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Fuzzy Classification of High Resolution Remote Sensing Scenes Using Visual Attention Features.

Linyi Li¹, Tingbao Xu², Yun Chen³

¹School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China.

Computational Intelligence and Neuroscience

|August 2, 2017

Summary

A new visual attention feature extraction algorithm improves high-resolution remote sensing scene classification. This fuzzy classification method (FC-VAF) enhances accuracy over existing techniques.

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Area of Science:

Remote Sensing
Computer Vision
Image Analysis

Background:

High spatial resolution in remote sensing images does not guarantee better automatic scene classification.
Human visual attention mechanisms can improve scene classification accuracy.

Purpose of the Study:

To propose a novel visual attention feature extraction algorithm for high-resolution remote sensing scene classification.
To develop a fuzzy classification method using these visual attention features (FC-VAF).

Main Methods:

A multiscale process was used to extract visual attention features.
A fuzzy classification method (FC-VAF) was developed and applied to IKONOS, QuickBird, and ZY-3 images.
The impact of decomposition levels and wavelets on classification accuracy was analyzed.

Main Results:

FC-VAF achieved more accurate classification results compared to other methods.
Quantitative accuracy evaluation indices confirmed the superiority of FC-VAF.
The study identified optimal decomposition levels and wavelets for improved classification.

Conclusions:

FC-VAF significantly improves the accuracy of high-resolution remote sensing scene classification.
The proposed method advances digital image analysis and the application of high-resolution remote sensing data.