Kernel Tensor Sparse Coding Model for Precise Crop Classification of UAV Hyperspectral Image - Yang, Lixia; Zhang, Rui; Bao, Yajun; Yang, Shuyuan; Jiao, Licheng (IEEE geoscience and remote sensing letters, 2023)
In this letter, a kernel tensor sparse coding model (KTSCM) is proposed for precise crop classification of unmanned aerial vehicle (UAV) hyperspectral image (HSI). Benefited from the kernel tensor representation mechanism in KTSCM, which can not only improve the linear separation but also well preserving the spatial-spectral structures of land-covers, the discriminability of UAV HSI is greatly improved. The L1-norm based tensor sparsity makes the tensor operation in KTSCM can be equivalently converted to matrix operation, which greatly reduces the computation cost. Furthermore, the analytical solution to KTSCM allows it be well optimized with very few iterations. The performance of KTSCM is assessed on two real UAV HSIs. The experimental results indicate that KTSCM can provides rapid and accurate crop classification results with limited labeled pixels and outperforms the related counterparts.