ArcGIS Desktop Supervised Classification problem

ArcGIS Desktop Supervised Classification: Getting Real Results (Without the Headaches)

So, you’re diving into supervised classification in ArcGIS Desktop? Smart move! It’s a fantastic way to pull meaningful information from satellite and aerial images, letting you map everything from forests to urban sprawl. Think of it as teaching your computer to “see” the world the way you do. But let’s be honest, it can also be a bit of a headache if you don’t watch out for some common pitfalls. I’ve been there, wrestling with misclassifications and head-scratching errors, so I’m here to share some hard-earned wisdom.

Garbage In, Garbage Out: The Training Data Trap

Seriously, your training data is everything. It’s like showing the computer examples of what each land cover type looks like. If those examples are bad, your results will be… well, also bad. Imagine trying to teach someone what a dog is, but only showing them pictures of Chihuahuas – they’d have a pretty skewed idea, right?

One big problem is spectral confusion. Sometimes, different things just look the same to the computer. I remember one project where bare soil kept getting flagged as buildings. Frustrating! Here’s how to dodge that bullet:

• Quantity matters: Don’t skimp on training samples. Thirty per class is a starting point, but the more complex your area, the more you’ll need. Think of it as giving the computer a well-rounded education.
• Spread the love: Don’t just sample one little corner of your image. Get samples from all over to capture the variations within each class.
• Trust, but verify: Use ground truth data (actual on-the-ground observations), high-res imagery, or existing maps to double-check your samples. Are you sure that’s a healthy forest and not a dying one?
• Are they really different? ArcGIS has tools to check if your classes are spectrally separable. If they’re mushed together, consider merging them or refining your samples.

Choosing Your Weapon: Classifier Selection and Taming the Parameters

ArcGIS gives you a few different classification algorithms to choose from, like Maximum Likelihood, Support Vector Machine (SVM), and Random Forest. Each one has its own quirks. Maximum Likelihood is like the old reliable – simple, but not always the smartest. SVM is a bit more sophisticated and can handle complex data, but it’s also trickier to set up. And Random Forest? Think of it as a team of decision trees working together – pretty robust and generally easy to use.

The thing is, whatever you pick, you’ll need to tweak the settings. These settings are called parameters, and they can make a huge difference. I spent way too long on one project before realizing I had the SVM parameters completely wrong! Experiment, validate, and don’t be afraid to RTFM (read the fine manual).

Cleaning Up the Mess: Data Pre-processing

Before you even get to classification, make sure your data is clean. Geometric errors (where the image is warped) and atmospheric effects (haze, etc.) can throw everything off.

• Georeferencing: Make sure your image lines up with the real world. Use ground control points and a good transformation method.
• Atmospheric Correction: Get rid of that haze! Tools like FLAASH or QUAC can help.

The Finishing Touches: Post-Classification Refinement

Okay, you’ve classified your image. Congratulations! But chances are, it’s not perfect. There will be some stray pixels and weird artifacts. That’s where post-classification refinement comes in.

• Majority Filtering: Smooth things out by replacing lone pixels with whatever’s most common around them.
• Region Grouping: Group pixels of the same class together. Then, get rid of those tiny, weirdly shaped regions.
• Manual Editing: Sometimes, you just have to fix things by hand. It’s tedious, but it can be worth it for critical areas.

How Good Is It, Really? Accuracy Assessment

Don’t just assume your map is perfect. You need to check its accuracy. This means comparing it to independent reference data.

• Confusion Matrix: This table breaks down where you made mistakes. It tells you which classes are getting confused with each other.
• Accuracy Metrics: Overall accuracy, user’s accuracy, and producer’s accuracy are all important metrics to look at.
• Kappa Coefficient: This is a statistical measure of agreement that takes into account the possibility of random chance.

Aim for at least 85% overall accuracy and a Kappa coefficient of 0.8 or higher. If you’re not there, go back and refine your process.

The Bottom Line

Supervised classification in ArcGIS Desktop is an awesome tool, but it’s not magic. It takes effort, attention to detail, and a willingness to learn from your mistakes. By avoiding these common pitfalls, you’ll be well on your way to creating accurate and informative maps. Good luck, and happy classifying!