A Triplet Semisupervised Deep Network for Fusion Classification of Hyperspectral and LiDAR Data
Jiaojiao Li, Yinle Ma, Rui Song, Bobo Xi, Danfeng Hong, Qian Du
Abstract
Data fusion of hyperspectral and light detection and ranging (LiDAR) is conducive to obtain more comprehensive surface information and thereby achieve better classification result in Earth Monitoring Systems. However, lack of labeled samples usually limits the performance of supervised classifiers, and the heterogeneity of multi-source data also brings great challenges to data fusion. Aiming to address these issues, we propose a triplet semi-supervised deep convolutional neural network (TSDN) for fusion classification of hyperspectral and LiDAR. Specifically, we utilize three basic pathways to extract deep learning features: 1D-CNN for spectral features in hyperspectral, 2D-CNN for spatial features in hyperspectral and Cascade Net for elevation features in LiDAR data. Furthermore, a novel label calibration module (LCM) is proposed to generate effective pseudo labels with high confidence based on the superpixel segmentation by comparing the multi-view classification results for assisting semi-supervised model training. In addition, we design a novel 3D-Cross Attention Block to enhance the complementary spatial features of multi-source data. Experiments on three public HSI-LiDAR benchmarks: Houston, Trento, and MUUFL Gulfport have demonstrated the effectiveness and superiority of our proposed method.