Classification method and device for hyperspectral remote sensing image and storage equipment

Abstract

The invention provides a classification method and device for a hyperspectral remote sensing image and storage equipment. According to the classification method for a hyperspectral remote sensing image, dimension reduction processing is performed on a high-dimensional hyperspectral remote sensing image, the training performed by sample data for a deep forest classification model is combined, and then a purpose of accurately and quickly classifying the hyperspectral remote sensing image is achieved through the trained deep forest classification model. The classification device for the hyperspectral remote sensing image and the storage equipment are used for implementing the classification method for the hyperspectral remote sensing image. The beneficial effects of the invention are that thehyperspectral remote sensing image is classified by using a deep forest algorithm, the classification accuracy and the classification speed of the deep forest classification model are improved, and the method provided by the invention involves fewer parameters, is easy to adjust and greatly improves the classification effect and practicability of the hyperspectral remote sensing image.

Description

technical field [0001] The invention relates to the field of hyperspectral remote sensing image classification, in particular to a hyperspectral remote sensing image classification method, device and storage device. Background technique [0002] Hyperspectral remote sensing images have the characteristics of numerous bands, high spectral resolution, high dimensionality, and huge data volume. Because they can accurately reflect the spatial information and spectral information of ground objects, they have been widely used in geological exploration, resource investigation, urban planning, Environmental disaster monitoring and other fields have been widely used and researched by various countries. [0003] In the process of hyperspectral remote sensing image classification, traditional classification methods include support vector machine classification, artificial neural network classification and decision tree classification. Due to the limited spatial resolution of remote se...

AI Technical Summary

Problems solved by technology

However, these methods not only rely on a large number of training samples, but also have problems such as complex structure, troublesome parameter adjustment, and weak robustness.

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