Performance testing of selected automated coastline detection techniques applied on multispectral satellite imageries
Performance testing of selected automated coastline detection techniques applied on multispectral satellite imageries
复制标题
对应用于多光谱卫星图像的选定自动海岸线检测技术进行性能测试
DOI：
--
复制
发表时间：
2017
影响因子：
2.8
通讯作者：
A. Santra
中科院分区：
文献类型：
--
作者：
S. S. Mitra;D. Mitra;A. Santra
海岸线的自动检测一直是环境保护者的主要兴趣，许多方法已经被引入到自动检测海岸线。遥感技术是最有希望在这方面取得令人满意结果的技术。在我们的研究中，我们的目标是检索性能水平的某些图像处理技术，大力用于自动划定海岸线的目的，并对几乎在同一时期由利斯III和LANDSAT ETM+传感器获得的两幅图像进行了测试。在研究中使用的算法是水指数，植被指数，复波段比，ISODATA，插值，ISH转换技术。通过对图像进行陆地和水域分类来检测海岸线的准确性已尝试通过三种方式进行评估，首先与视觉解释的高分辨率Google Earth图像进行比较，其次是实地收集的类参考点的GCP数据，第三是原始图像本身。但是，时间差异的问题导致的约束做精度评估的前两个参考数据和地图沿着海岸。总体而言，尽管六种技术中的四种技术，即密度切片、ISODATA分类、水指数和ISH变换技术，显示出令人满意的结果，但在LISS-III和ETM+的情况下，水指数（LISS-III的kappa值为0.95，ETM+的kappa值为0.97）和强度-色调-饱和度变换技术给出了更好的性能。传感器之间的变化可能会在具有类似潮汐影响的同一季节的图像中引入海岸线检测的某些差异。
Coastline detection has been of major interest for environmentalists and many methods have been introduced to detect coastline automatically. Remote Sensing techniques are the most promising ones to deliver a satisfactory result in this regard. In our study, the objective was to retrieve performance level of certain image processing techniques vigorously used for the purpose to delineate coastline automatically and they were tested against two images acquired almost on the same period by LISS III and LANDSAT ETM+ sensors. The algorithms used in the study are Water Index, NDVI, Complex Band Ratio, ISODATA, Thresholding, ISH Transfirmation techniques. Accuracy of the shoreline detection by classifying the image in land and water has been tried to be estimated in three ways, firstly with comparison to the visually interpreted high resolution google earth image, secondly field collected GCP data of reference points of classes and thirdly the raw image itself. But problem in temporal disparity caused the constraint doing accuracy assessment from the first two reference data and maps along the coast. As a whole although four techniques among six, show satisfactory results namely density slicing, ISODATA classification, Water Index and ISH transformation technique, in the case of LISS-III and ETM+, Water Index (with kappa value being 0.95 for LISS-III and 0.97 for ETM+) and Intensity-Hue-Saturation transformation techniques give better performance. Sensor to sensor variation might have introduced certain differences in shoreline detection in images of same season with similar tidal influence.