Satellite Image Processing and Analysis using Python

Prabakaran Chandran (~prabakaran4)


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Description:

Satellite imagery plays a crucial role in various fields such as agriculture, urban planning, disaster management, environmental monitoring, and more. The availability of high-resolution satellite imagery and advancements in computational capabilities have opened up new opportunities for extracting valuable insights from these vast amounts of data. This proposal aims to develop a comprehensive satellite image processing and analysis system using Python, enabling efficient and accurate interpretation of satellite imagery for diverse applications. Objective: The main objective of this project is to leverage Python's powerful libraries and tools to build a robust system for satellite image processing and analysis. The system will provide researchers, scientists, and organizations with the ability to extract meaningful information from satellite imagery, perform image enhancements, classification, change detection, and generate valuable insights for decision-making. Methodology: 1. Data Acquisition: Gather satellite imagery data from reliable sources such as NASA, ESA, or commercial providers, ensuring high-quality and diverse datasets covering various geographic regions and temporal resolutions. 2. Preprocessing and Image Enhancement: Develop Python scripts to preprocess satellite images, including radiometric calibration, geometric correction, and atmospheric correction. Implement enhancement techniques such as contrast stretching, histogram equalization, and noise reduction to improve image quality and visualization. 3. Feature Extraction and Classification: Utilize Python libraries, such as OpenCV, NumPy, and scikit-learn, to extract relevant features from satellite images. Implement machine learning algorithms, including supervised and unsupervised classification methods, to classify land cover, identify objects of interest, and detect changes over time.

Prerequisites:

Laptops with Python Enviroment

Content URLs:

TBA

Speaker Info:

I am a Data Scientist II at Captain Fresh, a leading company in the seafood supply chain industry, where I apply my skills and knowledge in data science, machine learning, and data engineering to build scalable and impactful solutions for fisheries and marine resource management. I work with the product and engineering teams to design and develop data cube, segmentation, data fusion, and microservices models, using tools such as Python, Pytorch, TensorFlow, Detectron2, and AWS MLOps.

I have a Bachelor of Engineering degree in Instrumentation and Control Engineering from St. Joseph's College Of Engineering, where I specialized in computational intelligence. I started my career as a Trainee Decision Scientist at Mu Sigma, a leading problem-solving company, where I worked on various data-driven business transformation projects for engineering, pharma, and customer intelligence domains, using advanced analytics, statistics, NLP, and computer vision techniques. I also mentored and trained other data scientists, and delivered webinars and seminars on deep learning and data science topics. I am passionate about learning and exploring new ways to leverage AI and data for social and environmental good, and I am an active member of the AI Guild community.

Speaker Links:

https://www.linkedin.com/in/prabakaranchandrantheds/

Section: Data Science, AI & ML
Type: Talks
Target Audience: Intermediate
Last Updated: