Hyperspectral classification method based on deep feedforward network

Abstract

The invention discloses a hyperspectral classification method based on a deep feedforward network. The method includes: utilizing a training sample allocation algorithm to calculate the number of training samples needing to be allocated to each category during classification; in each layer of classification network, generating a training sample data set for training a classifier by adopting a fixed training sample selection mode or a random training sample selection mode according to the number of the distributed training samples of each category; selecting a support vector machine or a convolutional neural network to perform initial classification on the images to obtain an initial classification result; extracting spatial feature information of the classification graph by using an edge preserving filter, and re-classifying the spatial feature information by using a trained support vector machine; and judging whether a stop condition is met or not, and if not, entering a lower-layer network for classification in a feedforward mode until an optimal classification result is finally obtained. According to the classification framework, through a series of spatial filters and feedforward operation, spatial feature information of the hyperspectral image is effectively mined, and an initial classification result is improved.

Description

technical field [0001] The invention belongs to the technical field of hyperspectral image classification, and in particular relates to a hyperspectral classification method based on a deep feedforward network. Background technique [0002] Compared with conventional remote sensing, hyperspectral imagery has greatly improved spectral resolution while retaining high spatial resolution, which makes it capable of describing the details of similar objects and identifying different types of objects. Significantly improved in all respects. Hyperspectral image classification is to classify each pixel in the hyperspectral image. Hyperspectral images have the characteristics of a large amount of continuous and rich spectral information, which makes them widely used in hyperspectral image classification and achieved good results. [0003] In recent years, spectral spatial classification algorithm, as a very effective hyperspectral classification method, has shown great potential in ...

AI Technical Summary

Problems solved by technology

However, in practice, the labeling of hyperspectral data samples is time-consuming, labor-intensive, and costly, so that it is very difficult to obtain a large number of training sample information

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