Tata Consultancy Services Limited, Kolkata 700 091, India.
Department of Computer Science and Engineering, National Institute of Technical Teachers' Training and Research, Kolkata 700 106, India.
Sensors (Basel). 2020 Nov 29;20(23):6823. doi: 10.3390/s20236823.
In recent years, hyperspectral images (HSIs) have attained considerable attention in computer vision (CV) due to their wide utility in remote sensing. Unlike images with three or lesser channels, HSIs have a large number of spectral bands. Recent works demonstrate the use of modern deep learning based CV techniques like convolutional neural networks (CNNs) for analyzing HSI. CNNs have receptive fields (RFs) fueled by learnable weights, which are trained to extract useful features from images. In this work, a novel multi-receptive CNN module called GhoMR is proposed for HSI classification. GhoMR utilizes blocks containing several RFs, extracting features in a residual fashion. Each RF extracts features which are used by other RFs to extract more complex features in a hierarchical manner. However, the higher the number of RFs, the greater the associated weights, thus heavier is the network. Most complex architectures suffer from this shortcoming. To tackle this, the recently found Ghost module is used as the basic building unit. Ghost modules address the feature redundancy in CNNs by extracting only limited features and performing cheap transformations on them, thus reducing the overall parameters in the network. To test the discriminative potential of GhoMR, a simple network called GhoMR-Net is constructed using GhoMR modules, and experiments are performed on three public HSI data sets-Indian Pines, University of Pavia, and Salinas Scene. The classification performance is measured using three metrics-overall accuracy (OA), Kappa coefficient (Kappa), and average accuracy (AA). Comparisons with ten state-of-the-art architectures are shown to demonstrate the effectiveness of the method further. Although lightweight, the proposed GhoMR-Net provides comparable or better performance than other networks. The PyTorch code for this study is made available at the iamarijit/GhoMR GitHub repository.
近年来，高光谱图像（HSI）在计算机视觉（CV）中受到了相当大的关注，因为它们在遥感中有广泛的应用。与具有三个或更少通道的图像不同，HSI 具有大量的光谱带。最近的工作展示了使用现代基于深度学习的 CV 技术，如卷积神经网络（CNN），来分析 HSI。CNN 具有由可学习权重驱动的感受野（RF），这些权重经过训练可以从图像中提取有用的特征。在这项工作中，提出了一种名为 GhoMR 的新型多感受野 CNN 模块，用于 HSI 分类。GhoMR 利用包含多个 RF 的块，以残差的方式提取特征。每个 RF 提取特征，其他 RF 则利用这些特征以分层的方式提取更复杂的特征。然而，RF 的数量越多，相关的权重就越大，因此网络就越重。大多数复杂的架构都存在这个缺点。为了解决这个问题，使用了最近发现的 Ghost 模块作为基本构建单元。Ghost 模块通过仅提取有限的特征并对其进行廉价的变换来解决 CNN 中的特征冗余问题，从而减少网络中的总体参数。为了测试 GhoMR 的判别潜力，使用 GhoMR 模块构建了一个名为 GhoMR-Net 的简单网络，并在三个公共 HSI 数据集-印第安纳松树、帕维亚大学和萨利纳斯场景上进行了实验。使用三个度量标准来衡量分类性能-总体精度（OA）、Kappa 系数（Kappa）和平均精度（AA）。与十个最先进的架构进行比较，进一步证明了该方法的有效性。尽管轻量级，但所提出的 GhoMR-Net 提供了与其他网络相当或更好的性能。本研究的 PyTorch 代码可在 iamarijit/GhoMR GitHub 存储库中获得。
