Fetching the paper…
Reading the bibliography…
Aerial scene classification, which aims to automatically label an aerial image with a specific semantic category, is a fundamental problem for understanding high-resolution remote sensing imagery.
R. M. Haralick, K. Shanmugam, and I. H. Dinstein, “Textural features for image classification,” IEEE Transactions on Systems, Man and Cybernetics , no. 6, pp. 610–621, 1973
1973
Earlier work this paper cites.
N. B. Kotliar and J. A. Wiens, “Multiple scales of patchiness and patch structure: a hierarchical framework for the study of heterogeneity,” Oikos , pp. 253–260, 1990
1990
Earlier work this paper cites.
M. J. Swain and D. H. Ballard, “Color indexing,” International journal of computer vision , vol. 7, no. 1, pp. 11–32, 1991
1991
Earlier work this paper cites.
M. A. Stricker and M. Orengo, “Similarity of color images,” in IS&T/SPIE’s Symposium on Electronic Imaging: Science & Technology . International Society for Optics and Photonics, 1995, pp. 381–392
1995
Earlier work this paper cites.
B. S. Manjunath and W.-Y. Ma, “Texture features for browsing and retrieval of image data,” IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 18, no. 8, pp. 837–842, 1996
1996
Earlier work this paper cites.
T. Blaschke and J. Strobl, “What¡¯s wrong with pixels? some recent developments interfacing remote sensing and gis,” GeoBIT/GIS , vol. 6, no. 01, pp. 12–17, 2001
2001
Earlier work this paper cites.
A. Oliva and A. Torralba, “Modeling the shape of the scene: A holistic representation of the spatial envelope,” International Journal of Computer Vision , vol. 42, no. 3, pp. 145–175, 2001
2001
Earlier work this paper cites.
T. Ojala, M. Pietikäinen, and T. Mäenpää, “Multiresolution gray-scale and rotation invariant texture classification with local binary patterns,” IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 24, no. 7, pp. 971–987, 2002
2002
Earlier work this paper cites.
T. Blaschke, “Object-based contextual image classification built on image segmentation,” in IEEE Workshop on Advances in Techniques for Analysis of Remotely Sensed Data . IEEE, 2003, pp. 113–119
2003
Earlier work this paper cites.
D. M. Blei, A. Y. Ng, and M. I. Jordan, “Latent dirichlet allocation,” the Journal of Machine Learning research , vol. 3, pp. 993–1022, 2003
2003
Earlier work this paper cites.
J. Sivic and A. Zisserman, “Video google: A text retrieval approach to object matching in videos,” in Proc. IEEE International Conference on Computer Vision , 2003, pp. 1470–1477
2003
Earlier work this paper cites.
D. G. Lowe, “Distinctive image features from scale-invariant keypoints,” International Journal of Computer Vision , vol. 60, no. 2, pp. 91–110, 2004
2004
Earlier work this paper cites.
G. Yan, J.-F. Mas, B. Maathuis, Z. Xiangmin, and P. Van Dijk, “Comparison of pixel-based and object-oriented image classification approaches¡ªa case study in a coal fire area, wuda, inner mongolia, china,” International Journal of Remote Sensing , vol. 27, no. 18, pp. 4039–4055, 2006
2006
Earlier work this paper cites.
S. Lazebnik, C. Schmid, and J. Ponce, “Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories,” in Proc. IEEE Conference on Computer Vision and Pattern Recognition , vol. 2, 2006, pp. 2169–2178
2006
Earlier work this paper cites.
A. Bosch, A. Zisserman, and X. Muñoz, “Scene classification via plsa,” in Proc. European Conference on Computer Vision , 2006, pp. 517–530
2006
Earlier work this paper cites.
G. E. Hinton, S. Osindero, and Y. W. Teh, “A fast learning algorithm for deep belief nets.” Neural Computation , vol. 18, no. 7, pp. 1527–54, 2006
2006
Earlier work this paper cites.
F. Perronnin and C. Dance, “Fisher kernels on visual vocabularies for image categorization,” in Proc. IEEE Conference on Computer Vision and Pattern Recognition . IEEE, 2007, pp. 1–8
2007
Earlier work this paper cites.
Y. Yang and S. Newsam, “Comparing sift descriptors and gabor texture features for classification of remote sensed imagery,” in IEEE International Conference on Image Processing . IEEE, 2008, pp. 1852–1855
2008
Earlier work this paper cites.
