Скачать или смотреть Hyperspectral Image Segmentation

This video gives a plain-English walkthrough of hyperspectral imaging and a fast, dependable way to segment a hyperspectral scene in Python. You’ll see why HSI is powerful—each pixel has a full spectrum—so we separate materials by spectral shape instead of brightness. Then we build a simple pipeline that works on most cubes: apply the bad-band list (BBL), L2-normalize each spectrum, compress with PCA (~10 comps), and cluster with MiniBatchKMeans (K ≈ 4–10). We show the results inline in Spyder (segmentation map, PCA preview, cluster-mean spectra, variance plot) and explain exactly what each tells you.

You’ll learn why segmentation helps—clean, object-like regions, less speckle, and faster downstream analysis—and how it differs from classification: segmentation finds the pieces (usually unsupervised), while classification names the pieces (supervised). In practice, the best workflow is often segment → classify (object-based image analysis) for cleaner maps with less labeling effort. We also cover common pitfalls—keeping bad bands, skipping normalization, or keeping too many/few PCA components—and how to avoid them. Finally, we touch on where this matters most: agriculture, water quality, geology, industrial QA, medical imaging, and cultural heritage.

To access the code use the following link:

https://github.com/mortezmaali/HSI_Se...