一种基于多阈值的形态学提取遥感图象海岸线特征方法

A Multi-threshold Based Morphological Approach for Extracting Coastal Line Feature from Remote Sensed Images

在利用遥感数据进行海洋监测、海事救援、海洋污染监控等应用时 ,往往需要首先确定图象的海岸线特征 ,也就是说 ,需要对图象中海岸线进行分割提取 .阈值方法是一类简洁而有效的图象分割方法 ,其虽可以用于检测遥感图象中的海岸线特征 ,然而用传统的阈值方法来对光学遥感图象进行海岸线检测时 ,对于图象中沿海岸线的物体阴影、散射特性很弱的植被、很暗的人工设施、受噪声影响的海湾水域等往往缺乏足够的区别辨识能力 .为此提出了一种基于多阈值的形态分割方法 ,该方法首先将阈值检测后的孤立区域划分为内陆、外海和沿海岸线 3种孤立区域 ,然后根据区域距离和最小路径的定义 ,并利用形态学算子来对沿海岸线的孤立区域进行处理 ,以提高海岸线检测的精确度和降低误检率 ,实验结果表明 ,该方法不仅提高了对上述物体阴影、植被、暗的人工设施的准确检测率 。

While executing tasks such as sea surveilling, maritime searching and rescue, sea pollution monitoring utilizing remote sensed images, the coastal line feature should be determined at first. Thresholding method is a type of simple but valid method for image segmentation, likewise, they can be used to detect coastal line feature in remote sensed images. However, while conventional thresholding methods used to do it, they are always short of enough discriminating ability to objects' shadow, weak scattering vegetations, dark artificial buildings, sea gulf blurred by noise along costal line. This paper proposes a multi threshold based morphological approach, which divides the isolated regions by thresholding detecting into intra continent, exterior sea, and along coastal isolated regions at first, and then utilizes two definitions and morphological operators to process along coastal regions further so as to improve the detecting accuracy and decreasing false detecting, especially to enhance detecting accuracy for above objects' shadow, vegetations and dark artificial builds. Experiments are executed and the results exhibit the proposed approach possessing better performance than conventional thresholding approach.