Process SAR & Multispectral

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Process SAR & Multispectral

本课程是 Satellite Imagery, Remote Sensing & Machine Learning 专项课程的一部分

位教师：ansrsource instructors

包含在中

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3个模块

深入了解一个主题并学习基础知识。

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推荐体验

3 小时完成

灵活的计划

自行安排学习进度

3个模块

深入了解一个主题并学习基础知识。

中级等级

推荐体验

3 小时完成

灵活的计划

自行安排学习进度

您将学到什么

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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积累特定领域的专业知识

本课程是 Satellite Imagery, Remote Sensing & Machine Learning 专项课程专项课程的一部分

在注册此课程时，您还会同时注册此专项课程。

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该课程共有3个模块

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.

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.

涵盖的内容

2个视频1篇阅读材料1个作业

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.

涵盖的内容

1个视频1篇阅读材料2个作业

1个视频总计3分钟

Change Detection That Means Something: Comparing Before and After3分钟

1篇阅读材料总计6分钟

Interpreting Multispectral Change: What Counts as Evidence and What Counts as Noise6分钟

2个作业总计25分钟

Hands-on Learning: Detect Flood-Extent Change After a Storm Using a Multispectral Stack15分钟
Practice Quiz: What Does This Change Signal Actually Mean?10分钟

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.

涵盖的内容

2个视频1篇阅读材料2个作业

2个视频总计8分钟

Accuracy Isn’t Optional: How to Know If Your Map Is Trustworthy5分钟
Congratulations and Your Next Step in Geospatial Data Science3分钟

1篇阅读材料总计10分钟

Beginner Accuracy Evaluation: Confusion Matrix Thinking Without the Overwhelm10分钟

2个作业总计35分钟

Graded Assessment: Flood Extent Evidence: Process, Detect, Evaluate20分钟
Hands-on Learning: Evaluate Classification Results for Flood Detection15分钟

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