The invention discloses an
extreme learning machine-based hyperspectral
remote sensing image ground object classification method. An original
extreme learning machine network is expanded into a hierarchical multi-channel fusion network. In terms of network training, the method is different from the
least squares algorithm-based output weight solving strategy of the original ELM (
extreme learning machine) and the global iterative optimization strategy of a
deep learning network; according to the method of the invention, a greedy layer-by-layer training mode is adopted to
train a hierarchical
network layer by layer, and therefore, the
training time of the network is greatly shortened; and in the layer-by-layer training process, a l1 regular optimization item is added into the training solving model of each layer of the network separately, so that parameter solving results are sparser, and the risk of over-fitting can be lowered. In terms of network functions, A single-
hidden layer ELM network focus on solving the fitting and classification problems of simple data, while the different levels of the
network model provided by the invention achieve target data
feature learning or
feature fusion, the
network model of the invention integrates the advantages of high training speed and strong generalization capacity of the single-
hidden layer ELM network, and therefore, the in-
orbit realization of the model is facilitated, and the requirements of
emergency response tasks can be satisfied.