B. Luo, J.-F. Aujol, Y. Gousseau, and S. Ladjal, “Indexing of satellite images with different resolutions by wavelet features,” IEEE Transactions on Image Processing , vol. 17, no. 8, pp. 1465–1472, 2008
2008
Earlier work this paper cites.
D. Tuia, F. Ratle, F. Pacifici, M. F. Kanevski, and W. J. Emery, “Active learning methods for remote sensing image classification,” IEEE Transactions on Geoscience and Remote Sensing , vol. 47, no. 7, pp. 2218–2232, 2009
2009
Earlier work this paper cites.
B. Luo, J.-F. Aujol, and Y. Gousseau, “Local scale measure from the topographic map and application to remote sensing images,” Multiscale modeling & simulation , vol. 8, no. 1, pp. 1–29, 2009
2009
Earlier work this paper cites.
J. Yang, K. Yu, Y. Gong, and T. Huang, “Linear spatial pyramid matching using sparse coding for image classification,” in Proc. IEEE Conference on Computer Vision and Pattern Recognition , 2009, pp. 1794–1801
2009
Earlier work this paper cites.
K. Yu, T. Zhang, and Y. Gong, “Nonlinear learning using local coordinate coding,” in Advances in Neural Information Processing Systems , 2009, pp. 2223–2231
2009
Earlier work this paper cites.
T. Blaschke, “Object based image analysis for remote sensing,” ISPRS journal of photogrammetry and remote sensing , vol. 65, no. 1, pp. 2–16, 2010
2010
Earlier work this paper cites.
J. A. dos Santos, O. A. B. Penatti, and R. da Silva Torres, “Evaluating the potential of texture and color descriptors for remote sensing image retrieval and classification.” in VISAPP (2) , 2010, pp. 203–208
2010
Earlier work this paper cites.
M. Liénou, H. Maître, and M. Datcu, “Semantic annotation of satellite images using latent dirichlet allocation,” IEEE Geoscience and Remote Sensing Letters , vol. 7, no. 1, pp. 28–32, 2010
2010
Earlier work this paper cites.
G.-S. Xia, W. Yang, J. Delon, Y. Gousseau, H. Sun, and H. Maître, “Structural high-resolution satellite image indexing,” in ISPRS TC VII Symposium-100 Years ISPRS , vol. 38, 2010, pp. 298–303
2010
Earlier work this paper cites.
Y. Yang and S. Newsam, “Bag-of-visual-words and spatial extensions for land-use classification,” in Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems . ACM, 2010, pp. 270–279
2010
Earlier work this paper cites.
F. Perronnin, J. Sánchez, and T. Mensink, “Improving the fisher kernel for large-scale image classification,” in Proc. European Conference on Computer Vision , 2010, pp. 143–156
2010
Earlier work this paper cites.
P. Vincent, H. Larochelle, I. Lajoie, Y. Bengio, and P.-A. Manzagol, “Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion,” The Journal of Machine Learning Research , vol. 11, pp. 3371–3408, 2010
2010
Earlier work this paper cites.
J. Wang, J. Yang, K. Yu, F. Lv, T. Huang, and Y. Gong, “Locality-constrained linear coding for image classification,” in Proc. IEEE Conference on Computer Vision and Pattern Recognition . IEEE, 2010, pp. 3360–3367
2010
Earlier work this paper cites.
2010
Cited alongside, same era.
V. Risojević and Z. Babić, “Aerial image classification using structural texture similarity,” in IEEE International Symposium on Signal Processing and Information Technology (ISSPIT) . IEEE, 2011, pp. 190–195
2011
Cited alongside, same era.
Y. Yang and S. Newsam, “Spatial pyramid co-occurrence for image classification,” in IEEE International Conference on Computer Vision (ICCV) . IEEE, 2011, pp. 1465–1472
2011
Cited alongside, same era.
D. Tuia, M. Volpi, L. Copa, M. Kanevski, and J. Muñoz-Marí, “A survey of active learning algorithms for supervised remote sensing image classification,” IEEE Journal of Selected Topics in Signal Processing , vol. 5, no. 3, pp. 606–617, 2011
2011
Cited alongside, same era.
R. Kusumaningrum, H. Wei, R. Manurung, and A. Murni, “Integrated visual vocabulary in latent dirichlet allocation–based scene classification for ikonos image,” Journal of Applied Remote Sensing , vol. 8, no. 1, pp. 083 690–083 690, 2014
2014
Later among the works it cites.
R. Negrel, D. Picard, and P.-H. Gosselin, “Evaluation of second-order visual features for land-use classification,” in International Workshop on Content-Based Multimedia Indexing (CBMI) . IEEE, 2014, pp. 1–5
2014
Later among the works it cites.
