摘要翻译：
卫星图像的分割是进行面向对象图像分类的一个必要步骤，由于其在高空间分辨率图像上的适用性而变得越来越重要。为了进行面向对象的图像分类，首先要对所研究的图像进行均匀区域分割。这种分割需要一个专家用户的手工工作，他必须详尽地探索图像来建立阈值，以产生有用的和代表性的片段，而不过度分割和不丢弃代表性的片段。针对这些问题，我们提出了一种自动分割多光谱图像的方法。我们利用形态学滤波器，根据图像的光谱特征识别出图像中的同质区域。这些同质带代表了图像中不同类型的土地覆盖物，并被用作GrowCut多光谱分割算法的种子。GrowCut是一个具有竞争区域增长的细胞自动机，它的细胞通过三个参数与图像中的每个像素相连：像素的光谱特征、标签和代表细胞捍卫标签强度的强度因子。种子细胞拥有最大的力量，并在自动机的整个进化过程中保持其状态。从种子细胞开始，图像中的每个细胞都被其相邻细胞迭代攻击。当自动机停止更新它的状态时，我们得到一个分割图像，其中每个像素都带有它的一个单元格的标签。本文将该算法应用于Landsat8在委内瑞拉瓜里科卡拉博佐农用地上获取的图像，该图像中存在不同类型的土地覆盖物：农业、城市、水体和稀树草原，并受到不同程度的人为干预。所得到的分割以包围地理对象的不规则多边形表示。
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英文标题：
《Identification of Seed Cells in Multispectral Images for GrowCut
  Segmentation》
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作者：
Wuilan Torres and Antonio Rueda-Toicen
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最新提交年份：
2018
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分类信息：

一级分类：Electrical Engineering and Systems Science        电气工程与系统科学
二级分类：Image and Video Processing        图像和视频处理
分类描述：Theory, algorithms, and architectures for the formation, capture, processing, communication, analysis, and display of images, video, and multidimensional signals in a wide variety of applications. Topics of interest include: mathematical, statistical, and perceptual image and video modeling and representation; linear and nonlinear filtering, de-blurring, enhancement, restoration, and reconstruction from degraded, low-resolution or tomographic data; lossless and lossy compression and coding; segmentation, alignment, and recognition; image rendering, visualization, and printing; computational imaging, including ultrasound, tomographic and magnetic resonance imaging; and image and video analysis, synthesis, storage, search and retrieval.
用于图像、视频和多维信号的形成、捕获、处理、通信、分析和显示的理论、算法和体系结构。感兴趣的主题包括：数学，统计，和感知图像和视频建模和表示；线性和非线性滤波、去模糊、增强、恢复和重建退化、低分辨率或层析数据；无损和有损压缩编码；分割、对齐和识别；图像渲染、可视化和打印；计算成像，包括超声、断层和磁共振成像；以及图像和视频的分析、合成、存储、搜索和检索。
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一级分类：Computer Science        计算机科学
二级分类：Computer Vision and Pattern Recognition        计算机视觉与模式识别
分类描述：Covers image processing, computer vision, pattern recognition, and scene understanding. Roughly includes material in ACM Subject Classes I.2.10, I.4, and I.5.
涵盖图像处理、计算机视觉、模式识别和场景理解。大致包括ACM课程I.2.10、I.4和I.5中的材料。
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英文摘要：
  The segmentation of satellite images is a necessary step to perform object-oriented image classification, which has become relevant due to its applicability on images with a high spatial resolution. To perform object-oriented image classification, the studied image must first be segmented in uniform regions. This segmentation requires manual work by an expert user, who must exhaustively explore the image to establish thresholds that generate useful and representative segments without oversegmenting and without discarding representative segments. We propose a technique that automatically segments the multispectral image while facing these issues. We identify in the image homogenous zones according to their spectral signatures through the use of morphological filters. These homogenous zones are representatives of different types of land coverings in the image and are used as seeds for the GrowCut multispectral segmentation algorithm. GrowCut is a cellular automaton with competitive region growth, its cells are linked to every pixel in the image through three parameters: the pixel's spectral signature, a label, and a strength factor that represents the strength with which a cell defends its label. The seed cells possess maximum strength and maintain their state throughout the automaton's evolution. Starting from seed cells, each cell in the image is iteratively attacked by its neighboring cells. When the automaton stops updating its states, we obtain a segmented image where each pixel has taken the label of one of its cells. In this paper the algorithm was applied in an image acquired by Landsat8 on agricultural land of Calabozo, Guarico, Venezuela where there are different types of land coverings: agriculture, urban regions, water bodies, and savannas with different degrees of human intervention. The segmentation obtained is presented as irregular polygons enclosing geographical objects.
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PDF链接：
https://arxiv.org/pdf/1801.05525