Summary of Kronecker Product Feature Fusion For Convolutional Neural Network in Remote Sensing Scene Classification, by Yinzhu Cheng
Kronecker Product Feature Fusion for Convolutional Neural Network in Remote Sensing Scene Classification
by Yinzhu Cheng
First submitted to arxiv on: 8 Jan 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this paper, researchers develop a novel approach to enhance Convolutional Neural Network (CNN) performance in remote sensing scene classification tasks. By unifying two successful Feature Fusion methods, Add and Concat, using the Kronecker Product (KPFF), the proposed algorithm demonstrates improved accuracy over state-of-the-art CNN algorithms. The authors also discuss the Backpropagation procedure associated with this novel approach. Experimental results validate the effectiveness of the proposed method in enhancing CNN’s accuracy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps improve how computers analyze and classify images from space or satellites. It shows a new way to make Convolutional Neural Networks (special kinds of computer programs) better at recognizing what kind of scene is in a picture, like a forest or a city. By combining two good ideas into one, the researchers created an even better way for computers to recognize these scenes. This makes it easier for us to use images from space and satellites to understand our world. |
Keywords
* Artificial intelligence * Backpropagation * Classification * Cnn * Neural network
