The use of deep neural networks (DNNs) has recently attracted great attention in the framework of the multi-label classification (MLC) of remote sensing (RS) images. To optimize the large number of parameters of DNNs a high number of reliable training images annotated with multi-labels is often required. However, the collection of a large training set is time-consuming, complex and costly. To minimize annotation efforts for data-demanding DNNs, in this paper we present several query functions for active learning (AL) in the context of DNNs for the MLC of RS images. Unlike the AL query functions defined for single-label classification or semantic segmentation problems, each query function presented in this paper is based on the evaluation of two criteria: i) multi-label uncertainty; and ii) multi-label diversity. The multi-label uncertainty criterion is associated to the confidence of the DNNs in correctly assigning multi-labels to each image. To assess the multi-label uncertainty, we present and adapt to the MLC problems three strategies: i) learning multi-label loss ordering; ii) measuring temporal discrepancy of multi-label prediction; and iii) measuring magnitude of approximated gradient embedding. The multi-label diversity criterion aims at selecting a set of uncertain images that are as diverse as possible to reduce the redundancy among them. To assess this criterion we exploit a clustering based strategy. We combine each of the above-mentioned uncertainty strategy with the clustering based diversity strategy, resulting in three different query functions. Experimental results obtained on two benchmark archives show that our query functions result in the selection of a highly informative set of samples at each iteration of the AL process in the context of MLC.
翻译:最近,在遥感图像多标签分类(MLC)的多标签分类(DNN)框架内,使用深层神经网络(DNN)最近引起极大注意。为了优化大量DNN的大量参数,经常需要多标签附加说明的大批可靠的培训图像。然而,收集大型培训成套材料需要花费时间、复杂和费用。为了最大限度地减少数据需求DNN的批注努力,我们在本文件中为刚果解放运动图像的DNN(AL)提出了一些积极学习的查询功能。与为单标签分类或语义分类问题定义的AL查询功能不同。为了优化DNNNM的大量参数,经常需要用多标签标签附加说明。然而,为了尽量减少数据需求DNNNNN对多标签 DNNN的批注努力,我们提出了几个查询功能,以积极学习(AL)积极学习(AL),与为单一标签分类分类分类或语义分解问题定义问题定义定义的AL查询功能不同。本文提出的每一项查询功能都基于对以下两个标准的评估:iLOL的每个标准进行时间差差差分级的计算。