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GeneSIS: A GA-based fuzzy segmentation algorithm for remote sensing images

机译：GeneSIS：基于遗传算法的遥感图像模糊分割算法

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摘要

This paper proposes an object-based classification scheme for handling remotely sensed images. The method combines the results of a supervised pixel-based classifier with spatial information extracted from image segmentation. First, pixel-wise classification is implemented by a fuzzy output SVM classifier using spectral and textural features of pixels. This classification results to a set of fuzzy membership maps. Operating on this transformed space, a Genetic Sequential Image Segmentation (GeneSIS) algorithm is next developed to partition the image into homogeneous regions. GeneSIS follows a sequential object extraction approach, whereby at each iteration a single object is extracted by invoking a GA-based object extraction algorithm. This module evaluates the fuzzy content of candidate regions, and through an effective fitness function design provides objects with optimal balance between three fuzzy components: coverage, consistency and smoothness. The final classification map is obtained automatically via segmentation, since each segment is extracted with its own class label. The validity of the proposed method is shown on the land cover classification of three different remote sensing images, with varying number of spectral bands (multispectral/hyperspectral), different spatial resolutions and ground truth cover types. The accuracy results of our approach are favorably compared with the ones obtained by other segmentation-based classification techniques.
机译：本文提出了一种基于对象的分类方案来处理遥感图像。该方法将基于监督像素的分类器的结果与从图像分割中提取的空间信息结合在一起。首先，通过模糊输出SVM分类器使用像素的光谱和纹理特征来实现按像素分类。该分类导致一组模糊隶属关系图。接下来，在此变换后的空间上运行，遗传序列图像分割（GeneSIS）算法将图像划分为均匀区域。 GeneSIS遵循顺序对象提取方法，其中在每次迭代时，通过调用基于GA的对象提取算法来提取单个对象。该模块评估候选区域的模糊内容，并通过有效的适应度函数设计为对象提供三个模糊成分之间的最佳平衡：覆盖度，一致性和平滑度。最终的分类图是通过分段自动获得的，因为每个分段都是使用其自己的类别标签提取的。三种不同遥感影像的土地覆盖分类显示了所提方法的有效性，这三种遥感影像具有不同数量的光谱带（多光谱/高光谱），不同的空间分辨率和地面真实覆盖类型。与其他基于细分的分类技术获得的结果相比，我们的方法的准确性结果得到了很好的比较。

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