A novel spectral-spatial 3D auxiliary conditional GAN integrated convolutional LSTM for hyperspectral image classification

IF 2.7 4区 地球科学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Pallavi Ranjan, Ashish Girdhar, Ankur, Rajeev Kumar
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Abstract

Hyperspectral Imaging (HSI) has revolutionized earth observation through advanced remote sensing technology, providing rich spectral and spatial information across multiple bands. However, this wealth of data introduces significant challenges for classification, including high spectral correlation, the curse of dimensionality due to limited labeled data, the need to model long-term dependencies, and the impact of sample input on deep learning performance. These challenges are further exacerbated by the costly and complex acquisition of HSI data, resulting in limited availability of labeled samples and class imbalances. To address these critical issues, our study proposes a novel approach for generating high-quality synthetic hyperspectral data cubes using an advanced Generative Adversarial Network (GAN) integrated with the Wasserstein loss and gradient penalty phenomenon (WGAN-GP). This approach aims to augment real-world data, mitigating the scarcity of labeled samples that has long been a bottleneck in hyperspectral image analysis and classification. To fully leverage both the synthetic and real data, we introduce a novel Convolutional LSTM classifier designed to process the intricate spatial and spectral correlations inherent in hyperspectral data. This classifier excels in modeling multi-dimensional relationships within the data, effectively capturing long-term dependencies and improving feature extraction and classification accuracy. The performance of our proposed model, termed 3D-ACWGAN-ConvLSTM, is rigorously validated using benchmark hyperspectral datasets, demonstrating its effectiveness in augmenting real-world data and enhancing classification performance. This research contributes to addressing the critical need for robust data augmentation techniques in hyperspectral imaging, potentially opening new avenues for applications in areas constrained by limited data availability and complex spectral-spatial relationships.

Abstract Image

用于高光谱图像分类的新型光谱空间三维辅助条件 GAN 集成卷积 LSTM
高光谱成像(HSI)通过先进的遥感技术彻底改变了地球观测,提供了跨多个波段的丰富光谱和空间信息。然而,如此丰富的数据给分类带来了巨大挑战,包括高光谱相关性、标注数据有限导致的维度诅咒、建立长期依赖关系模型的需要以及样本输入对深度学习性能的影响。由于获取人机交互数据的成本高且复杂,导致标记样本的可用性有限和类不平衡,这些挑战进一步加剧。为了解决这些关键问题,我们的研究提出了一种新方法,利用先进的生成对抗网络(GAN)与瓦瑟斯坦损失和梯度惩罚现象(WGAN-GP)相结合,生成高质量的合成高光谱数据立方体。这种方法旨在增强真实世界的数据,缓解长期以来一直是高光谱图像分析和分类瓶颈的标记样本稀缺问题。为了充分利用合成数据和真实数据,我们引入了一种新型卷积 LSTM 分类器,旨在处理高光谱数据固有的复杂空间和光谱相关性。这种分类器擅长对数据中的多维关系建模,能有效捕捉长期依赖关系,提高特征提取和分类的准确性。我们提出的模型被称为 3D-ACWGAN-ConvLSTM ,其性能通过基准高光谱数据集得到了严格验证,证明了它在增强真实世界数据和提高分类性能方面的有效性。这项研究有助于满足高光谱成像对稳健数据增强技术的迫切需求,为受限于有限数据可用性和复杂光谱空间关系的领域的应用开辟了新途径。
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来源期刊
Earth Science Informatics
Earth Science Informatics COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-GEOSCIENCES, MULTIDISCIPLINARY
CiteScore
4.60
自引率
3.60%
发文量
157
审稿时长
4.3 months
期刊介绍: The Earth Science Informatics [ESIN] journal aims at rapid publication of high-quality, current, cutting-edge, and provocative scientific work in the area of Earth Science Informatics as it relates to Earth systems science and space science. This includes articles on the application of formal and computational methods, computational Earth science, spatial and temporal analyses, and all aspects of computer applications to the acquisition, storage, processing, interchange, and visualization of data and information about the materials, properties, processes, features, and phenomena that occur at all scales and locations in the Earth system’s five components (atmosphere, hydrosphere, geosphere, biosphere, cryosphere) and in space (see "About this journal" for more detail). The quarterly journal publishes research, methodology, and software articles, as well as editorials, comments, and book and software reviews. Review articles of relevant findings, topics, and methodologies are also considered.
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