Hyperspectral imagery (HSI) one-class classification is aimed at identifying a single target class from the HSI by using only positive labels, which can significantly reduce the requirements for annotation. However, HSI one-class classification is far more challenging than HSI multi-class classification, due the lack of negative labels and the low target proportion, which are issues that have rarely been considered in the previous HSI classification studies. In this paper, a weakly supervised HSI one-class classifier, namely HOneCls is proposed to solve the problem of under-fitting of the positive class occurs in the HSI data with low target proportion, where a risk estimator -- the One-Class Risk Estimator -- is particularly introduced to make the full convolutional neural network (FCN) with the ability of one class classification. The experimental results obtained on challenging hyperspectral classification datasets, which includes 20 kinds of ground objects with very similar spectra, demonstrate the efficiency and feasibility of the proposed One-Class Risk Estimator. Compared with the state-of-the-art one-class classifiers, the F1-score is improved significantly in the HSI data with low target proportion.
翻译:高超光谱图像(HSI)一等分类的目的是通过只使用正面标签,从高超光谱图像(HSI)中确定一个单一目标类别,这只能使用正面标签,从而大大降低批注要求;然而,高超光谱图像(HSI)一等分类比HSI多级分类更具有挑战性,因为缺少负面标签和低目标比例,而这些问题在以前的高光谱图像分类研究中很少考虑。在本文件中,提议使用一个监督不力的HSI单级分类,即HOoneCls, 以解决HSI数据中阳性分类不足的问题,该数据在目标比例低的HSI数据中出现,其中风险估计器 -- -- 一等风险模拟器 -- -- 特别采用风险估计器 -- -- 使具有一种分类能力的全动态神经网络(FCN)变得特别困难。在具有挑战性的超光谱分类数据集(包括20种具有非常相似光谱的地面物体)上取得的实验结果,表明拟议的一等风险模拟器的效率和可行性。与一等一等高的目标分类中的数据比例大大改进。