A Lightweight Conditional Convolutional Neural Network for Hyperspectral Image Classification

Deep learning (dl), especially convolutional neural networks (cnns), has been proven to be an excellent feature extractor and widely applied to hyperspectral image (hsi) classification. However, dl is a computationally demanding algorithm with many parameters and a high computational burden, which seriously restricts the deployment of dl-based hsi classification algorithms on mobile and embedded systems. In this paper, we propose an extremely lightweight conditional three-dimensional (3D) hsi with a double-branch structure to solve these problems. Specifically, we introduce a lightweight conditional 3D convolution to replace the conventional 3D convolution to reduce the computational and memory cost of the network and achieve flexible hsi feature extraction. Then, based on lightweight conditional 3D convolution, we build two parallel paths to independently exploit and optimize the diverse spatial and spectral features. Furthermore, to precisely locate the key information, which is conducive to classification, a lightweight attention mechanism is carefully designed to refine extracted spatial and spectral features, and improve the classification accuracy with less computation and memory costs. Experiments on three public hsi data sets show that the proposed model can effectively reduce the cost of computation and memory, achieve high execution speed, and better classification performance compared with several recent dl-based models.