Abstract
In this paper, we propose an improved rotation invariant uniform local binary pattern (RIU-LBP) operator for segmenting high-resolution sensing image which can effectively describe the texture features of a high-resolution remote sensing image. The improved RIU-LBP is based on RIU-LBP. It introduces a threshold in binarization of region pixels. The new LBP operator can better tolerate small texture variation and better distinguish the plain and rough texture than the original RIU-LBP does. Then, a merging criterion of texture regions is proposed, which is based on regional LBP value distribution and Bhattacharyya distance. Finally, the texture merging criterion and spectral merging criterion are combined in the statistical region merging (SRM)-based remote sensing image segmentation method to improve segmentation results, taking full advantage of rich spectral and texture information in high-resolution remote sensing images. This algorithm can be adjusted to the number of segmented regions, and experiments indicate better segmentation results than ENVI 5.0 and the SRM method.
Highlights
Segmentation is an important problem in remote sensing image processing [1,2]
We propose a high-resolution remote sensing image segmentation algorithm based on the improved local binary pattern (LBP) feature and statistical region merging (SRM) region merging method
2.1 Rotation invariant uniform local binary pattern To describe the image texture features, the LBP was proposed by Ojala et al [12]
Summary
Segmentation is an important problem in remote sensing image processing [1,2]. Early remote sensing image segmentation methods utilize pixel-based strategies and ignore rich spectral and structure information. Object-oriented segmentation methods are extensively applied in remote sensing image analysis Homogenous region features such as intensity, texture, and shape can be used to improve the segmentation accuracy. We propose a high-resolution remote sensing image segmentation algorithm based on the improved LBP feature and SRM region merging method. The appropriate criterion can be adaptively chosen in the region merging step according to the characteristics of regions, which can further improve the segmentation performance This algorithm can segment high-resolution remote sensing images with complex scene effectively. The contributions of our proposed method includes the following: First, an improved RIU-LBP is proposed to describe texture features as the traditional LBP has difficulties in differentiating high-resolution remote sensing image regions with different textures. The improved RIU-LBP operator is applied to extract texture information in high-resolution remote sensing images for segmentation tasks.
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