Integrating Adversarial Generative Network with Variational Autoencoders towards Cross-Modal Alignment for Zero-Shot Remote Sensing Image Scene Classification
Remote sensing image scene classification takes image blocks as classification units and predicts their semantic descriptors. Because it is difficult to obtain enough labeled samples for all classes of remote sensing image scenes, zero-shot classification methods which can recognize image scenes tha...
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| Vydáno v: | Remote sensing (Basel, Switzerland) Ročník 14; číslo 18; s. 4533 |
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01.09.2022
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| Abstract | Remote sensing image scene classification takes image blocks as classification units and predicts their semantic descriptors. Because it is difficult to obtain enough labeled samples for all classes of remote sensing image scenes, zero-shot classification methods which can recognize image scenes that are not seen in the training stage are of great significance. By projecting the image visual features and the class semantic features into the latent space and ensuring their alignment, the variational autoencoder (VAE) generative model has been applied to address remote-sensing image scene classification under a zero-shot setting. However, the VAE model takes the element-wise square error as the reconstruction loss, which may not be suitable for measuring the reconstruction quality of the visual and semantic features. Therefore, this paper proposes to augment the VAE models with the generative adversarial network (GAN) to make use of the GAN’s discriminator in order to learn a suitable reconstruction quality metric for VAE. To promote feature alignment in the latent space, we have also proposed cross-modal feature-matching loss to make sure that the visual features of one class are aligned with the semantic features of the class and not those of other classes. Based on a public dataset, our experiments have shown the effects of the proposed improvements. Moreover, taking the ResNet models of ResNet18, extracting 512-dimensional visual features, and ResNet50 and ResNet101, both extracting 2048-dimensional visual features for testing, the impact of the different visual feature extractors has also been investigated. The experimental results show that better performance is achieved by ResNet18. This indicates that more layers of the extractors and larger dimensions of the extracted features may not contribute to the image scene classification under a zero-shot setting. |
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| AbstractList | Remote sensing image scene classification takes image blocks as classification units and predicts their semantic descriptors. Because it is difficult to obtain enough labeled samples for all classes of remote sensing image scenes, zero-shot classification methods which can recognize image scenes that are not seen in the training stage are of great significance. By projecting the image visual features and the class semantic features into the latent space and ensuring their alignment, the variational autoencoder (VAE) generative model has been applied to address remote-sensing image scene classification under a zero-shot setting. However, the VAE model takes the element-wise square error as the reconstruction loss, which may not be suitable for measuring the reconstruction quality of the visual and semantic features. Therefore, this paper proposes to augment the VAE models with the generative adversarial network (GAN) to make use of the GAN’s discriminator in order to learn a suitable reconstruction quality metric for VAE. To promote feature alignment in the latent space, we have also proposed cross-modal feature-matching loss to make sure that the visual features of one class are aligned with the semantic features of the class and not those of other classes. Based on a public dataset, our experiments have shown the effects of the proposed improvements. Moreover, taking the ResNet models of ResNet18, extracting 512-dimensional visual features, and ResNet50 and ResNet101, both extracting 2048-dimensional visual features for testing, the impact of the different visual feature extractors has also been investigated. The experimental results show that better performance is achieved by ResNet18. This indicates that more layers of the extractors and larger dimensions of the extracted features may not contribute to the image scene classification under a zero-shot setting. |
