https://github.com/nitheshgoutham/sentinel-2-data-processing-for-pichavaram-mangrove-forest-using-cnn
Image Processing using CNN
https://github.com/nitheshgoutham/sentinel-2-data-processing-for-pichavaram-mangrove-forest-using-cnn
cnn cnn-classification cnn-keras data deep-learning matplotlib ploty python seaborn-python visualization
Last synced: about 2 months ago
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Image Processing using CNN
- Host: GitHub
- URL: https://github.com/nitheshgoutham/sentinel-2-data-processing-for-pichavaram-mangrove-forest-using-cnn
- Owner: NitheshGoutham
- Created: 2022-05-10T05:26:00.000Z (about 3 years ago)
- Default Branch: main
- Last Pushed: 2024-11-12T14:16:43.000Z (6 months ago)
- Last Synced: 2025-01-26T11:08:13.208Z (4 months ago)
- Topics: cnn, cnn-classification, cnn-keras, data, deep-learning, matplotlib, ploty, python, seaborn-python, visualization
- Language: Jupyter Notebook
- Homepage:
- Size: 12.9 MB
- Stars: 0
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Sentinel-2 Data Processing for Pichavaram Mangrove Forest Using CNN
# Introduction
The project involves building a solution to analyze satellite imagery data for environmental monitoring. Utilizes Sentinel-2 satellite data to study the Pichavaram Mangrove Forest. Applies Convolutional Neural Networks (CNNs) to process, classify, and identify features in the satellite images.
# Domain: Environmental Monitoring
# Skills Takeaway
Image processing, Remote sensing, Deep learning (CNNs), Environmental data analysis, Data visualization.
# Overview of Data Processing
# Data Collection:
Collects Sentinel-2 satellite imagery focused on the Pichavaram Mangrove Forest area. Imagery is retrieved using satellite data sources
# Data Preprocessing:
Image filtering and resizing to enhance feature extraction. Converts raw data into structured inputs for CNN analysis. Applies necessary preprocessing for noise reduction and image clarity.
# Analysis and Classification Using CNN:
Uses a Convolutional Neural Network to classify and segment key features in the forest region. Processes image data to differentiate between mangrove areas, water bodies, and other land cover types. Creates a model that can help track changes in the mangrove forest over time.
# Technology and Tools
-> Python
-> Sentinel-2 Satellite Data
-> Convolutional Neural Networks (CNNs)
-> Remote sensing tools and APIs
-> Data Visualization (Matplotlib, Seaborn)
# Packages and Libraries
👉 tensorflow
👉 numpy
👉 matplotlib
👉 seaborn
👉 rasterio
👉 sklearn
# Features
# Data Collection:
Uses API to pull specific Sentinel-2 imagery for Pichavaram. Applies geospatial techniques to target the mangrove region.
# Data Storage:
Processed data is stored in structured formats like GeoTIFF for analysis. Classified images and relevant metadata are saved for further studies.
# Data Analysis:
Uses CNNs to process the satellite imagery. Generates visual outputs showing classifications and changes in the forest area. Displays the results and visualizations for trend analysis.
# Contact:
LINKEDIN : https://www.linkedin.com/in/nithesh-goutham-m-0b0514205/
WEBSITE : https://digital-cv-using-streamlit.onrender.com/
EMAIL: [email protected]