Extreme learning machine-based hyperspectral remote sensing image ground object classification method

Abstract

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.

Description

technical field [0001] The invention belongs to the technical field of remote sensing image processing, and relates to a ground object classification method based on hyperspectral remote sensing images. Background technique [0002] While imaging the spatial information of ground objects, hyperspectral remote sensing images can also form dozens or even hundreds of narrow bands for each spatial pixel through dispersion to perform continuous spectral coverage and record the spectral information of ground objects. The data formed in this way can not only reflect the size, shape and other characteristics of the object in the spatial dimension like conventional image data, but also reflect the physical structure, chemical material, etc. Attributes. These data characteristics make hyperspectral images have important application value in the fields of scientific urban configuration, mineral resource detection, forest coverage detection, agricultural planting distribution planning,...

AI Technical Summary

Problems solved by technology

The core of the method is to design a multi-channel fusion network model (Enhanced Hierarchical Extreme Learning Machine, EH-ELM), which makes full use of the spatial characteristics and spectral characteristics of the objects in the image, enriches the representation information of the target, and solves the problem of hyperspectral image. The problem of insufficient classification accuracy

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