Although a number of change detection (CD) methods have been proposed during the past years, most of them are developed based on the assumption that there are either training samples or no training samples for both the pretime and posttime images. Few studies have addressed the scenario of only small amounts of training samples are available only in a single-time image. In this article, we propose a novel approach that can detect multiple changes in bitemporal hyperspectral images when only a few training samples are available in one of the images (the source image). The proposed method consists of four main steps: first, unsupervised CD based on uncertain area analysis to generate the binary change map; second, classification of the source image (X1) according to active learning; third, classification of the target image (X2) by the use of improved transfer learning; and fourth, generation of the multiclass change map by postclassification comparison. The proposed method was tested on one simulated dataset and two pairs of real bitemporal hyperspectral images. Experimental results demonstrate that: first, uncertain area analysis can improve the binary CD accuracy; while active learning and improved transfer learning can enhance the classification accuracy of the source and target images, the multiple CD accuracy is increased by the use of the proposed method; and second, compared with the state-of-the-art methods, the proposed method produced best results.

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基于不确定区域分析和改进迁移学习的高光谱变化检测新方法

尽管在过去几年中已经提出了许多变化检测 (CD) 方法，但其中大多数是基于以下假设开发的：对于前时间和后时间图像都有训练样本或没有训练样本。很少有研究解决仅在单次图像中只有少量训练样本可用的情况。在本文中，我们提出了一种新方法，当其中一个图像（源图像）中只有少数训练样本可用时，该方法可以检测双时态高光谱图像中的多个变化。该方法包括四个主要步骤：首先，基于不确定区域分析的无监督CD生成二值变化图；其次，根据主动学习对源图像（X1）进行分类；第三，使用改进的迁移学习对目标图像 (X2) 进行分类；第四，通过分类后比较生成多类变化图。所提出的方法在一个模拟数据集和两对真实双时相高光谱图像上进行了测试。实验结果表明：首先，不确定区域分析可以提高二进制CD精度；虽然主动学习和改进的迁移学习可以提高源图像和目标图像的分类精度，但通过使用所提出的方法提高了多重CD精度；其次，与最先进的方法相比，所提出的方法产生了最好的结果。所提出的方法在一个模拟数据集和两对真实双时相高光谱图像上进行了测试。实验结果表明：首先，不确定区域分析可以提高二进制CD精度；虽然主动学习和改进的迁移学习可以提高源图像和目标图像的分类精度，但通过使用所提出的方法提高了多重CD精度；其次，与最先进的方法相比，所提出的方法产生了最好的结果。所提出的方法在一个模拟数据集和两对真实双时相高光谱图像上进行了测试。实验结果表明：首先，不确定区域分析可以提高二进制CD精度；虽然主动学习和改进的迁移学习可以提高源图像和目标图像的分类精度，但通过使用所提出的方法提高了多重CD精度；其次，与最先进的方法相比，所提出的方法产生了最好的结果。