A Novel Unsupervised Segmentation Quality Evaluation Method for Remote Sensing Images

Abstract

Abstract:The segmentation of a high spatial resolution remote sensing image is a critical step in geographic object-based image analysis (GEOBIA). Evaluating the performance of segmentation without ground truth data, i.e., unsupervised evaluation, is important for the comparison of segmentation algorithms and the automatic selection of optimal parameters. This unsupervised strategy currently faces several challenges in practice, such as difficulties in designing effective indicators and limitations of the spectral values in the feature representation. This study proposes a novel unsupervised evaluation method to quantitatively measure the quality of segmentation results to overcome these problems. In this method, multiple spectral and spatial features of images are first extracted simultaneously and then integrated into a feature set to improve the quality of the feature representation of ground objects. The indicators designed for spatial stratified heterogeneity and spatial autocorrelation are included to estimate the properties of the segments in this integrated feature set. These two indicators are then combined into a global assessment metric as the final quality score. The trade-offs of the combined indicators are accounted for using a strategy based on the Mahalanobis distance, which can be exhibited geometrically. The method is tested on two segmentation algorithms and three testing images. The proposed method is compared with two existing unsupervised methods and a supervised method to confirm its capabilities. Through comparison and visual analysis, the results verified the effectiveness of the proposed method and demonstrated the reliability and improvements of this method with respect to other methods.

Normalization in Unsupervised Segmentation Parameter Optimization: A Solution Based on Local Regression Trend Analysis

“…SSPO methods have been criticized for being cost-ineffective since they operate on a trial-and-error manner (for visual interpretation) or require reference segments to be digitized (for quantitative analysis), and also for being susceptible to the subjectivity of the user. To treat this issue, unsupervised segmentation parameter optimization (USPO) methods have been developed and are particularly important in the context of increasing data loads and automation purposes [14,[18][19][20][21][22][23][24][25]. To identify optimal segmentation parameters, USPO procedures usually employ a combination of geospatial metrics that describe spectral heterogeneity between and within image segments [9,[26][27][28].…”

Species-Level Vegetation Mapping in a Himalayan Treeline Ecotone Using Unmanned Aerial System (UAS) Imagery

“…However, for achieving usable accuracies using GEOBIA substantial visual interpretation of objects is required to optimize segmentation parameters, and this can be a challenge in practical applications of GEOBIA over large areas. Therefore, among the possibilities for GEOBIA technique refinement, there is scope for developing algorithms that semi-automatically evaluate the outputs of multiple segmentations and are able to determine the most optimal segmentation parameters for users with limited human interpretation e.g., Reference [60].…”

An Alarm System for Segmentation Algorithm Based on Shape Model

“…Also the registration based approach can hardly be transferred between datasets or modalities. [4] and [7] used unsupervised methods to estimate the segmentation quality using geometrical and other features. However their application in medical settings is not clear.…”