Thermal Infrared Hyperspectral Band Selection via Graph Neural Network for Land Surface Temperature Retrieval

Thermal infrared hyperspectral imagery presents a superior capability for capturing intricate spectral details of atmospheres and ground objects compared to multispectral images, thus offering a more nuanced dataset for land surface temperature (LST) retrieval. However, extensive interband correlations pose computational challenges and undesirable “dimension disaster” problems. To address this issue, this article proposes a purpose-built framework of thermal infrared hyperspectral band selection (BS) using a graph neural network for LST retrieval. Specifically, the thermal infrared hyperspectral data is first mapped onto a graph topology, followed by feeding it into a graph attention module with brightness temperature constraints to extract band features. Following this, the extracted band features undergo a comprehensive analysis through a multiscale convolution module consisting of convolution kernels with multiple sizes, which have more variety and larger receptive fields for calculating the correlation between different band features, assigning different weights to each band. Finally, a weight selection module is designed to filter the bands based on their assigned weights, creating a subset of bands with greater significance for LST retrieval. Training the designed model, 65100 observations are simulated utilizing MODTRAN, with 80% allocated for training and 20% for testing. The experimental results validate the effectiveness of the proposed model, with a root mean square error (RMSE) of 1.85 K in practical applications on IASI imagery. This accomplishment substantiates the model’s capacity to reliably employ a judiciously selected subset of thermal infrared hyperspectral bands for LST retrieval applications, thus offering a promising contribution to the advancement of thermal infrared hyperspectral image processing methodologies.