L. Zhao, P. Tang, and L. Huo, “A 2-d wavelet decomposition-based bag-of-visual-words model for land-use scene classification,” International Journal of Remote Sensing , vol. 35, no. 6, pp. 2296–2310, 2014
2014
Later among the works it cites.
L.-J. Zhao, P. Tang, and L.-Z. Huo, “Land-use scene classification using a concentric circle-structured multiscale bag-of-visual-words model,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , vol. 7, no. 12, pp. 4620–4631, 2014
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
S. W. Myint, P. Gober, A. Brazel, S. Grossman-Clarke, and Q. Weng, “Per-pixel vs. object-based classification of urban land cover extraction using high spatial resolution imagery,” Remote sensing of environment , vol. 115, no. 5, pp. 1145–1161, 2011
2011
Cited alongside, same era.
L. Chen, W. Yang, K. Xu, and T. Xu, “Evaluation of local features for scene classification using vhr satellite images,” in Joint Urban Remote Sensing Event (JURSE) . IEEE, 2011, pp. 385–388
2011
Cited alongside, same era.
D. Dai and W. Yang, “Satellite image classification via two-layer sparse coding with biased image representation,” IEEE Geoscience and Remote Sensing Letters , vol. 8, no. 1, pp. 173–176, 2011
2011
Cited alongside, same era.
V. Risojević, S. Momić, and Z. Babić, “Gabor descriptors for aerial image classification,” in Adaptive and Natural Computing Algorithms . Springer, 2011, pp. 51–60
2011
Cited alongside, same era.
G. Sheng, W. Yang, T. Xu, and H. Sun, “High-resolution satellite scene classification using a sparse coding based multiple feature combination,” International journal of remote sensing , vol. 33, no. 8, pp. 2395–2412, 2012
2012
Cited alongside, same era.
V. Risojević and Z. Babić, “Orientation difference descriptor for aerial image classification,” in International Conference on Systems, Signals and Image Processing (IWSSIP) . IEEE, 2012, pp. 150–153
2012
Cited alongside, same era.
D. C. Duro, S. E. Franklin, and M. G. Dubé, “A comparison of pixel-based and object-based image analysis with selected machine learning algorithms for the classification of agricultural landscapes using spot-5 hrg imagery,” Remote Sensing of Environment , vol. 118, pp. 259–272, 2012
2012
Cited alongside, same era.
S. Mallat and L. Sifre, “Combined scattering for rotation invariant texture analysis,” submitted to ESANN , 2012
2012
Cited alongside, same era.
2014
Later among the works it cites.
Q. Zhu, Y. Zhong, and L. Zhang, “Multi-feature probability topic scene classifier for high spatial resolution remote sensing imagery,” in IEEE International Geoscience and Remote Sensing Symposium (IGARSS) . IEEE, 2014, pp. 2854–2857
2014
Later among the works it cites.
Y. Zhong, J. Zhao, and L. Zhang, “A hybrid object-oriented conditional random field classification framework for high spatial resolution remote sensing imagery,” IEEE Transactions on Geoscience and Remote Sensing , vol. 52, no. 11, pp. 7023–7037, 2014
2014
Later among the works it cites.
Y. Jia, E. Shelhamer, J. Donahue, S. Karayev, J. Long, R. Girshick, S. Guadarrama, and T. Darrell, “Caffe: Convolutional architecture for fast feature embedding,” in Proceedings of the ACM International Conference on Multimedia . ACM, 2014, pp. 675–678
2014
Later among the works it cites.
2014
Later among the works it cites.
2014
Later among the works it cites.
G. Cheng, J. Han, L. Guo, Z. Liu, S. Bu, and J. Ren, “Effective and efficient midlevel visual elements-oriented land-use classification using vhr remote sensing images,” IEEE Transactions on Geoscience and Remote Sensing , vol. 53, no. 8, pp. 4238–4249, 2015
2015
Later among the works it cites.
F. Hu, G.-S. Xia, J. Hu, and L. Zhang, “Transferring deep convolutional neural networks for the scene classification of high-resolution remote sensing imagery,” Remote Sensing , vol. 7, no. 11, pp. 14 680–14 707, 2015
2015
Later among the works it cites.
S. Chen and Y. Tian, “Pyramid of spatial relatons for scene-level land use classification,” IEEE Transactions on Geoscience and Remote Sensing , vol. 53, no. 4, pp. 1947–1957, 2015
2015
Later among the works it cites.
