A New Approach to Line Simplification Based on Image Processing: A Case Study of Water Area Boundaries
Abstract: Line simplification is an important component of map generalization. In recent years,
摘要：线的简化是地图简化中一个重要的组成部分。近些年，线简化算法被广泛地研究，
algorithms for line simplification have been widely researched, and most of them are based on vector
而其中的大部分是基于矢量数据的。
data. However, with the increasing development of computer vision, analysing and processing
但是，随着计算机视觉的快速发展，从无结构的图像数据中的分析和处理信息也是有意义而且有挑战的。
information from unstructured image data is both meaningful and challenging. Therefore, in this
因此，在本文中，
paper, we present a new line simplification approach based on image processing (BIP), which is
我们展现了一种新的基于图像处理的线简化方法（BIP），它是专门为栅格数据所设计的。
specifically designed for raster data. First, the key corner points on a multi-scale image feature
首先，多尺度图像特征上的关键边角点检测出来，并且被当成候选点。
are detected and treated as candidate points. Then, to capture the essence of the shape within a
然后，为了用最少的可能片段捕捉给定边界内的形状的本质，
given boundary using the fewest possible segments, the minimum-perimeter polygon (MPP) is
计算最小周边多边形（MPP），
calculated and the points of the MPP are defined as the approximate feature points. Finally, the points
并将MPP的点定义为近似特征点。
after simplification are selected from the candidate points by comparing the distances between
最后，通过比较候选点与近似特征点之间的距离，
the candidate points and the approximate feature points. An empirical example was used to test
从候选点中选择简化后的点。
the applicability of the proposed method. The results showed that (1) when the key corner points
使用一个经验实例来检验所提出的方法的适用性。结果显示：（1）当基于多尺度图像特征的关键角点被检测出来时，
are detected based on a multi-scale image feature, the local features of the line can be extracted
该方法能够很好地提取和保持直线的局部特征，
and retained and the positional accuracy of the proposed method can be maintained well; and (2)
并保持直线的定位精度；（2）
by defining the visibility constraint of geographical features, this method is especially suitable for
通过定义地理特征的能见度约束，这种方法特别适合于简化水域，
simplifying water areas as it is aligned with people’s visual habits
因为它符合人们的视觉习惯。
1. Introduction......1
2. Scale Space and Map Generalization......3
3. Methodologies for Line Simplification......4
4. Experiments and Evaluations ......11
