Abstract
Segmentation of a river scene is a representative case of complex image segmentation. Different from road segmentation, river scenes often have unstructured boundaries and contain complex light and shadow on the water’s surface. According to the imaging mechanism of water pixels, this paper designed a water description feature based on a multi-block local binary pattern (MB-LBP) and Hue variance in HSI color space to detect the water region in the image. The improved Local Binary Pattern (LBP) feature was used to recognize the water region and the local texture descriptor in HSI color space using Hue variance was used to detect the shadow area of the river surface. Tested on two data sets including simple and complex river scenes, the proposed method has better segmentation performance and consumes less time than those of two other widely used methods.
Highlights
Segmentation of a river scene plays an important role in many fields such as the water hazard detection of unmanned ground vehicles [1], the navigation of unmanned ships [2], river analysis or flood monitoring by remote sensing [3,4,5,6] and vision-based object monitoring on rivers
We focus on the image segmentation of outdoor river scenes
To solve the problem that current methods often missed detection and made false segmentations when applied to complex river scenes, this study proposed a novel segmentation method based on a reflection mechanism of the water surface
Summary
Segmentation of a river scene plays an important role in many fields such as the water hazard detection of unmanned ground vehicles [1], the navigation of unmanned ships [2], river analysis or flood monitoring by remote sensing [3,4,5,6] and vision-based object monitoring on rivers. Researchers have explored different kinds of methods that fall into three main categories—image processing-based methods, Machine Learning-based methods (including Deep Learning, Supervised Learning, Clustering, etc.), and hardware-based methods. For image processing-based methods, Rankin et al [7] combined the color and texture features to detect the water region according to the appearance characteristics of the river in the outdoor scene. A designed texture feature is used to perform K-Means clustering on each 9 × 9 small patch in the image, where the class with the smallest average value of texture is classified as the water region, where the detection of water region with shadow needs the aid of stereo vision.
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