In recent times, the fusion of spatial relaxation with spectral data has achieved remarkable success in target classification methods. Spatial relaxation is a scheme which exploits the neighbourhood relationship between the pixels of an image to minimize the spatio-spectral distortion. Application of spatial relaxation with spectral data can lead to reduce the noise effect and increase the class characterization. Such methods can also be applied to estimate the posteriors of the probabilistic classifier, to increase the classifier’s final accuracy. In this paper, we have introduced an edge based feature fusion method which helps in characterizing the class labels of hyperspectral image (HSI) in a better sense. It is an iterative method which exploits the spatial information from an image in such a manner that it assumes the feature preservation in vertical and horizontal directions for each pixel. With combining subspace regression based probabilistic method, the proposed method gives better accuracy for benchmark HSI datasets. Before this, we have implemented a fast Bayesian subspace regression method to achieve the posterior probabilities, for our edge feature relaxation method. Finally, we have compared the results with some recently proposed methods, and $$\alpha $$ α expansion graph cut optimization method, which is an efficient technique to fuse the contextual knowledge in posterior probabilities.

中文翻译：

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HSI图像分类中的高效特征融合

近年来，空间弛豫与光谱数据的融合在目标分类方法中取得了显着的成功。空间松弛是一种利用图像像素之间的邻域关系来最小化空间光谱失真的方案。对光谱数据应用空间松弛可以减少噪声影响并增加类别特征。这些方法也可以用于估计概率分类器的后验，以提高分类器的最终精度。在本文中，我们介绍了一种基于边缘的特征融合方法，该方法有助于更好地表征高光谱图像 (HSI) 的类标签。它是一种迭代方法，它以假设每个像素在垂直和水平方向上的特征保留的方式利用来自图像的空间信息。结合基于子空间回归的概率方法，该方法为基准 HSI 数据集提供了更好的准确性。在此之前，我们已经为边缘特征松弛方法实现了快速贝叶斯子空间回归方法来实现后验概率。最后，我们将结果与一些最近提出的方法和 $$\alpha $$ α 扩展图切割优化方法进行了比较，这是一种在后验概率中融合上下文知识的有效技术。所提出的方法为基准 HSI 数据集提供了更好的准确性。在此之前，我们已经为边缘特征松弛方法实现了快速贝叶斯子空间回归方法来实现后验概率。最后，我们将结果与最近提出的一些方法和 $$\alpha $$ α 扩展图切割优化方法进行了比较，这是一种在后验概率中融合上下文知识的有效技术。所提出的方法为基准 HSI 数据集提供了更好的准确性。在此之前，我们已经为边缘特征松弛方法实现了快速贝叶斯子空间回归方法来实现后验概率。最后，我们将结果与一些最近提出的方法和 $$\alpha $$ α 扩展图切割优化方法进行了比较，这是一种在后验概率中融合上下文知识的有效技术。