Curriculum Overview
The Satellite Image Processing program at Indian Institute Of Remote Sensing is structured over eight semesters, each carefully designed to build upon previous knowledge and introduce increasingly complex concepts. The curriculum combines foundational science courses, core engineering principles, departmental electives, and specialized project work to ensure a well-rounded education.
First Year Courses
| Course Code | Course Title | Credit Structure (L-T-P-C) | Prerequisites |
|---|---|---|---|
| PHYS101 | Physics of Electromagnetic Waves | 3-1-0-4 | None |
| MATH101 | Mathematics for Engineers | 4-0-0-4 | None |
| CS101 | Introduction to Programming | 2-0-2-3 | None |
| ENGR101 | Digital Electronics | 3-1-0-4 | MATH101 |
| CHEM101 | Chemistry for Engineers | 3-1-0-4 | None |
| PHYS102 | Introduction to Optics and Lasers | 3-1-0-4 | PHYS101 |
Second Year Courses
| Course Code | Course Title | Credit Structure (L-T-P-C) | Prerequisites |
|---|---|---|---|
| MATH201 | Probability and Statistics | 3-0-0-3 | MATH101 |
| CS201 | Data Structures and Algorithms | 3-0-2-5 | CS101 |
| ENGR201 | Signal Processing Fundamentals | 3-1-0-4 | MATH101, CS101 |
| PHYS201 | Optical Instruments and Systems | 3-1-0-4 | PHYS101 |
| GEOS201 | Introduction to Remote Sensing | 3-1-0-4 | MATH101, PHYS101 |
| ENGR202 | Image Processing Fundamentals | 3-1-0-4 | CS101, ENGR201 |
| MATH202 | Linear Algebra and Differential Equations | 4-0-0-4 | MATH101 |
Third Year Courses
| Course Code | Course Title | Credit Structure (L-T-P-C) | Prerequisites |
|---|---|---|---|
| ENGR301 | Remote Sensing Sensors and Platforms | 3-1-0-4 | GEOS201, ENGR201 |
| CS301 | Machine Learning for Remote Sensing Applications | 3-1-0-4 | CS201, MATH201 |
| GEOS301 | Atmospheric Effects in Satellite Data | 3-1-0-4 | GEOS201 |
| ENGR302 | Image Enhancement Techniques | 3-1-0-4 | ENGR202 |
| CS302 | Geographic Information Systems (GIS) | 3-1-0-4 | GEOS201, CS201 |
| MATH301 | Numerical Methods for Engineers | 3-0-0-3 | MATH101, MATH202 |
Fourth Year Courses
| Course Code | Course Title | Credit Structure (L-T-P-C) | Prerequisites |
|---|---|---|---|
| ENGR401 | Advanced SAR Data Analysis | 3-1-0-4 | ENGR301, ENGR201 |
| CS401 | Deep Learning for Remote Sensing | 3-1-0-4 | CS301 |
| GEOS401 | Hyperspectral Imaging and Data Fusion | 3-1-0-4 | ENGR301, CS302 |
| ENGR402 | Real-Time Data Fusion Techniques | 3-1-0-4 | ENGR302 |
| CS402 | Data Visualization for Geospatial Applications | 3-1-0-4 | CS301, CS302 |
| ENGR403 | Remote Sensing in Urban Environments | 3-1-0-4 | GEOS201, CS302 |
Departmental Electives (Year 3 & 4)
Advanced departmental electives offer students opportunities to specialize in areas of interest. These courses are designed to deepen understanding and prepare students for research or industry roles.
- ENGR501 - Hyperspectral Imaging: Focuses on the acquisition, processing, and interpretation of hyperspectral data. Students learn about spectral signatures, classification techniques, and applications in mineral exploration, agriculture, and environmental monitoring.
- CS501 - AI for Remote Sensing: Explores how artificial intelligence can be applied to satellite image analysis, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformer-based models. Students gain hands-on experience with tools like TensorFlow and PyTorch.
- GEOS501 - Coastal Monitoring: Covers the use of satellite imagery for tracking coastal erosion, sea-level rise, and pollution events. The course includes modules on wave modeling, sediment transport, and ecosystem impacts.
- ENGR502 - Disaster Risk Assessment: Examines how satellite data can be used to assess risks associated with natural disasters such as floods, earthquakes, and wildfires. Students learn about early warning systems and emergency response planning.
- CS502 - Cloud Computing for Remote Sensing: Introduces students to cloud-based platforms like AWS, Google Earth Engine, and Microsoft Azure for handling large satellite datasets. Topics include data storage, processing, and visualization techniques.
- GEOS502 - Climate Change Monitoring: Focuses on using satellite data to study climate trends and impacts. Students explore topics such as carbon dioxide levels, temperature variations, and sea ice extent.
- ENGR503 - SAR Signal Processing: Provides an in-depth look at synthetic aperture radar (SAR) signal processing techniques, including interferometry, polarimetry, and speckle reduction. Students work with real SAR data from various missions.
- CS503 - Spatial Data Mining: Teaches students how to extract meaningful patterns from large spatial datasets using data mining algorithms. Applications include urban planning, environmental monitoring, and public health.
Project-Based Learning Framework
The program emphasizes project-based learning as a cornerstone of the educational experience. Students are required to complete both mandatory mini-projects and a final-year thesis or capstone project.
Mini-projects begin in the third year, where students work in small teams on real-world problems related to satellite image processing. These projects are guided by faculty mentors and often involve collaboration with industry partners or government agencies. The scope of these projects ranges from developing classification algorithms for land cover mapping to creating interactive dashboards for flood monitoring.
The final-year thesis project is a significant undertaking that allows students to explore an area of personal interest in depth. Students select their topics in consultation with faculty mentors, and the project typically involves designing, implementing, and evaluating a novel solution or approach to a relevant problem in satellite image processing. The process includes literature review, methodology development, data collection and analysis, and presentation of findings.
Evaluation criteria for projects are designed to assess both technical competence and communication skills. Students must submit detailed reports, present their work to faculty panels, and defend their findings orally. This rigorous evaluation ensures that students develop not only strong analytical abilities but also the ability to convey complex ideas clearly and effectively.