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AID: A Benchmark Data Set for Performance Evaluation of Aerial Scene Classification

Xia, Gui-Song, Hu, Jingwen, Hu, Fan, Shi, Baoguang, Bai, Xiang, Zhong, Yanfei, Zhang, Liangpei, Lu, Xiaoqiang·2017·IEEE Transactions on Geoscience and Remote Sensing
DOI 10.1109/tgrs.2017.2685945
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  • Deep models' performance strongly relies on the quality and quantity of training data.

    “their performance strongly relies on the quality and quantity of training data (LeCun et al., 2015, Xia et al., 2017, Ding et al., 2021)”

    From Enabling country-scale land cover mapping with meter-resolution satellite imagery (2023) · cites this paper as Xia et al., 2017

    Section 2.3 says that training large deep networks on aerial datasets with only hundreds or thousands of images can lead to overfitting and lower accuracy, supporting the importance of training-data quantity. The paper discusses dataset diversity and image relevance, but the cited passages do not establish a comparable dependence of model performance on training-data quality. (LLM, conf=0.84)

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  1. 10.1109/TGRS.2015.2435801 · DOI
  2. 10.1109/TGRS.2015.2488681 · DOI
  3. castelluccio, 2015, Land use classification in remote sensing images by convolutional neural networks
  4. 10.1109/JSTARS.2015.2444405 · DOI
  5. 10.1109/IGARSS.2015.7326956 · DOI
  6. 10.3390/rs71114988 · DOI
  7. 10.1109/TGRS.2014.2357078 · DOI
  8. 10.1109/TGRS.2015.2400449 · DOI
  9. 10.1109/LGRS.2015.2483680 · DOI
  10. 10.1109/CVPRW.2015.7301382 · DOI
  11. 10.1117/1.JRS.9.095064 · DOI
  12. 10.1109/TGRS.2015.2411331 · DOI
  13. 10.1109/LGRS.2014.2357392 · DOI
  14. 10.1109/TGRS.2013.2241444 · DOI
  15. 10.1109/CBMI.2014.6849835 · DOI
  16. 10.1117/1.JRS.8.083690 · DOI
  17. 10.1109/JSTARS.2014.2339842 · DOI
  18. zhao, 2014, A 2-D wavelet decomposition-based bag-of-visual-words model for land-use scene classification, Int J Remote Sens, 35, 2296, 10.1080/01431161.2014.890762 · DOI
  19. 10.1109/TGRS.2014.2351395 · DOI
  20. zhu, 2014, Multi-feature probability topic scene classifier for high spatial resolution remote sensing imagery, Proc IEEE Int Geosci Remote Sens Symp (IGARSS), 2854
  21. fan, 2008, LIBLINEAR: A library for large linear classification, J Mach Learn Res, 9, 1871
  22. 10.1016/j.rse.2010.12.017 · DOI
  23. 10.1016/j.rse.2011.11.020 · DOI
  24. 10.1109/JURSE.2011.5764800 · DOI
  25. 10.1145/1869790.1869829 · DOI
  26. xia, 2010, Structural high-resolution satellite image indexing, Proc ISPRS TC 7th Symp Years ISPRS, 38, 298
  27. 10.1109/LGRS.2009.2023536 · DOI
  28. dos santos, 2010, Evaluating the potential of texture and color descriptors for remote sensing image retrieval and classification, Proc VISAPP, 203
  29. 10.1109/ICIP.2008.4712139 · DOI
  30. 10.1109/TGRS.2014.2360100 · DOI
  31. 10.1109/TGRS.2014.2306692 · DOI
  32. 10.1007/s11760-015-0804-2 · DOI
  33. 10.3390/rs71114680 · DOI
  34. 10.1109/TGRS.2014.2374218 · DOI
  35. 10.1016/j.isprsjprs.2016.03.014 · DOI
  36. 10.1109/ICCV.2011.6126403 · DOI
  37. 10.1016/j.isprsjprs.2009.06.004 · DOI
  38. risojevi?, 2011, Aerial image classification using structural texture similarity, Proc IEEE Int Symp Signal Process Inf Technol (ISSPIT), 190
  39. 10.1080/01431161.2011.608740 · DOI
  40. 10.2307/3545542 · DOI
  41. blaschke, 2001, What’s wrong with pixels? Some recent developments interfacing remote sensing and GIS, GeoBIT/GIS, 6, 12
  42. gao, 2006, Comparison of pixel-based and object-oriented image classification approaches—A case study in a coal fire area, Wuda, Inner Mongolia, China, Int J Remote Sens, 27, 4039, 10.1080/01431160600702632 · DOI
  43. 10.1109/WARSD.2003.1295182 · DOI
