Cross Hyperspectral and LiDAR Attention Transformer: An Extended Self-Attention for Land Use and Land Cover Classification

Roy, Swalpa Kumar; Sukul, Atri; Jamali, Ali; Haut, Juan M.; Ghamisi, Pedram

doi:10.1109/tgrs.2024.3374324

Cited by 11 publications

(1 citation statement)

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“…To this end, a popular technique called the attention mechanism in the fields of neural machine translation [136][137][138] and computer vision [139][140][141][142] was introduced to capture salient spectral bands and relevant spatial areas of HSI cubes [143,144]. Many effective attention modules, such as self-attention (SA) modules [145], squeeze-and-excitation (SE) modules [146], convolutional block attention modules (CBAMs) [147], non-local modules [148], etc., have been proposed to enhance the discrimination of features.…”

Section: Introductionmentioning

confidence: 99%

Discriminating Spectral–Spatial Feature Extraction for Hyperspectral Image Classification: A Review

Li,

Wang,

Cheikh

2024

Sensors

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Hyperspectral images (HSIs) contain subtle spectral details and rich spatial contextures of land cover that benefit from developments in spectral imaging and space technology. The classification of HSIs, which aims to allocate an optimal label for each pixel, has broad prospects in the field of remote sensing. However, due to the redundancy between bands and complex spatial structures, the effectiveness of the shallow spectral–spatial features extracted by traditional machine-learning-based methods tends to be unsatisfying. Over recent decades, various methods based on deep learning in the field of computer vision have been proposed to allow for the discrimination of spectral–spatial representations for classification. In this article, the crucial factors to discriminate spectral–spatial features are systematically summarized from the perspectives of feature extraction and feature optimization. For feature extraction, techniques to ensure the discrimination of spectral features, spatial features, and spectral–spatial features are illustrated based on the characteristics of hyperspectral data and the architecture of models. For feature optimization, techniques to adjust the feature distances between classes in the classification space are introduced in detail. Finally, the characteristics and limitations of these techniques and future challenges in facilitating the discrimination of features for HSI classification are also discussed further.

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