Hyperspectral Image Classification Using Deep Pixel-Pair Features
Beijing University of Chemical Technology · Mississippi State University
Abstract
The deep convolutional neural network (CNN) is of great interest recently. It can provide excellent performance in hyperspectral image classification when the number of training samples is sufficiently large. In this paper, a novel pixel-pair method is proposed to significantly increase such a number, ensuring that the advantage of CNN can be actually offered. For a testing pixel, pixel-pairs, constructed by combining the center pixel and each of the surrounding pixels, are classified by the trained CNN, and the final label is then determined by a voting strategy. The proposed method utilizing deep CNN to learn pixel-pair features is expected to have more discriminative power. Experimental results based on…
Citation impact
- FWCI
- 76.63
- Percentile
- 100%
- References
- 32
Authors
4Topics & keywords
- Pixel
- Hyperspectral imaging
- Artificial intelligence
- Computer science
- Discriminative model
- Pattern recognition (psychology)
- Convolutional neural network
- Deep learning
- Reduced inequalities