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首页> 外文期刊>International Journal of Advanced Robotic Systems >A novel hyperspectral image classification approach based on multiresolution segmentation with a few labeled samples:
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A novel hyperspectral image classification approach based on multiresolution segmentation with a few labeled samples:

机译：一种基于多分辨率分割和少量标记样本的新颖高光谱图像分类方法：

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摘要

Hyperspectral remote sensing technology becomes more and more popular in recent years which can be applied to satellite, plane, and flying robots. An important application of hyperspectral remote sensing is the classification of ground objects. However, when the number of labeled samples is very small, the classification accuracy of pixelwise classifiers will decline dramatically. In this article, a novel hyperspectral image classification approach is proposed based on multiresolution segmentation with a few labeled samples. The proposed method is motivated by the fact that pixels within a homogenous region are very likely to have the same class label, which can be utilized to increase the number of labeled samples. The proposed method consists of four steps. First, the hyperspectral image was segmented using the multiresolution image segmentation method. Second, the unlabeled neighbor pixels in the same region as the labeled pixels were selected randomly to assign the class labels. Next, one pixelwise classifier, that is, support vector machine, is used to classify the hyperspectral image with the new labeled sample set. Finally, edge-preserving filtering is performed on the classification result to remove the salt-and-pepper noise and preserve edges of ground objects. Experimental results on three real hyperspectral images demonstrate that the proposed method can improve the classification accuracy significantly when the number of labeled samples is relatively small.
机译：近年来，高光谱遥感技术变得越来越流行，可以应用于卫星，飞机和飞行机器人。高光谱遥感的重要应用是地面物体的分类。然而，当标记样本的数量很少时，像素分类器的分类精度将急剧下降。在本文中，提出了一种基于多分辨率分割和少量标记样本的新型高光谱图像分类方法。提出的方法是受以下事实激励的：在同质区域内的像素极有可能具有相同的类别标签，该标签可用于增加标记样本的数量。所提出的方法包括四个步骤。首先，使用多分辨率图像分割方法对高光谱图像进行分割。其次，随机选择与标记像素相同区域中的未标记相邻像素，以分配类别标签。接下来，使用一个像素级分类器（即支持向量机）对具有新标记样本集的高光谱图像进行分类。最后，对分类结果进行边缘保留滤波，以消除椒盐噪声并保留地面对象的边缘。在三个真实的高光谱图像上的实验结果表明，当标记的样本数量相对较少时，该方法可以显着提高分类精度。

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