基于近邻协同的高光谱图像谱-空联合分类

遥感高光谱成像能够获得丰富的地物光谱信息, 为高精度的地物分析提供了可能. 针对高光谱图像分类中通常面临的数据维数高、标记样本少、计算量大等问题, 提出了一种简单有效的谱--空联合分类方法. 利用高光谱图像丰富的光谱信息和地物分布的空间平滑特性, 该算法首先对光谱数据进行特征提取和空间滤波, 然后利用本文提出的基于近邻协同的支持向量机(Neighborhood collaborative support vector machine, NC-SVM)进行分类. 近邻协同进一步利用地物分布的空间平滑特性, 通过联合空间近邻的判决信息进行中心像素的类别判定, 有效减小了只有少量训练样本下的错分概率. 实验表明, 相比已有的相关方法, 该算法在不明显增加计算量的情况下可获得更高的分类正确率, 能够实现少量训练样本下高光谱图像的快速高精度分类.

Spectral-spatial Classification of Hyperspectral Images Based on Neighborhood Collaboration

Hyperspectral imaging can provide rich spectral information about land covers, allowing detailed analysis of the materials on the earth. To address the high dimensionality, lack of sufficient labeled samples, and computationally intensive problems involved in hyperspectral image classification, this article presents a simple and efficient method to realize high accuracy classification with a limited training set. Taking advantage of the rich spectral features and the spatially homogeneous property of land covers'' distribution, the proposed method firstly employs feature extraction and spatial filtering to reduce the dimension and the noise of the original hyperspectral data, and then uses the proposed neighborhood collaborative support vector machine (NC-SVM) to classify each pixel. The NC-SVM further exploits the spatially homogeneous property of land covers'' distribution by combining the discriminant information of neighboring pixels to make reliable class judgement for the central one. Neighborhood collaboration can efficiently reduce the probability of misclassification when a small training set is available. Experiments show that the proposed algorithm can achieve a higher classification accuracy without significant increment in computational cost than several state-of-the-art methods. It can realize fast and high accuracy classification of hyperspectral images with limited training samples.