[Badrinarayanan, V., Kendall, A. and Cipolla, R. (2017). Segnet: A deep convolutional encoder-decoder architecture for image segmentation, IEEE Transactions on Geoscience & Remote Sensing39(12): 2481–2495.10.1109/TPAMI.2016.264461528060704]Search in Google Scholar
[Bei, Z., Bo, H. and Zhong, Y. (2017). Transfer learning with fully pretrained deep convolution networks for land-use classification, IEEE Geoscience & Remote Sensing LettersPP(99): 1–5.]Search in Google Scholar
[Caesar, H., Uijlings, J. and Ferrari, V. (2018). Coco-stuff: Thing and stuff classes in context, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA, pp. 1209–1218.]Search in Google Scholar
[Carreira, J., Rui, C., Batista, J. and Sminchisescu, C. (2012). Semantic segmentation with second-order pooling, European Conference on Computer Vision, Firenze, Italy, pp. 430–443.]Search in Google Scholar
[Carreira, J. and Sminchisescu, C. (2011). CPMC: Automatic object segmentation using constrained parametric min-cuts, IEEE Transactions on Pattern Analysis and Machine Intelligence34(7): 1312–1328.10.1109/TPAMI.2011.23122144523]Search in Google Scholar
[Castelluccio, M., Poggi, G., Sansone, C. and Verdoliva, L. (2015). Land use classification in remote sensing images by convolutional neural networks, Acta Ecologica Sinica28(2): 627–635.]Search in Google Scholar
[Chandra, S. and Kokkinos, I. (2016). Fast, exact and multi-scale inference for semantic image segmentation with deep Gaussian CRFS, European Conference on Computer Vision, Amsterdam, The Netherlands, pp. 402–418.]Search in Google Scholar
[Chandra, S., Usunier, N. and Kokkinos, I. (2017). Dense and low-rank Gaussian CRFs using deep embeddings, IEEE International Conference on Computer Vision, Honolulu, HI, USA, pp. 5103–5112.]Search in Google Scholar
[Chao, P., Zhang, X., Gang, Y., Luo, G. and Jian, S. (2017). Large kernel matters—Improve semantic segmentation by global convolutional network, IEEE Conference on Computer Vision and Pattern Recognition, Venice, Italy, pp. 4353–4361.]Search in Google Scholar
[Chen, L.C., Papandreou, G., Kokkinos, I., Murphy, K. and Yuille, A.L. (2017a). DeepLab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs, IEEE Transactions on Pattern Analysis & Machine Intelligence40(4): 834–848.10.1109/TPAMI.2017.269918428463186]Search in Google Scholar
[Chen, L.-C., Papandreou, G., Schroff, F. and Adam, H. (2017b). Rethinking atrous convolution for semantic image segmentation, arXiv 1706.05587.]Search in Google Scholar
[Chen, L.C., Zhu, Y., Papandreou, G., Schroff, F. and Adam, H. (2018). Encoder-decoder with atrous separable convolution for semantic image segmentation, Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany, pp. 801–818.]Search in Google Scholar
[Cleve, C., Kelly, M., Kearns, F.R. and Moritz, M. (2008). Classification of the wildland–urban interface: A comparison of pixel- and object-based classifications using high-resolution aerial photography, Computers Environment & Urban Systems32(4): 317–326.10.1016/j.compenvurbsys.2007.10.001]Search in Google Scholar
[Cordts, M., Omran, M., Ramos, S., Rehfeld, T., Enzweiler, M., Benenson, R., Franke, U., Roth, S. and Schiele, B. (2016). The cityscapes dataset for semantic urban scene understanding, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA, pp. 3213–3223.]Search in Google Scholar
[Fu, J., Jing, L., Wang, Y. and Lu, H. (2019). Stacked deconvolutional network for semantic segmentation, IEEE Transactions on Image ProcessingPP(99): 1–1.10.1109/TIP.2019.289546030703024]Search in Google Scholar
[Fulkerson, B., Vedaldi, A. and Soatto, S. (2009). Class segmentation and object localization with superpixel neighborhoods, IEEE International Conference on Computer Vision, Kyoto, Japan, pp. 670–677.]Search in Google Scholar