X. Chen, T. Fang, H. Huo, and D. Li, “Measuring the effectiveness of various features for thematic information extraction from very high resolution remote sensing imagery,” IEEE Transactions on Geoscience and Remote Sensing , vol. 53, no. 9, pp. 4837–4851, 2015
2015
Later among the works it cites.
Y. Zhong, M. Cui, Q. Zhu, and L. Zhang, “Scene classification based on multifeature probabilistic latent semantic analysis for high spatial resolution remote sensing images,” Journal of Applied Remote Sensing , vol. 9, no. 1, pp. 095 064–095 064, 2015
2015
Later among the works it cites.
H. Sridharan and A. Cheriyadat, “Bag of lines (bol) for improved aerial scene representation,” IEEE Geoscience and Remote Sensing Letters , vol. 12, no. 3, pp. 676–680, 2015
2015
Later among the works it cites.
J. Hu, G.-S. Xia, F. Hu, and L. Zhang, “A comparative study of sampling analysis in the scene classification of optical high-spatial resolution remote sensing imagery,” Remote Sensing , vol. 7, no. 11, pp. 14 988–15 013, 2015
2015
Later among the works it cites.
J. Hu, T. Jiang, X. Tong, G.-S. Xia, and L. Zhang, “A benchmark for scene classification of high spatial resolution remote sensing imagery,” in IEEE International Geoscience and Remote Sensing Symposium (IGARSS) . IEEE, 2015, pp. 5003–5006
2015
Later among the works it cites.
F. Hu, G.-S. Xia, Z. Wang, X. Huang, L. Zhang, and H. Sun, “Unsupervised feature learning via spectral clustering of multidimensional patches for remotely sensed scene classification,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , vol. 8, no. 5, pp. 2015–2030, 2015
2015
Later among the works it cites.
2015
Later among the works it cites.
O. A. B. Penatti, K. Nogueira, and J. A. dos Santos, “Do deep features generalize from everyday objects to remote sensing and aerial scenes domains?” in Proc. IEEE Conference on Computer Vision and Pattern Recognition , June 2015
2015
Later among the works it cites.
F. Luus, B. Salmon, F. van den Bergh, and B. Maharaj, “Multiview deep learning for land-use classification,” IEEE Geoscience and Remote Sensing Letters , vol. 12, no. 12, pp. 2448–2452, 2015
2015
Later among the works it cites.
W. Yang, X. Yin, and G.-S. Xia, “Learning high-level features for satellite image classification with limited labeled samples,” IEEE Transactions on Geoscience and Remote Sensing , vol. 53, no. 8, pp. 4472–4482, 2015
2015
Later among the works it cites.
F. Zhang, B. Du, and L. Zhang, “Saliency-guided unsupervised feature learning for scene classification,” IEEE Transactions on Geoscience and Remote Sensing , vol. 53, no. 4, pp. 2175–2184, 2015
2015
Later among the works it cites.
——, “Scene classification via a gradient boosting random convolutional network framework,” IEEE Transactions on Geoscience and Remote Sensing , vol. PP, no. 99, pp. 1–10, 2015
2015
Later among the works it cites.
Y. Zhong, Q. Zhu, and L. Zhang, “Scene classification based on the multifeature fusion probabilistic topic model for high spatial resolution remote sensing imagery,” IEEE Transactions on Geoscience and Remote Sensing , vol. 53, no. 11, pp. 6207–6222, 2015
2015
Later among the works it cites.
C. Chen, B. Zhang, H. Su, W. Li, and L. Wang, “Land-use scene classification using multi-scale completed local binary patterns,” Signal, Image and Video Processing , pp. 1–8, 2015
2015
Later among the works it cites.
Q. Zou, L. Ni, T. Zhang, and Q. Wang, “Deep learning based feature selection for remote sensing scene classification,” Geoscience and Remote Sensing Letters, IEEE , vol. 12, no. 11, pp. 2321–2325, 2015
2015
Later among the works it cites.
J. Zhao, Y. Zhong, and L. Zhang, “Detail-preserving smoothing classifier based on conditional random fields for high spatial resolution remote sensing imagery,” IEEE Transactions on Geoscience and Remote Sensing , vol. 53, no. 5, pp. 2440–2452, 2015
2015
Later among the works it cites.
O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, A. C. Berg, and L. Fei-Fei, “ImageNet Large Scale Visual Recognition Challenge,” International Journal of Computer Vision , pp. 1–42, April 2015
2015
Later among the works it cites.