# Map-guided Hyperspectral Image Superpixel Segmentation Using Proportion Maps

## TL;DR

This paper introduces a novel map-guided superpixel segmentation method for hyperspectral images, combining a hyperspectral-optimized SLIC algorithm with semi-supervised topic modeling to improve segmentation quality.

## Contribution

It presents a new hyperspectral-specific superpixel segmentation approach that integrates map guidance and semi-supervised topic modeling, outperforming existing methods.

## Key findings

- Outperforms existing hyperspectral superpixel segmentation methods
- Effective use of map information improves segmentation accuracy
- Validated on real hyperspectral datasets

## Abstract

A map-guided superpixel segmentation method for hyperspectral imagery is developed and introduced. The proposed approach develops a hyperspectral-appropriate version of the SLIC superpixel segmentation algorithm, leverages map information to guide segmentation, and incorporates the semi-supervised Partial Membership Latent Dirichlet Allocation (sPM-LDA) to obtain a final superpixel segmentation. The proposed method is applied to two real hyperspectral data sets and quantitative cluster validity metrics indicate that the proposed approach outperforms existing hyperspectral superpixel segmentation methods.