[Gang, C., Weng, Q., Hay, G.J. and He, Y. (2018). Geographic object-based image analysis (geobia): Emerging trends and future opportunities, GIScience & Remote Sensing55(2): 159–182.10.1080/15481603.2018.1426092]Search in Google Scholar
[Gibbons, J. and Chakraborti, S. (2011). The Wilcoxon rank-sum test and confidence interval, Nonparametric Statistical Inference59(4): 290–293.]Search in Google Scholar
[Gong, C., Han, J., Lei, G., Liu, Z., Bu, S. and Ren, J. (2015). Effective and efficient midlevel visual elements-oriented land-use classification using VHR remote sensing images, IEEE Transactions on Geoscience & Remote Sensing53(8): 4238–4249.10.1109/TGRS.2015.2393857]Search in Google Scholar
[Grauman, K. and Darrell, T. (2005). Pyramid match kernels: Discriminative classification with sets of image features, 10th IEEE International Conference on Computer Vision, Beijing, China, pp. 1458–1465.]Search in Google Scholar
[He, K., Zhang, X., Ren, S. and Sun, J. (2015). Spatial pyramid pooling in deep convolutional networks for visual recognition, IEEE Transactions on Pattern Analysis & Machine Intelligence37(9): 1904–16.10.1109/TPAMI.2015.238982426353135]Search in Google Scholar
[Kim, J.H., Lee, H., Hong, S.J., Kim, S., Park, J., Hwang, J.Y. and Choi, J.P. (2018). Objects segmentation from high-resolution aerial images using U-Net with pyramid pooling layers, IEEE Geoscience and Remote Sensing Letters16(1): 115-119.10.1109/LGRS.2018.2868880]Search in Google Scholar
[Lazebnik, S., Schmid, C. and Ponce, J. (2006). Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories, IEEE Computer Society Conference on Computer Vision and Pattern Recognition, New York, NY, USA, pp. 2169–2178.]Search in Google Scholar
[Lin, G., Milan, A., Shen, C. and Reid, I. (2017). Refinenet: Multi-path refinement networks with identity mappings for high-resolution semantic segmentation, IEEE International Conference on Computer Vision, Venice, Italy, pp. 1925–1934.]Search in Google Scholar
[Liu, W., Rabinovich, A. and Berg, A. (2015). Parsenet: Looking wider to see better, arXiv 1506.04579.]Search in Google Scholar
[Long, J., Shelhamer, E. and Darrell, T. (2015). Fully convolutional networks for semantic segmentation, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA, pp. 3431–3440.]Search in Google Scholar
[Maggiori, E., Tarabalka, Y., Charpiat, G. and Alliez, P. (2016). Convolutional neural networks for large-scale remote sensing image classification, IEEE Transactions on Geoscience & Remote Sensing55(2): 645–657.10.1109/TGRS.2016.2612821]Search in Google Scholar
[Marcos, D., Volpi, M., Kellenberger, B. and Tuia, D. (2018). Land cover mapping at very high resolution with rotation equivariant CNNs: Towards small yet accurate models, ISPRS Journal of Photogrammetry and Remote Sensing145(5): 96–107.10.1016/j.isprsjprs.2018.01.021]Search in Google Scholar
[Marmanis, D., Schindler, K., Wegner, J., Galliani, S., Datcu, M. and Stilla, U. (2016). Classification with an edge: Improving semantic image segmentation with boundary detection, ISPRS Journal of Photogrammetry and Remote Sensing135(7): 158–172.10.1016/j.isprsjprs.2017.11.009]Search in Google Scholar
[Mi, Z. and Hu, X. (2017). Learning dual multi-scale manifold ranking for semantic segmentation of high-resolution images, Remote Sensing9(5): 500.10.3390/rs9050500]Search in Google Scholar
[Miao, L., Zang, S., Bing, Z., Li, S. and Wu, C. (2014). A review of remote sensing image classification techniques: The role of spatio-contextual information, European Journal of Remote Sensing47(1): 389–411.10.5721/EuJRS20144723]Search in Google Scholar
