A New Spatio-Spectral Morphological Segmentation For Multi-Spectral Remote-Sensing Images

TL;DR

This paper introduces a novel spatio-spectral segmentation framework for multi-spectral remote sensing images, combining classification-driven stochastic watershed with dimensionality reduction to produce more accurate and reliable image contours.

Contribution

It presents a new segmentation method that integrates spectral classification, factor analysis, and stochastic watershed driven by Monte Carlo simulations, improving contour regularity and reliability.

Findings

More regular and reliable contours than standard watershed.

Effective integration of spectral classification and spatial information.

Enhanced segmentation accuracy in multi-spectral images.

Abstract

A general framework of spatio-spectral segmentation for multi-spectral images is introduced in this paper. The method is based on classification-driven stochastic watershed (WS) by Monte Carlo simulations, and it gives more regular and reliable contours than standard WS. The present approach is decomposed into several sequential steps. First, a dimensionality-reduction stage is performed using the factor-correspondence analysis method. In this context, a new way to select the factor axes (eigenvectors) according to their spatial information is introduced. Then, a spectral classification produces a spectral pre-segmentation of the image. Subsequently, a probability density function (pdf) of contours containing spatial and spectral information is estimated by simulation using a stochastic WS approach driven by the spectral classification. The pdf of the contours is finally segmented by a WS controlled by markers from a regularization of the initial classification.