The classification methods based on minimum spanning forest (MSF) have yielded impressive results for hyperspectral image. However, previous methods exist several drawbacks, i.e., marker selection methods are easily affected by boundary noise pixels, dissimilarity measure methods between pixels are inaccurate, and also image segmentation process is not robust, since they have not effectively utilized spatial information. To this end, in this paper, novel gradient-based marker selection technique, dissimilarity measures, and adaptive connection weighting method are proposed by making full use of spatial information in hyperspectral image. Concretely, for a given hyperspectral image, a pixel-wise classification is firstly performed, and meanwhile the gradient map is generated by a morphology-based algorithm. Secondly, the most reliable pixels are selected as the markers from the classification map, and then the boundary noise pixels are excluded from the marker map by using the gradient map. Thirdly, several new dissimilarity measures are proposed by incorporating gradient information or probability information of pixels. Furthermore, in the growth procedure of MSF, the connection weighting between pixels is adjusted adaptively to improve the robustness of the MSF algorithm. Finally, when building the final classification map by using the majority voting rule, the labels of the training samples are used to dominate the label prediction. Experimental results are performed on two hyperspectral image sets Indian Pines and University of Pavia with different resolutions and contexts. The proposed approach yields higher classification accuracies compared to previously proposed classification methods, and provides accurate segmentation maps.

基于最小生成林（MSF）的分类方法在高光谱图像方面取得了令人印象深刻的结果。然而，先前的方法存在一些缺点，即，标记选择方法容易受到边界噪声像素的影响，像素之间的相异度测量方法不准确，并且图像分割过程也不可靠，因为它们没有有效利用空间信息。为此，本文提出了一种新的基于梯度的标记选择技术，相异性度量和自适应连接加权方法，该方法充分利用了高光谱图像中的空间信息。具体地，对于给定的高光谱图像，首先进行逐像素分类，同时通过基于形态学的算法生成梯度图。其次，从分类图中选择最可靠的像素作为标记，然后使用梯度图从标记图中排除边界噪声像素。第三，通过结合像素的梯度信息或概率信息，提出了几种新的相异性度量。此外，在MSF的增长过程中，自适应调整像素之间的连接权重，以提高MSF算法的鲁棒性。最后，当使用多数表决规则构建最终分类图时，训练样本的标签将用于主导标签预测。实验结果在两个高光谱图像集上执行 通过结合像素的梯度信息或概率信息，提出了几种新的相异性度量。此外，在MSF的增长过程中，自适应调整像素之间的连接权重，以提高MSF算法的鲁棒性。最后，当使用多数投票规则构建最终分类图时，训练样本的标签将用于主导标签预测。实验结果在两个高光谱图像集上执行 通过结合像素的梯度信息或概率信息，提出了几种新的相异性度量。此外，在MSF的增长过程中，自适应调整像素之间的连接权重，以提高MSF算法的鲁棒性。最后，当使用多数投票规则构建最终分类图时，训练样本的标签将用于主导标签预测。实验结果在两个高光谱图像集上执行 当使用多数投票规则构建最终分类图时，训练样本的标签将用于主导标签预测。实验结果在两个高光谱图像集上执行 当使用多数投票规则构建最终分类图时，训练样本的标签将用于主导标签预测。实验结果在两个高光谱图像集上执行印度松和帕维亚大学拥有不同的决议和环境。与先前提出的分类方法相比，提出的方法产生了更高的分类精度，并提供了准确的分割图。