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Monitoring and Evaluation of Flooded Areas Based on Fused Texture Descriptors

机译：基于融合纹理描述符的水灾地区监测与评估

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The paper presents a new method based on combining textural and color information for patch classification to segment flooded areas from aerial images. To this end, the paper presents a method which combines information provided by various important descriptors of texture like Local Binary Patterns, Histogram of Oriented Gradients, and Fractal Dimension in color version. The remote images were taken by the aid of an Unmanned Aircraft System (UAV) designed and implemented by an authors' team. The algorithm of remote image segmentation uses non-overlapped patches of dimension 128 x 128 pixels. It has two phases: (a) the learning phase to create the representatives of the flood class and (b) the segmentation phase, based on patch classification, to estimate the flood size. The classification is made by a voting criterion which takes into consideration the weights calculating from the three descriptors (fractal dimension, LBP and HOG). The accuracy of segmentation, evaluated from 100 real images, was better than in the separate approaches.
机译：本文提出了一种基于纹理信息和颜色信息相结合的补丁分类方法，可以从航空图像中分割出洪灾区域。为此，本文提出了一种方法，该方法结合了各种重要的纹理描述符所提供的信息，这些描述符包括彩色版本的局部二元图案，定向梯度直方图和分形维数。远程图像是在作者团队设计和实施的无人机系统（UAV）的帮助下拍摄的。远程图像分割算法使用尺寸为128 x 128像素的非重叠色块。它分为两个阶段：（a）学习阶段，以创建洪水类别的代表;（b）基于补丁分类的分段阶段，以评估洪水规模。通过投票标准进行分类，该投票标准考虑了从三个描述符（分维，LBP和HOG）计算出的权重。从100个真实图像评估的分割精度要优于单独的方法。

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