Region Merging Considering Within- and Between-Segment Heterogeneity: An Improved Hybrid Remote-Sensing Image Segmentation Method

Yongji Wang,Qingwen Qi,Ying Liu,Jian Yang,Qingyan Meng

doi:10.3390/rs10050781

Abstract

Image segmentation is an important process and a prerequisite for object-based image analysis, but segmenting an image into meaningful geo-objects is a challenging problem. Recently, some scholars have focused on hybrid methods that employ initial segmentation and subsequent region merging since hybrid methods consider both boundary and spatial information. However, the existing merging criteria (MC) only consider the heterogeneity between adjacent segments to calculate the merging cost of adjacent segments, thus limiting the goodness-of-fit between segments and geo-objects because the homogeneity within segments and the heterogeneity between segments should be treated equally. To overcome this limitation, in this paper a hybrid remote-sensing image segmentation method is employed that considers the objective heterogeneity and relative homogeneity (OHRH) for MC during region merging. In this paper, the OHRH method is implemented in five different study areas and then compared to our region merging method using the objective heterogeneity (OH) method, as well as the full lambda-schedule algorithm (FLSA). The unsupervised evaluation indicated that the OHRH method was more accurate than the OH and FLSA methods, and the visual results showed that the OHRH method could distinguish both small and large geo-objects. The segments showed greater size changes than those of the other methods, demonstrating the superiority of considering within- and between-segment heterogeneity in the OHRH method.

Highlights

With the thriving development of satellite sensors with different spatial resolutions, geographic object-based image analysis (GEOBIA) is currently available as a new and evolving paradigm in remote-sensing translation and analysis [1,2], which uses spectral, textural, and contextual information and geo-object features to improve image classification [3,4,5]
All the images were taken in north-eastern or south-eastern Beijing, China, and acquired from the gaofen-1 (GF-1) satellite, which is the first satellite of the Chinese High-resolution Earth Observation System (CHEOS), but the images differed in sensor type and spatial resolution
We proposed a hybrid remote-sensing image segmentation method that employs a within- and between-segment heterogeneity considered strategy for merging criteria (MC) in the region merging method

Summary

Introduction

With the thriving development of satellite sensors with different spatial resolutions, geographic object-based image analysis (GEOBIA) is currently available as a new and evolving paradigm in remote-sensing translation and analysis [1,2], which uses spectral, textural, and contextual information and geo-object features to improve image classification [3,4,5]. The edge-based segmentation method considers the gray values to be discontinuous at different boundary regions and generally searches for places where the gray values in the image are discontinuous to determine the edge [15,19,20,21,22]. The region-based segmentation method considers the similarity and adjacent relations between pixels, and ensures that the image satisfies a homogeneity criterion for each segmented object [23,24,25,26]. It is impossible to segment all land cover properly using the above mentioned segmentation categories because over-segmentation or under-segmentation problems often occur

Methods

Results

Discussion

Conclusion