| Author | Li, Zheng Ma, Suqiang Liu, Chun Yang, Wei |
| Author_xml | – sequence: 1 givenname: Suqiang surname: Ma fullname: Ma, Suqiang – sequence: 2 givenname: Chun surname: Liu fullname: Liu, Chun – sequence: 3 givenname: Zheng surname: Li fullname: Li, Zheng – sequence: 4 givenname: Wei surname: Yang fullname: Yang, Wei |
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| Cites_doi | 10.3390/rs14102483 10.1080/01431161.2012.705443 10.1007/978-3-319-50077-5_2 10.1109/TGRS.2020.3047447 10.1109/TKDE.2009.191 10.1109/CVPR.2019.00844 10.3788/AOS201939.0610002 10.1109/CVPR.2018.00115 10.1109/CVPR.2016.15 10.1109/IJCNN.2019.8852315 10.1016/j.asoc.2021.107352 10.1109/TGRS.2017.2783902 10.1109/CVPRW.2009.5206594 10.3390/rs71114680 10.1109/TGRS.2014.2351395 10.1109/CVPR.2019.00089 10.1007/978-3-642-35085-6_6 10.3390/s20061594 10.1109/CVPR.2017.321 10.1109/CVPR.2017.473 10.1109/CVPR.2018.00581 10.1016/j.isprsjprs.2018.01.004 10.1109/TPAMI.2015.2408354 10.1109/TGRS.2017.2689071 10.1109/TGRS.2017.2685945 10.5244/C.31.3 10.1145/1869790.1869829 10.1109/ICCV.2015.474 10.1109/CVPRW.2015.7301382 10.3788/AOS201636.0428001 10.1016/j.rse.2018.05.006 10.1016/j.isprsjprs.2021.08.001 10.1109/ICECOME.2018.8645056 10.1109/JPROC.2017.2675998 10.1109/CVPR.2016.90 |
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| References | Hu (ref_5) 2015; 7 Liu (ref_1) 2016; 36 Chen (ref_34) 2019; 39 Cheng (ref_17) 2018; 56 Ding (ref_12) 2014; 9284 ref_14 ref_35 ref_11 ref_33 ref_10 ref_30 Cheng (ref_3) 2013; 34 ref_18 Goodfellow (ref_22) 2014; 27 Li (ref_37) 2020; 49 ref_39 ref_38 ref_15 Hinton (ref_48) 2008; 9 Xia (ref_41) 2017; 55 Li (ref_8) 2017; 55 Rostami (ref_32) 2022; 8 Chen (ref_2) 2014; 53 ref_25 ref_47 ref_24 Luo (ref_31) 2021; 107 ref_46 ref_23 ref_45 ref_21 ref_43 ref_20 ref_40 Cheng (ref_42) 2017; 105 Pan (ref_9) 2009; 22 Fu (ref_13) 2015; 37 ref_29 ref_28 Li (ref_19) 2021; 179 ref_27 ref_26 Zhang (ref_4) 2018; 212 Chen (ref_36) 2019; 46 ref_7 Li (ref_16) 2021; 59 Zhou (ref_44) 2018; 145 ref_6 |
| References_xml | – ident: ref_18 doi: 10.3390/rs14102483 – volume: 34 start-page: 45 year: 2013 ident: ref_3 article-title: Automatic landslide detection from remote-sensing imagery using a scene classification method based on BoVW and pLSA publication-title: Int. J. Remote Sens. doi: 10.1080/01431161.2012.705443 – ident: ref_26 doi: 10.1007/978-3-319-50077-5_2 – volume: 59 start-page: 10590 year: 2021 ident: ref_16 article-title: Learning deep cross-modal embedding networks for zero-shot remote sensing image scene classification publication-title: IEEE Trans. Geosci. Remote Sens. doi: 10.1109/TGRS.2020.3047447 – ident: ref_47 – volume: 22 start-page: 1345 year: 2009 ident: ref_9 article-title: A survey on transfer learning publication-title: IEEE Trans. Knowl. Data Eng. doi: 10.1109/TKDE.2009.191 – ident: ref_15 doi: 10.1109/CVPR.2019.00844 – volume: 39 start-page: 0610002 year: 2019 ident: ref_34 article-title: Image Feature Fusion Based Remote Sensing Scene Zero-Shot Classification Algorithm publication-title: Acta Opt. Sin. doi: 10.3788/AOS201939.0610002 – ident: ref_10 doi: 10.1109/CVPR.2018.00115 – volume: 27 start-page: 139 year: 2014 ident: ref_22 article-title: Generative adversarial nets publication-title: Adv. Neural Inf. Process. Syst. – ident: ref_27 doi: 10.1109/CVPR.2016.15 – ident: ref_39 – ident: ref_11 doi: 10.1109/IJCNN.2019.8852315 – volume: 107 start-page: 107352 year: 2021 ident: ref_31 article-title: Dual VAEGAN: A generative model for generalized zero-shot learning publication-title: Appl. Soft Comput. doi: 10.1016/j.asoc.2021.107352 – volume: 56 start-page: 2811 year: 2018 ident: ref_17 article-title: When Deep Learning Meets Metric Learning: Remote Sensing Image Scene Classification via Learning Discriminative CNNs publication-title: IEEE Trans. Geosci. Remote Sens. doi: 10.1109/TGRS.2017.2783902 – ident: ref_25 doi: 10.1109/CVPRW.2009.5206594 – volume: 8 start-page: 100278 year: 2022 ident: ref_32 article-title: Zero-shot image classification