A dense convolutional neural network for hyperspectral image classification
| Autor: | Xuchu Yu, Bing Liu, Xiangpo Wei, Lu Zhi |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
010504 meteorology & atmospheric sciences
Computer science business.industry Concatenation 0211 other engineering and technologies Hyperspectral imaging Pattern recognition 02 engineering and technology 01 natural sciences Convolutional neural network Image (mathematics) Feature (computer vision) Earth and Planetary Sciences (miscellaneous) Hyperspectral image classification Artificial intelligence Electrical and Electronic Engineering business 021101 geological & geomatics engineering 0105 earth and related environmental sciences |
| Zdroj: | Remote Sensing Letters. 10:59-66 |
| ISSN: | 2150-7058 2150-704X |
| DOI: | 10.1080/2150704x.2018.1526424 |
| Popis: | In this letter, a dense convolutional neural network (DCNN) is proposed for hyperspectral image classification, aiming to improve classification performance by promoting feature reuse and strengthening the flow of features and gradients. In the network, features are learned mainly through designed dense blocks, where feature maps generated in each layer can connect directly to the subsequent layers by a concatenation mode. Experiments are conducted on two well-known hyperspectral image data sets, using the proposed method and four comparable methods. Results demonstrate that overall accuracies of the DCNN reached 97.61 and 99.50% for the respective image data sets, representing an obvious improvement over the accuracies of the compared methods. The study confirms that the DCNN can provide more discriminable features for hyperspectral image classification and can offer higher classification accuracies and smoother classification maps. |
| Databáze: | OpenAIRE |
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