example requires the Ubuntu 16.

• Tensorflow 2 GPU
• Python 3.7

Ideal EC2 Instances

• g4dn.4xlarge
• p2.xlarge

Ideal System Config

• 64 GB Mem
• 12 GB GPU

Instructions

The applications will run through Flask and the processing time depends on Network Speed and GPU/RAM

• Clone the repo
• Move Sentinel_2A_PS to home directory (/home/ubuntu)
• mkdir -p /home/ubuntu/digisat
• Move the other 3 files from repo to digisat Moveinit.shto/home/ubuntu/`
• Give permissions to init.sh using sudo chmod 755 init.sh
• Run ./init.sh

The applications should be hosted at http://<your_ip>:5000