Abstract
Change detection (CD) is an important application of remote sensing, which provides timely change information about large-scale Earth surface. With the emergence of hyperspectral imagery, CD technology has been greatly promoted, as hyperspectral data with the highspectral resolution are capable of detecting finer changes than using the traditional multispectral imagery. Nevertheless, the high dimension of hyperspectral data makes it difficult to implement traditional CD algorithms. Besides, endmember abundance information at subpixel level is often not fully utilized. In order to better handle high dimension problem and explore abundance information, this paper presents a General End-to-end Two-dimensional CNN (GETNET) framework for hyperspectral image change detection (HSI-CD). The main contributions of this work are threefold: 1) Mixed-affinity matrix that integrates subpixel representation is introduced to mine more cross-channel gradient features and fuse multi-source information; 2) 2-D CNN is designed to learn the discriminative features effectively from multi-source data at a higher level and enhance the generalization ability of the proposed CD algorithm; 3) A new HSI-CD data set is designed for the objective comparison of different methods. Experimental results on real hyperspectral data sets demonstrate the proposed method outperforms most of the state-of-the-arts.
Abstract (translated)
变化检测（CD）是遥感技术的一个重要应用，它能及时提供大规模地表变化信息。随着高光谱图像的出现，CD技术得到了极大的发展，高光谱分辨率的高光谱数据比传统的多光谱图像更能检测出细微的变化。然而，高光谱数据的高维性使得传统的CD算法难以实现。此外，在亚像素级的端成员丰度信息往往没有得到充分利用。为了更好地处理高维问题和探索丰富的信息，本文提出了一个用于高光谱图像变化检测（HSI-CD）的通用端到端二维CNN（GETNET）框架。本文的主要贡献有三个方面：1）引入混合亲和矩阵，结合子像素表示，挖掘出更多的跨信道梯度特征，融合多源信息；2）设计了二维CNN，从更高层次的多源数据中有效地学习识别特征，增强了通用性。提出的CD算法的运算能力；3）针对不同方法的客观比较，设计了一种新的HSI-CD数据集。对实际高光谱数据集的实验结果表明，该方法优于大多数现有技术。
URL
https://arxiv.org/abs/1905.01662
