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A Novel Unsupervised Segmentation Quality Evaluation Method for Remote Sensing Images.

Han Gao^1,2, Yunwei Tang³, Linhai Jing⁴

¹Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China. gaohan@radi.ac.cn.

Sensors (Basel, Switzerland)

|October 25, 2017

Summary

This summary is machine-generated.

This study introduces a new unsupervised method for evaluating remote sensing image segmentation quality. It uses spectral and spatial features to reliably assess segmentation without ground truth data.

Keywords:

high spatial resolution remote sensing image segmentation spatial stratified heterogeneity statistical features unsupervised segmentation evaluation

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Area of Science:

Remote Sensing
Geographic Information Systems (GIS)
Image Analysis

Background:

Segmentation is crucial for Geographic Object-Based Image Analysis (GEOBIA) using high-resolution remote sensing imagery.
Unsupervised evaluation of segmentation is vital for algorithm comparison and parameter optimization, but faces challenges in indicator design and feature representation.
Existing unsupervised methods struggle with effective indicator design and limited feature representation.

Purpose of the Study:

To propose a novel unsupervised evaluation method for quantitatively measuring remote sensing image segmentation quality.
To overcome limitations of current unsupervised evaluation strategies, particularly in feature representation and indicator design.
To provide a reliable and effective tool for assessing segmentation performance without ground truth data.

Main Methods:

Extracting and integrating multiple spectral and spatial features for improved ground object representation.
Designing indicators for spatial stratified heterogeneity and spatial autocorrelation to assess segment properties.
Combining indicators into a global assessment metric using a Mahalanobis distance-based strategy to handle trade-offs.

Main Results:

The proposed method was tested on two segmentation algorithms and three remote sensing images.
Performance was compared against two existing unsupervised methods and one supervised method.
Results demonstrated the effectiveness, reliability, and superiority of the proposed unsupervised evaluation method.

Conclusions:

The novel unsupervised method effectively quantifies segmentation quality in high-resolution remote sensing images.
Integrating spectral and spatial features enhances the representation of ground objects for evaluation.
The method offers a reliable alternative to supervised evaluation, improving segmentation algorithm assessment and parameter selection.