Hyperspectral image (HSI) classification is an important task in earth observation missions. Convolution neural networks (CNNs) with the powerful ability of feature extraction have shown prominence in HSI classification tasks. However, existing CNN-based approaches cannot sufficiently mine the sequence attributes of spectral features, hindering the further performance promotion of HSI classification. This article presents a hyperspectral image transformer (HiT) classification network by embedding convolution operations into the transformer structure to capture the subtle spectral discrepancies and convey the local spatial context information. HiT consists of two key modules, i.e., spectral-adaptive 3-D convolution projection module and convolution permutator (ConV-Permutator) to retrieve the subtle spatial–spectral discrepancies. The spectral-adaptive 3-D convolution projection module produces the local spatial–spectral information from HSIs using two spectral-adaptive 3-D convolution layers instead of the linear projection layer. In addition, the Conv-Permutator module utilizes the depthwise convolution operations to separately encode the spatial–spectral representations along the height, width, and spectral dimensions, respectively. Extensive experiments on four benchmark HSI datasets, including Indian Pines, Pavia University, Houston2013, and Xiongan (XA) datasets, show the superiority of the proposed HiT over existing transformers and the state-of-the-art CNN-based methods. Our codes of this work are available at https://github.com/xiachangxue/DeepHyperX for the sake of reproducibility.

Numerous state-of-the-art transformer-based techniques with self-attention mechanisms have recently been demonstrated to be quite effective in the classification of hyperspectral images (HSIs). However, traditional transformer-based methods severely suffer from the following problems when processing HSIs with three dimensions: (1) processing the HSIs using 1D sequences misses the 3D structure information; (2) too expensive numerous parameters for hyperspectral image classification tasks; (3) only capturing spatial information while lacking the spectral information. To solve these problems, we propose a novel Quaternion Transformer Network (QTN) for recovering self-adaptive and long-range correlations in HSIs. Specially, we first develop a band adaptive selection module (BASM) for producing Quaternion data from HSIs. And then, we propose a new and novel quaternion self-attention (QSA) mechanism to capture the local and global representations. Finally, we propose a new and novel transformer method, i.e ., QTN by stacking a series of QSA for hyperspectral classification. The proposed QTN could exploit computation using Quaternion algebra in hypercomplex spaces. Extensive experiments on three public datasets demonstrate that the QTN outperforms the state-of-the-art vision transformers and convolution neural networks.

Fine classification of vegetation types has always been the focus and difficulty in the application field of remote sensing. Unmanned Aerial Vehicle (UAV) sensors and platforms have become important data sources in various application fields due to their high spatial resolution and flexibility. Especially, UAV hyperspectral images can play a significant role in the fine classification of vegetation types. However, it is not clear how the ultrahigh resolution UAV hyperspectral images react in the fine classification of vegetation types in highly fragmented planting areas, and how the spatial resolution variation of UAV images will affect the classification accuracy. Based on UAV hyperspectral images obtained from a commercial hyperspectral imaging sensor (S185) onboard a UAV platform, this paper examines the impact of spatial resolution on the classification of vegetation types in highly fragmented planting areas in southern China by aggregating 0.025 m hyperspectral image to relatively coarse spatial resolutions (0.05, 0.1, 0.25, 0.5, 1, 2.5 m). The object-based image analysis (OBIA) method was used and the effects of several segmentation scale parameters and different number of features were discussed. Finally, the classification accuracies from 84.3% to 91.3% were obtained successfully for multi-scale images. The results show that with the decrease of spatial resolution, the classification accuracies show a stable and slight fluctuation and then gradually decrease since the 0.5 m spatial resolution. The best classification accuracy does not occur in the original image, but at an intermediate level of resolution. The study also proves that the appropriate feature parameters vary at different scales. With the decrease of spatial resolution, the importance of vegetation index features has increased, and that of textural features shows an opposite trend; the appropriate segmentation scale has gradually decreased, and the appropriate number of features is 30 to 40. Therefore, it is of vital importance to select appropriate feature parameters for images in different scales so as to ensure the accuracy of classification.