  44. nogueira, 2016, Towards better exploiting convolutional neural networks for remote sensing scene classification
  45. 10.1109/LGRS.2015.2475299 · DOI · Paper page
  46. 10.1109/JSTSP.2011.2139193 · DOI
  47. 10.1109/TGRS.2008.2010404 · DOI
  48. mallat, 2012, Combined scattering for rotation invariant texture analysis, Proc ESANN, 68
  49. 10.1137/080730627 · DOI
  50. 10.1109/TIP.2008.925367 · DOI
  51. perronnin, 2010, Improving the fisher kernel for large-scale image classification, Proc Eur Conf Comput Vis, 143
  52. 10.1109/CVPR.2012.6248021 · DOI
  53. yang, 2009, Linear spatial pyramid matching using sparse coding for image classification, Proc IEEE Conf Comput Vis Pattern Recognit, 1794
  54. blei, 2003, Latent Dirichlet allocation, J Mach Learn Res, 3, 993
  55. stricker, 1995, Similarity of color images, Proc IST/SPIE Electron Imag Sci Technol, 381
  56. 10.1109/LGRS.2010.2055033 · DOI
  57. 10.1109/IGARSS.2013.6721125 · DOI
  58. risojevi?, 2011, Gabor descriptors for aerial image classification, Adaptive and Natural Computing Algorithms, 51, 10.1007/978-3-642-20267-4_6 · DOI
  59. 10.1023/A:1011139631724 · DOI
  60. 10.1007/s11263-013-0640-1 · DOI
  61. 10.1007/BF00130487 · DOI
  62. 10.1109/34.531803 · DOI
  63. 10.1007/s11263-009-0312-3 · DOI
  64. 10.1109/ICPR.2014.288 · DOI
  65. 10.1109/TGRS.2015.2393857 · DOI
  66. hu, 2013, Exploring the use of Google earth imagery and object-based methods in land use/cover mapping, Remote Sens, 5, 6026, 10.3390/rs5116026 · DOI
  67. 10.1162/neco.2006.18.7.1527 · DOI · Paper page
  68. 10.1109/TPAMI.2011.235 · DOI · Paper page
  69. 10.1145/2647868.2654889 · DOI · Paper page
  70. sermanet, 2013, Overfeat Integrated Recognition Localization and Detection Using Convolutional Networks
  71. 10.1109/CVPR.2007.383266 · DOI · Paper page
  72. min, 2013, Network in Network
  73. yu, 2009, Nonlinear learning using local coordinate coding, Proc Adv Neural Inf Process Syst, 2223
  74. risojevi?, 2012, Orientation difference descriptor for aerial image classification, Proc Int Conf Syst Signals Image Process (IWSSIP), 150
  75. 10.3390/rs5052275 · DOI
  76. 10.1109/JSTARS.2012.2228254 · DOI
  77. shao, 2013, A hierarchical scheme of multiple feature fusion for high-resolution satellite scene categorization, Computer Vision Systems, 324, 10.1007/978-3-642-39402-7_33 · DOI
  78. 10.1080/01431161.2013.845925 · DOI
  79. 10.1109/LGRS.2012.2225596 · DOI
  80. vincent, 2010, Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion, J Mach Learn Res, 11, 3371
  81. 10.1109/TGRS.2012.2205158 · DOI
  82. 10.1007/11744085_40 · DOI
  83. 10.1080/2150704X.2013.858843 · DOI
  84. szegedy, 2014, Going Deeper with Convolutions
  85. 10.1109/LGRS.2012.2216499 · DOI
  86. krizhevsky, 2012, ImageNet classification with deep convolutional neural networks, Proc Adv Neural Inf Process Syst, 1097
  87. 10.1007/s11760-014-0704-x · DOI
  88. 10.1109/TSMC.1973.4309314 · DOI · Paper page
  89. 10.1109/TGRS.2016.2523563 · DOI
  90. simonyan, 2014, Very Deep Convolutional Networks for Large-scale Image Recognition
  91. zhou, 2015, Learning deep features for scene recognition using places database, Proc Adv Neural Inf Process Syst, 1, 487
  92. 10.1109/TNNLS.2014.2359471 · DOI
  93. 10.1109/TGRS.2016.2601622 · DOI · Paper page

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Metadata

Authors
Xia, G.-S., Hu, J., Hu, F., Shi, B., Bai, X., Zhong, Y., Zhang, L., & Lu, X.
ORCID
Venue
IEEE Transactions on Geoscience and Remote Sensing
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2017-07
Volume
55
Issue
7
Pages
3965-3981
ISSN
  • 0196-2892
  • 1558-0644
DOI
10.1109/tgrs.2017.2685945
Cited by
2,372
References
7
Licence
ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html (vor)
Funders
National Natural Science Foundation of China (41501462, 91338113)

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