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Process SAR & Multispectral

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Coursera

Process SAR & Multispectral

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Gain insight into a topic and learn the fundamentals.
Intermediate level

Recommended experience

3 hours to complete
Flexible schedule
Learn at your own pace
Gain insight into a topic and learn the fundamentals.
Intermediate level

Recommended experience

3 hours to complete
Flexible schedule
Learn at your own pace

What you'll learn

  • Process SAR imagery for flood mapping when clouds block optical data, applying speckle filtering to improve clarity

  • Detect post-disaster changes using multispectral imagery to identify areas where flooding altered surface conditions

  • Evaluate analysis reliability using accuracy metrics and identify limitations before sharing disaster response results

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Recently updated!

April 2026

Assessments

5 assignments¹

AI Graded see disclaimer
Taught in English

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This course is part of the Satellite Imagery, Remote Sensing & Machine Learning Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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There are 3 modules in this course

In this module, you are introduced to SAR as a practical disaster-response data source that remains available even when optical imagery is blocked by clouds. You focus on one core barrier to using SAR confidently: speckle. Rather than treating speckle as a vague “noise problem,” you learn what it looks like, why it occurs, and how filtering changes interpretability. The module is designed to develop judgment: you learn to apply speckle filtering, compare outcomes, and reason about trade-offs because aggressive smoothing can hide meaningful edges while weak filtering may leave the scene unreadable.

What's included

2 videos1 reading1 assignment

In this module, you move from preprocessing to analysis. Using multispectral imagery captured across time, you perform change detection to identify where surface conditions shifted after a storm event. The module emphasizes interpretive reasoning: you learn that a “change map” is not automatically a flood map, and you must think about what the change signal could represent. You also learn how to structure your outputs so you can communicate results clearly, highlighting what changed, where confidence is higher, and what limitations remain.

What's included

1 video1 reading2 assignments

The final module teaches you the habit that makes your work trustworthy: evaluation. You learn that classification outputs can look convincing while still being wrong in critical ways. The module introduces beginner-friendly accuracy evaluation concepts and asks you to make judgment calls: is this accurate enough for the decision at hand, and what would you disclose as limitations? This module ties directly to operational credibility because in real flood response, the cost of being confidently wrong is high.

What's included

2 videos1 reading2 assignments

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¹ Some assignments in this course are AI-graded. For these assignments, your data will be used in accordance with Coursera's Privacy Notice.