Abstract Many researches have demonstrated that the spatial information can play an important role in the classification of hyperspectral imagery. Recently, an effective approach for spatial-spectral classification has been proposed using Minimum Spanning Forest (MSF) algorithm. Our goal is to improve this approach to the classification of hyperspectral images in urban areas. In the proposed method two spatial/texture features, using wavelet and Gabor filters, are first extracted. The Weighted Genetic (WG) algorithm is then used to obtain the subspace of hyperspectral data and texture features. They are then fed into a novel marker-based MSF classification algorithm. In this algorithm, the markers are extracted from the two spatial-spectral classification maps. To evaluate the efficiency of the proposed approach two image datasets, Pavia University acquired by ROSIS-03 and Berlin by HyMap, were used. Experimental results demonstrate that the proposed approach achieves approximately 17% and 14% better overall accuracy than the original MSF-based algorithm for these datasets, respectively.

中文翻译：

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使用扩展最小生成森林算法改进高光谱影像的空间光谱分类

摘要 大量研究表明，空间信息在高光谱影像分类中具有重要作用。最近，使用最小生成森林 (MSF) 算法提出了一种有效的空间光谱分类方法。我们的目标是改进这种方法来对城市地区的高光谱图像进行分类。在所提出的方法中，首先使用小波和 Gabor 滤波器提取两个空间/纹理特征。然后使用加权遗传 (WG) 算法获得高光谱数据和纹理特征的子空间。然后将它们输入到一种新的基于标记的 MSF 分类算法中。在该算法中，标记是从两个空间光谱分类图中提取的。为了评估所提出的方法对两个图像数据集的效率，使用 ROSIS-03 收购的帕维亚大学和 HyMap 收购的柏林。实验结果表明，对于这些数据集，所提出的方法比原始的基于 MSF 的算法的总体准确度分别提高了大约 17% 和 14%。