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Spectral-spatial classification of hyperspectral data based on a stochastic minimum spanning forest approach.

Kévin Bernard¹, Yuliya Tarabalka, Jesús Angulo

¹University of Iceland, Reykjavik, Iceland.

IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society

|November 17, 2011

Summary

A novel supervised hyperspectral data classification method uses stochastic minimum spanning forests (MSF). This approach aggregates multiple MSF classifications for improved accuracy on diverse airborne image datasets.

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Area of Science:

Remote Sensing
Computer Vision
Machine Learning

Background:

Hyperspectral data classification is crucial for analyzing Earth's surface.
Existing methods face challenges with spatial-spectral information integration.
Supervised classification requires robust feature extraction and aggregation strategies.

Purpose of the Study:

To introduce a new supervised classification method for hyperspectral data.
To leverage stochastic minimum spanning forests (MSF) for improved classification accuracy.
To evaluate the method's performance across varied hyperspectral image datasets.

Main Methods:

Pixelwise classification followed by marker map generation.
Construction of multiple minimum spanning forests (MSF) from marker maps.
Aggregation of MSF results using a maximum vote decision rule.

Main Results:

The proposed MSF method demonstrates competitive performance on three hyperspectral airborne image datasets.
Experimental analysis investigates the impact of marker count and realization number (M).
Comparison with pixelwise and spectral-spatial techniques highlights the method's effectiveness.

Conclusions:

The stochastic minimum spanning forest approach offers a promising new direction for hyperspectral image classification.
The method shows adaptability to different image resolutions and contexts.
Further research can explore optimizing MSF parameters for enhanced performance.