{'fraction_non_alphanumeric': 0.02783109404990403, 'fraction_numerical': 0.0, 'mean_word_length': 5.51875, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
A Mutual Guidance Attention-Based Multi-Level Fusion Network for Hyperspectral and LiDAR Classification

Hyperspectral image (HSI) and light detection and ranging (LiDAR) data classification has attracted more and more attention in remote sensing. Convolution neural network (CNN) has been proven to be effective for HSI and LiDAR data classification. In this letter, a novel three-branch CNN is designed to learn spectral, spatial, and elevation features, each of which adopts the multi-level feature fusion (MLF) module to fuse the shallow and deep features. Furthermore, in order to fully fuse the spatial and elevation information, we propose a mutual guidance attention (MGA) module. The MGA module increases the information flow between spatial and elevation branches, highlights the features of interest, and weakens useless features. The proposed method is evaluated on public datasets Houston and Trento. Experimental results demonstrate that our proposed method can provide higher classification accuracy than some existing methods.