Effect of pooling strategy on convolutional neural network for classification of hyperspectral remote sensing images
| Autor: | Vimal K. Shrivastava, Somenath Bera |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Contextual image classification
Artificial neural network Computer science Computation Pooling Hyperspectral imaging 020206 networking & telecommunications 02 engineering and technology Convolutional neural network Convolution Remote sensing (archaeology) Signal Processing 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Computer Vision and Pattern Recognition Electrical and Electronic Engineering Software Remote sensing |
| Zdroj: | IET Image Processing. 14:480-486 |
| ISSN: | 1751-9667 |
| DOI: | 10.1049/iet-ipr.2019.0561 |
| Popis: | The deep convolutional neural network (CNN) has recently attracted the researchers for classification of hyperspectral remote sensing images. The CNN mainly consists of convolution layer, pooling layer and fully connected layer. The pooling is a regularisation technique and improves the performance of CNN while reducing the computation time. Various pooling strategies have been developed in literature. This study shows the effect of pooling strategy on the performance of deep CNN for classification of hyperspectral remote sensing images. The authors have compared the performance of various pooling strategies such as max pooling, average pooling, stochastic pooling, rank-based average pooling and rank-based weighted pooling. The experiments were performed on three well-known hyperspectral remote sensing datasets: Indian Pines, University of Pavia and Kennedy Space Center. The proposed experimental results show that max pooling has produced better results for all the three considered datasets. |
| Databáze: | OpenAIRE |
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