IEEE Trans Image Process. 2018 Jun;27(6):2623-2634. doi: 10.1109/TIP.2018.2809606.
Convolutional neural network (CNN) is of great interest in machine learning and has demonstrated excellent performance in hyperspectral image classification. In this paper, we propose a classification framework, called diverse region-based CNN, which can encode semantic context-aware representation to obtain promising features. With merging a diverse set of discriminative appearance factors, the resulting CNN-based representation exhibits spatial-spectral context sensitivity that is essential for accurate pixel classification. The proposed method exploiting diverse region-based inputs to learn contextual interactional features is expected to have more discriminative power. The joint representation containing rich spectral and spatial information is then fed to a fully connected network and the label of each pixel vector is predicted by a softmax layer. Experimental results with widely used hyperspectral image data sets demonstrate that the proposed method can surpass any other conventional deep learning-based classifiers and other state-of-the-art classifiers.
卷积神经网络（CNN）在机器学习中备受关注，并在高光谱图像分类中表现出了优异的性能。在本文中，我们提出了一种分类框架，称为基于多样区域的 CNN，它可以对语义上下文感知表示进行编码，从而获得有前景的特征。通过合并一组多样化的判别外观因子，基于 CNN 的表示形式表现出空间光谱上下文敏感性，这对于准确的像素分类至关重要。利用基于多样区域的输入来学习上下文交互特征的所提出的方法预计将具有更强的判别能力。然后，将包含丰富光谱和空间信息的联合表示形式输入到全连接网络中，并通过 softmax 层预测每个像素向量的标签。使用广泛使用的高光谱图像数据集进行的实验结果表明，所提出的方法可以超越任何其他传统的基于深度学习的分类器和其他最先进的分类器。
