Looking for breakthrough ideas for innovation challenges? Try Patsnap Eureka!

Hyperspectral image compression and classification method based on discriminative feature learning

A technology of hyperspectral image and feature learning, applied in the field of hyperspectral image compression and classification of discriminative feature learning, can solve problems such as poor practicability and achieve good practicability.

Inactive Publication Date: 2019-10-11
NORTHWESTERN POLYTECHNICAL UNIV
View PDF6 Cites 4 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0003] In order to overcome the disadvantages of poor practicability of existing hyperspectral image compression and classification methods, the present invention provides a hyperspectral image compression and classification method with discriminative feature learning

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Hyperspectral image compression and classification method based on discriminative feature learning
  • Hyperspectral image compression and classification method based on discriminative feature learning
  • Hyperspectral image compression and classification method based on discriminative feature learning

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0016] Define a hyperspectral data containing N pixels X=[x 1 ,x 2 ...,x N ]∈R B×N , where each pixel contains B bands, x i represents the spectral data of the i-th sample. For a classification problem of L classes, the training set Contains a total of M training samples, and the associated labels are expressed as The task of hyperspectral image classification is to pass the training set X t with Y t , assigning a predicted label to each pixel in X.

[0017] The present invention relates to encoder modules, decoder modules and classifier modules. The encoder module is used to map labeled data and unlabeled data in a latent space that maintains data discriminability; the decoder module is responsible for recovering the compressed data in the latent space as much as possible. Here, the decoder and the encoder have a completely symmetrical structure; the classifier module is used to map the data into the class space. The following is an introduction from three aspects...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

PUM

No PUM Login to View More

Abstract

The invention discloses a hyperspectral image compression and classification method based on discriminative feature learning, which is used for solving the technical problem of poor practicability ofthe existing hyperspectral image compression and classification method. According to the technical scheme, the end-to-end compressed classification network comprises two branch structures, wherein onepath is a stack self-encoding module and is used for learning data identifiability features, the encoder is used for feature compression, and the decoder is used for feature decompression. A mean square error loss function of all data is calculated through an encoder and a decoder; and the other path is a classification module which is used for classifying identifiable features, the encoder and the classifier use a shared module, and the classifier performs classification by utilizing identifiable compression features obtained by the encoder so as to complete end-to-end feature compression and classification tasks. The shared encoder module not only can obtain identifiable characteristics, but also can efficiently perform a hyperspectral image classification task according to the identifiable characteristics, and is good in practicability.

Description

technical field [0001] The invention relates to a hyperspectral image compression and classification method, in particular to a hyperspectral image compression and classification method for discriminative feature learning. Background technique [0002] Different from traditional images, each pixel of hyperspectral images contains a series of continuous spectral bands, and the rich spectral information makes it widely used in many fields. Due to the abundance of spectral bands, hyperspectral images consume more storage and computing costs than conventional color images, especially in the field of satellite remote sensing. In recent years, deep learning methods have been used more and more in hyperspectral image classification tasks, but models based on fully connected or convolutional neural networks usually require huge parameters and calculations. How to maintain discrimination while, Reducing the amount of model parameters or the amount of hyperspectral data is an urgent ...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

Application Information

Patent Timeline
no application Login to View More
IPC IPC(8): G06K9/62H04N19/42H04N19/44
CPCH04N19/42H04N19/44G06F18/24G06F18/214
Inventor 魏巍张磊张锦阳张艳宁
Owner NORTHWESTERN POLYTECHNICAL UNIV
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Patsnap Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic, Popular Technical Reports.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com