A GAN based method for cross-scene classification of hyperspectral scenes captured by different sensors

IF 3 4区 计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS
Amir Mahmoudi, Alireza Ahmadyfard
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引用次数: 0

Abstract

Labeling samples in hyperspectral images is time-consuming and labor-intensive. Domain adaptation methods seek to address this challenge by transferring the knowledge from a labeled source domain to an unlabeled target domain, enabling classification with minimal or no labeled samples in the target domain. This is achieved by mitigating the domain shift caused by differences in sensing conditions. However, most of the existing works implement domain adaptation techniques on homogeneous hyperspectral data where both source and target are acquired by the same sensor and contain an equal number of spectral bands. The present paper proposes an end-to-end network, Generative Adversarial Network for Heterogeneous Domain Adaptation (GANHDA), capable of handling domain adaptation between target and source scenes captured by different sensors with varying spectral and spatial resolutions, resulting in non-equivalent data representations across domains. GANHDA leverages adversarial training, a bi-classifier, variational autoencoders, and graph regularization to transfer high-level conceptual knowledge from the source to the target domain, aiming for improved classification performance. This approach is applied to two heterogeneous hyperspectral datasets, namely RPaviaU-DPaviaC and EHangzhou-RPaviaHR. All source labels are used for training, while only 5 pixels per class from the target are used for training. The results are promising and we achieved an overall accuracy of 90.16% for RPaviaU-DPaviaC and 99.12% for EHangzhou-RPaviaHR, outperforming previous methods. Our code Implementation can be found at https://github.com/amirmah/HSI_GANHDA.

Abstract Image

基于 GAN 的方法,用于对不同传感器捕获的高光谱场景进行跨场景分类
对高光谱图像中的样本进行标记既耗时又耗力。域适应方法旨在解决这一难题,它将知识从已标注的源域转移到未标注的目标域,从而在目标域中使用最少或无标注样本进行分类。这是通过减轻感知条件差异造成的领域偏移来实现的。然而,现有的大多数研究都是在同质高光谱数据上实施域自适应技术,即源数据和目标数据由同一个传感器采集,且包含相同数量的光谱带。本文提出了一种端到端网络--异构域自适应生成对抗网络(GANHDA),能够处理由不同传感器捕获的目标和源场景之间的域自适应问题,这些场景的光谱和空间分辨率各不相同,导致跨域的数据表示不相等。GANHDA 利用对抗训练、双分类器、变异自动编码器和图正则化将高级概念知识从源领域转移到目标领域,从而提高分类性能。这种方法适用于两个异构高光谱数据集,即 RPaviaU-DPaviaC 和 EHangzhou-RPaviaHR。所有源标签都被用于训练,而目标的每个类别只有 5 个像素被用于训练。结果令人鼓舞,RPaviaU-DPaviaC 和 EHangzhou-RPaviaHR 的总体准确率分别达到 90.16% 和 99.12%,优于之前的方法。我们的代码实现见 https://github.com/amirmah/HSI_GANHDA。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Multimedia Tools and Applications
Multimedia Tools and Applications 工程技术-工程:电子与电气
CiteScore
7.20
自引率
16.70%
发文量
2439
审稿时长
9.2 months
期刊介绍: Multimedia Tools and Applications publishes original research articles on multimedia development and system support tools as well as case studies of multimedia applications. It also features experimental and survey articles. The journal is intended for academics, practitioners, scientists and engineers who are involved in multimedia system research, design and applications. All papers are peer reviewed. Specific areas of interest include: - Multimedia Tools: - Multimedia Applications: - Prototype multimedia systems and platforms
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