The rapid development of sensor technology has made multi-modal remote sensing data valuable for land cover classification due to its diverse and complementary information. Many feature extraction methods for multi-modal data, combining light detection and ranging (LiDAR) and hyperspectral imaging (HSI), have recognized the importance of incorporating multiple spatial scales. However, effectively capturing both long-range global correlations and short-range local features simultaneously on different scales remains a challenge, particularly in large-scale, complex ground scenes. To address this limitation, we propose a multi-scale graph encoder–decoder network (MGEN) for multi-modal data classification. The MGEN adopts a graph model that maintains global sample correlations to fuse multi-scale features, enabling simultaneous extraction of local and global information. The graph encoder maps multi-modal data from different scales to the graph space and completes feature extraction in the graph space. The graph decoder maps the features of multiple scales back to the original data space and completes multi-scale feature fusion and classification. Experimental results on three HSI-LiDAR datasets demonstrate that the proposed MGEN achieves considerable classification accuracies and outperforms state-of-the-art methods.

A high-precision cross-correlation cavity length demodulation method for fiber-optic Fabry–Perot (F–P) sensors based on two different wavelength superluminescent diodes (SLDs) was proposed. This method can solve the problem of low demodulation accuracy caused by the difficulty in identifying the maximum cross-correlation coefficient when the cavity length of the fiber-optic F–P fiber sensor is too short, or when the spectral bandwidth of the illuminating single-light source is too narrow. This demodulation method is based on the principle that the two main peaks of the two cross-correlation curves corresponding to two different spectral ranges should match, and the average value of the two calculated cavity lengths corresponding to the two matched peaks is determined as the real cavity length. The cavity length demodulation of fiber-optic F–P sensors in the range of 20–200 μm shows a maximum measurement deviation of 0.008 μm, which is significantly smaller than the demodulation result obtained with a single light source, and the standard deviation of the measurement results is only approximately 0.0005 μm, indicating the high precision and stability of a dual SLD cross-correlation demodulation method.