Image Segmentation Based on Constrained Spectral Variance Difference and Edge Penalty

Bo Chen,Bingfang Wu,Fang Qiu,Hongyue Du

doi:10.3390/rs70505980

Abstract

Segmentation, which is usually the first step in object-based image analysis (OBIA), greatly influences the quality of final OBIA results. In many existing multi-scale segmentation algorithms, a common problem is that under-segmentation and over-segmentation always coexist at any scale. To address this issue, we propose a new method that integrates the newly developed constrained spectral variance difference (CSVD) and the edge penalty (EP). First, initial segments are produced by a fast scan. Second, the generated segments are merged via a global mutual best-fitting strategy using the CSVD and EP as merging criteria. Finally, very small objects are merged with their nearest neighbors to eliminate the remaining noise. A series of experiments based on three sets of remote sensing images, each with different spatial resolutions, were conducted to evaluate the effectiveness of the proposed method. Both visual and quantitative assessments were performed, and the results show that large objects were better preserved as integral entities while small objects were also still effectively delineated. The results were also found to be superior to those from eCongnition’s multi-scale segmentation.

Highlights

The launch of a number of commercial satellites such as IKONOS, GeoEye and WorldView-1, 2, and 3 in the late 1990s has been an exciting development in the field of remote sensing
Five parameters control the quality of the final segments: the initial segmentation scale, T, ε, the scale parameter of merging criterion (MC) for region merging, and lastly the minimum object size in the minor object elimination process
We introduced constrained spectral variance difference (CSVD) and Edge Penalty (EP) to generate Merging Criterion (MC), and adopted a global mutual best-fitting strategy implemented through region adjacent graphs (RAG) and nearest neighbor graphs (NNG) to achieve this objective

Summary

Introduction

The launch of a number of commercial satellites such as IKONOS, GeoEye and WorldView-1, 2, and 3 in the late 1990s has been an exciting development in the field of remote sensing. These satellites provide improved capability to acquire high spatial resolution images. Compared with low and medium resolution images, high spatial resolution images are endowed with more detailed spatial information; this detail poses great challenges for traditional image processing approaches, such as pixel-based image classification. Successfully applied to low and moderate spatial resolution data, pixel-based classification schemes, which treat single pixels as processing units without considering contextual relationships with neighboring pixels, are not sufficient for high spatial resolution data.

Objectives

Results

Conclusion