Process SAR & Multispectral

Overview

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Process SAR & Multispectral is a short course for learners who want to move beyond viewing satellite imagery and begin producing structured geospatial analysis. Designed for those with basic familiarity with maps and raster imagery, the course introduces practical techniques for interpreting and analyzing satellite data in a disaster-response scenario: estimating flood extent after a major storm. You will first work with Synthetic Aperture Radar (SAR), learning why it is essential when clouds block optical imagery and how speckle filtering can improve interpretability while introducing analytical trade-offs. The course then transitions to multispectral imagery, where you explore change detection across time to identify areas where surface conditions may have shifted after the storm. Finally, you will evaluate whether your results are reliable enough to share by interpreting simple accuracy metrics and identifying limitations in your analysis. Through guided videos, applied exercises, and scenario-based assessments, you will build both technical understanding and analytical judgment—preparing you for more advanced geospatial analysis workflows.

Syllabus

Make SAR Usable: Speckle Filtering for Interpretation

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.

Detect Flood-Extent Change With Multispectral Stacks

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.

Evaluate Classification Accuracy Before You Share Results

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.