Remote sensing image classification algorithm based on texture feature and extreme learning machine

Document Type

Article

Publication Date

1-1-2020

Abstract

With the development of satellite technology, the satellite imagery of the earth's surface and the whole surface makes it possible to survey surface resources and master the dynamic changes of the earth with high efficiency and low consumption. As an important tool for satellite remote sensing image processing, remote sensing image classification has become a hot topic. According to the natural texture characteristics of remote sensing images, this paper combines different texture features with the Extreme Learning Machine, and proposes a new remote sensing image classification algorithm. The experimental tests are carried out through the standard test dataset SAT-4 and SAT-6. Our results show that the proposed method is a simpler and more efficient remote sensing image classification algorithm. It also achieves 99.434% recognition accuracy on SAT-4, which is 1.5% higher than the 97.95% accuracy achieved by DeepSat. At the same time, the recognition accuracy of SAT-6 reaches 99.5728%, which is 5.6% higher than DeepSat's 93.9%.

Identifier

85091159641 (Scopus)

Publication Title

Computers Materials and Continua

External Full Text Location

https://doi.org/10.32604/cmc.2020.011308

e-ISSN

15462226

ISSN

15462218

First Page

1385

Last Page

1395

Issue

2

Volume

65

Grant

201901056009

This document is currently not available here.

Share

COinS