ISRO - Redundant Region Classification for Satellite Image Compression
ML model to classify uninformative regions in satellite images, enabling intelligent data compression and prioritized transmission.
Project Snapshot
Role
Data Engineer Research Intern
Organization
ISRO - National Remote Sensing Centre (NRSC), Hyderabad
Timeline
February – June 2025
Status
Research / POC
Deployment
POC / Research
Tech Stack
Impact & Results
- Demonstrated significant reduction in effective data volume for transmission
- Prototype integrated into compression pipeline
A machine learning proof-of-concept to classify uninformative regions in massive satellite images, enabling intelligent data compression and prioritized transmission for ISRO NRSC.
The Problem
Satellite images from ISRO are massive, often multiple gigabytes each. Large portions of these images (such as clouds, dark areas, or open ocean) carry no useful information. Classifying these redundant regions would enable intelligent compression of large image archives and optimize data transmission.
The Solution
An ML-based classification pipeline that identifies redundant image patches, allowing uninformative sections to be compressed aggressively without losing scientifically useful data.
Architecture & Implementation
Modeling: A CNN-based patch classifier model to evaluate and categorize image patches as informative vs. redundant.
Pipeline Integration: Integrated the classification model into a prototype image compression pipeline.
Data Used: Multi-gigabyte satellite imagery from ISRO NRSC.
Tech Stack: PyTorch, TensorFlow, OpenCV, Python, NumPy.
Challenges & Solutions
Dataset Creation: Creating a representative labeled dataset of 'redundant' vs 'informative' regions from scratch.
Inference Speed: Optimizing the CNN model for high-speed inference to keep up with the data pipeline.
Resource Constraints: Training models in an air-gapped, resource-constrained environment.
Key Takeaways
Applied Convolutional Neural Networks for vision tasks.
Handling and preprocessing massive spatial data formats.
Optimizing PyTorch models for throughput.