@inproceedings{3192e17cf9a74d6aaed4a91f197916cf,
title = "A convolutional neural network based complex scene classification framework using transfer deep combined convolutional activations",
abstract = "In many scene classification applications, the variety of surface objects, high within-category diversity and between-category similarity carry challenges for the classification Framework. Most of CNN-based classification methods only extract image features from a single network layer, which may cause the completed image information difficult to extract in complex scenes. We propose a novel transfer deep combined convolutional activations (TDCCA) to integrate both the low-level and high-level features. Extensive comparative experiments are conducted on UC Merced database, Aerial Image database and NWPU-RESISC45 database. The results reveal that our proposed TDCCA achieves higher experimental accuracies than other up-to-date popular methods.",
keywords = "Transfer deep combined convolutional activations, convolutional neural network, image scene classification, weighted k-nearest neighbor classifier",
author = "Shuyun Liu and Hong Wang and Yutong Jiang and Zhonglin Yang and Zhiyang Ma",
note = "Publisher Copyright: {\textcopyright} 2022 SPIE.; 2021 International Conference on Optical Instruments and Technology: Optical Systems, Optoelectronic Instruments, Novel Display, and Imaging Technology ; Conference date: 08-04-2022 Through 10-04-2022",
year = "2022",
doi = "10.1117/12.2618661",
language = "English",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
publisher = "SPIE",
editor = "Juan Liu and Baohua Jia and Liangcai Cao and Xincheng Yao and Yongtian Wang and Takanori Nomura",
booktitle = "2021 International Conference on Optical Instruments and Technology",
address = "United States",
}