using coupled dictionary embedding publication-title: Mach. Learn. Appl. – volume: 7 start-page: 14680 year: 2015 ident: ref_5 article-title: Transferring deep convolutional neural networks for the scene classification of high-resolution remote sensing imagery publication-title: Remote Sens. doi: 10.3390/rs71114680 – volume: 53 start-page: 1947 year: 2014 ident: ref_2 article-title: Pyramid of spatial relatons for scene-level land use classification publication-title: IEEE Trans. Geosci. Remote Sens. doi: 10.1109/TGRS.2014.2351395 – ident: ref_14 doi: 10.1109/CVPR.2019.00089 – ident: ref_24 doi: 10.1007/978-3-642-35085-6_6 – volume: 9284 start-page: 135 year: 2014 ident: ref_12 article-title: Improving zero shot learning by mitigating the hubness problem publication-title: Comput. Sci. – ident: ref_43 doi: 10.3390/s20061594 – ident: ref_21 – ident: ref_29 doi: 10.1109/CVPR.2017.321 – ident: ref_23 doi: 10.1109/CVPR.2017.473 – ident: ref_30 doi: 10.1109/CVPR.2018.00581 – volume: 145 start-page: 197 year: 2018 ident: ref_44 article-title: PatternNet: A benchmark dataset for performance evaluation of remote sensing image retrieval publication-title: ISPRS J. Photogramm. Remote Sens. doi: 10.1016/j.isprsjprs.2018.01.004 – volume: 37 start-page: 2332 year: 2015 ident: ref_13 article-title: Transductive multi-view zero-shot learning publication-title: IEEE Trans. Pattern Anal. Mach. Intell. doi: 10.1109/TPAMI.2015.2408354 – ident: ref_6 – volume: 55 start-page: 4157 year: 2017 ident: ref_8 article-title: Zero-shot scene classification for high spatial resolution remote sensing images publication-title: IEEE Trans. Geosci. Remote Sens. doi: 10.1109/TGRS.2017.2689071 – ident: ref_33 – volume: 55 start-page: 3965 year: 2017 ident: ref_41 article-title: AID: A benchmark data set for performance evaluation of aerial scene classification publication-title: IEEE Trans. Geosci. Remote Sens. doi: 10.1109/TGRS.2017.2685945 – ident: ref_46 doi: 10.5244/C.31.3 – ident: ref_40 doi: 10.1145/1869790.1869829 – ident: ref_28 doi: 10.1109/ICCV.2015.474 – ident: ref_7 doi: 10.1109/CVPRW.2015.7301382 – volume: 46 start-page: 286 year: 2019 ident: ref_36 article-title: Word Vectors Fusion Based Remote Sensing Scenes Zero-shot Classification Algorithm publication-title: Comput. Sci. – volume: 9 start-page: 2579 year: 2008 ident: ref_48 article-title: Visualizing data using t-SNE publication-title: J. Mach. Learn. Res. – volume: 36 start-page: 0428001 year: 2016 ident: ref_1 article-title: High spatial resolution remote sensing image classification based on deep learning publication-title: Acta Opt. Sin. doi: 10.3788/AOS201636.0428001 – ident: ref_38 – volume: 212 start-page: 231 year: 2018 ident: ref_4 article-title: Integrating bottom-up classification and top-down feedback for improving urban land-cover and functional-zone mapping publication-title: Remote Sens. Environ. doi: 10.1016/j.rse.2018.05.006 – volume: 49 start-page: 1564 year: 2020 ident: ref_37 article-title: Zero-shot remote sensing image scene classification based on robust cross-domain mapping and gradual refinement of semantic space publication-title: Acta Geod. Cartogr. Sin. – volume: 179 start-page: 145 year: 2021 ident: ref_19 article-title: Robust deep alignment network with remote sensing knowledge graph for zero-shot and generalized zero-shot remote sensing image scene classification publication-title: ISPRS J. Photogramm. Remote Sens. doi: 10.1016/j.isprsjprs.2021.08.001 – ident: ref_20 – ident: ref_35 doi: 10.1109/ICECOME.2018.8645056 – volume: 105 start-page: 1865 year: 2017 ident: ref_42 article-title: Remote sensing image scene classification: Benchmark and state of the art publication-title: Proc. IEEE doi: 10.1109/JPROC.2017.2675998 – ident: ref_45 doi: 10.1109/CVPR.2016.90 |
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| SubjectTerms | Alignment Classification cross-modal feature alignment data collection Deep learning Experiments Feature extraction generative adversarial network Generative adversarial networks Image classification Neural networks Normal distribution Reconstruction Remote sensing remote sensing image scene classification Semantics Sensory integration variational autoencoder zero-shot learning |
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