Remote sensing image classification based on DenseNet feature hashing

LI Guoxiang; XIA Guo’en; BAI Liming; MA Wenbin

doi:10.6046/zrzyyg.2022028

2023 Vol. 35, No. 1

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LI Guoxiang, XIA Guo’en, BAI Liming, MA Wenbin. 2023. Remote sensing image classification based on DenseNet feature hashing. Remote Sensing for Natural Resources, 35(1): 66-73. doi: 10.6046/zrzyyg.2022028

Citation:

LI Guoxiang, XIA Guo’en, BAI Liming, MA Wenbin. 2023. Remote sensing image classification based on DenseNet feature hashing. Remote Sensing for Natural Resources, 35(1): 66-73. doi: 10.6046/zrzyyg.2022028

Remote sensing image classification based on DenseNet feature hashing

1. Department of Academic Affairs Guangxi University of Finance and Economics, Nanning 530003, China；
2. Guangxi Engineering Research Center of Big Data Analysis of Finance and Taxation, Nanning 530003, China；
3. School of Business Administration, Guangxi University of Finance and Economics, Nanning 530003, China

Received Date: 21 January 2022

Revised Date: 15 March 2023

Publish Date: 20 March 2023

Abstract

Abstract

To achieve accurate remote sensing scene classification, this study proposed a classification algorithm based on DenseNet feature hashing. First, dimension reduction was conducted for high-level semantic features output by a DenseNet through a fully connected layer. Then, normalized feature vectors were generated as the input of the classification layer using an activation function, and an end-to-end classification network was formed. Using the trained network as a feature extractor, the features of the activation layer of test data were mapped into binary hash codes. Finally, the remote sensing scene classification was conducted using support vector machine. The new algorithm was validated on public data sets UC Merced, WHU, and NWPU-RESISC45, and its classification effect was compared with that of multiple algorithms at three levels, namely the conventional local feature descriptor, transfer learning, and depth feature coding. The experimental results are as follows. The new algorithm had significantly higher classification accuracy than conventional algorithms based on mid- and low-level semantic features. Compared with the algorithm based on transfer learning, the proposed algorithm has fine-scale DenseNet feature mapping and accumulates elements used to determine core categories of images and, thus, is more suitable for the feature distribution of remote sensing images. Compared with the depth feature coding algorithm, the new algorithm has a simple feature structure, high classification accuracy, and strong transferability and extensibility and, thus, can meet the classification requirements of different remote sensing scenarios.
- transfer learning /
- feature coding /
- DenseNet /
- hash code

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References

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