A Level Set Method for Infrared Image Segmentation Using Global and Local Information

Minjie Wan,Xavier Maldague,Weixian Qian,Kan Ren,Guohua Gu,Qian Chen,Jianhong Sun

doi:10.3390/rs10071039

Abstract

Infrared image segmentation plays a significant role in many burgeoning applications of remote sensing, such as environmental monitoring, traffic surveillance, air navigation and so on. However, the precision is limited due to the blurred edge, low contrast and intensity inhomogeneity caused by infrared imaging. To overcome these challenges, a level set method using global and local information is proposed in this paper. In our method, a hybrid signed pressure function is constructed by fusing a global term and a local term adaptively. The global term is represented by the global average intensity, which effectively accelerates the evolution when the evolving curve is far away from the object. The local term is represented by a multi-feature-based signed driving force, which accurately guides the curve to approach the real boundary when it is near the object. Then, the two terms are integrated via an adaptive weight matrix calculated based on the range value of each pixel. Under the framework of geodesic active contour model, a new level set formula is obtained by substituting the proposed signed pressure function for the edge stopping function. In addition, a Gaussian convolution is applied to regularize the level set function for the purpose of avoiding the computationally expensive re-initialization. By iteration, the object of interest can be segmented when the level set function converges. Both qualitative and quantitative experiments verify that our method outperforms other state-of-the-art level set methods in terms of accuracy and robustness with the initial contour being set randomly.

Highlights

Infrared (IR) imaging system that passively receives IR radiation (760 nm–1 mm) and converts the invisible rays into images has been extensively applied in remote sensing [1,2]
We argue that the width of this transition interval should be strictly controlled, i.e., ζ1 corresponding to the central point A and ζ2 corresponding to the lower inflection point B should be carefully selected, because once the relatively smooth area with a small ζ is dominated by the local term of signed pressure function (SPF), the evolution of curve will be extremely slow and is easy to drop into local minima
We have addressed the problem of constructing the SPF fusing global and local information, there are still three aspects that should be noticed in the implementation of the whole algorithm: (i) the initialization of level set function; (ii) the construction of level set formula; (iii) the evolution of level set function

Summary

Introduction

Infrared (IR) imaging system that passively receives IR radiation (760 nm–1 mm) and converts the invisible rays into images has been extensively applied in remote sensing [1,2]. The accuracy and robustness of IR image segmentation is hard to be guaranteed due to the inherent drawbacks of IR imaging itself, including the blurred edge, low target/background contrast, local inhomogeneity and so on [4]. As a result, it is of great necessity for us to further investigate a robust and precise segmentation method for IR image. The main drawbacks of edge-based ACMs are that they are quite sensitive to noise and the segmentation results are highly dependent on the initialization of contour [13]. The resulting boundary is always incomplete when the edge of target is weak or fuzzy, which is the so-called ‘boundary leakage’ problem [14]

Methods

Findings

Discussion

Conclusion