[Noh, H., Hong, S. and Han, B. (2015). Learning deconvolution network for semantic segmentation, IEEE International Conference on Computer Vision, Santiago, Chile, pp. 1520–1528.]Search in Google Scholar
[Peng, D., Zhang, Y. and Guan, H. (2019). End-to-end change detection for high resolution satellite images using improved UNet++, Remote Sensing11(11): 1382.10.3390/rs11111382]Search in Google Scholar
[Pohlen, T., Hermans, A., Mathias, M. and Leibe, B. (2017). Full-resolution residual networks for semantic segmentation in street scenes, IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA, pp. 4151–4160.]Search in Google Scholar
[Razavian, A.S., Azizpour, H., Sullivan, J. and Carlsson, S. (2014). CNN features off-the-shelf: An astounding baseline for recognition, IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA, pp. 806–813.]Search in Google Scholar
[Ren, S., Girshick, R., Girshick, R. and Sun, J. (2017). Faster r-CNN: Towards real-time object detection with region proposal networks, IEEE Transactions on Pattern Analysis & Machine Intelligence39(6): 1137–1149.10.1109/TPAMI.2016.257703127295650]Search in Google Scholar
[Ronneberger, O., Fischer, P. and Brox, T. (2015). U-Net: Convolutional networks for biomedical image segmentation, International Conference on Medical Image Computing & Computer-Assisted Intervention, Munich, Germany, pp. 234–241.]Search in Google Scholar
[Scott, G.J., England, M.R., Starms, W.A., Marcum, R.A. and Davis, C.H. (2017). Training deep convolutional neural networks for land-cover classification of high-resolution imagery, IEEE Geoscience & Remote Sensing Letters14(9): 1638–1642.10.1109/LGRS.2017.2722988]Search in Google Scholar
[Sharma, A., Liu, X., Yang, X. and Shi, D. (2017). A patch-based convolutional neural network for remote sensing image classification, Neural Networks95(7): 19.10.1016/j.neunet.2017.07.01728843092]Search in Google Scholar
[Shotton, J., Johnson, M. and Cipolla, R. (2008). Semantic texton forests for image categorization and segmentation, Conference on Computer Vision and Pattern Recognition, Anchorage, AK, USA, pp. 1–8.]Search in Google Scholar
[Shotton, J., Winn, J., Rother, C. and Criminisi, A. (2009). Textonboost for image understanding: Multi-class object recognition and segmentation by jointly modeling texture, layout, and context, International Journal of Computer Vision81(1): 2–23.10.1007/s11263-007-0109-1]Search in Google Scholar
[Simonyan, K. and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition, arXiv 1409.1556.]Search in Google Scholar
[Sivic and Zisserman (2003). Video Google: A text retrieval approach to object matching in videos, Proceedings of the 9th IEEE International Conference on Computer Vision, Nice, France, Vol.2, pp. 1470–1477.]Search in Google Scholar
[Tao, L., Abd-Elrahman, A., Morton, J. and Wilhelm, V.L. (2018). Comparing fully convolutional networks, random forest, support vector machine, and patch-based deep convolutional neural networks for object-based wetland mapping using images from small unmanned aircraft system, GIScience & Remote Sensing55(2): 243–264.10.1080/15481603.2018.1426091]Search in Google Scholar
[Vemulapalli, R., Tuzel, O., Liu, M.Y. and Chellappa, R. (2016). Gaussian conditional random field network for semantic segmentation, Computer Vision & Pattern Recognition, Las Vegas, NV, USA, pp. 3224–3233.]Search in Google Scholar
[Volpi, M. and Tuia, D. (2017). Dense semantic labeling of subdecimeter resolution images with convolutional neural networks, IEEE Transactions on Geoscience and Remote Sensing55(2): 881–893.10.1109/TGRS.2016.2616585]Search in Google Scholar
[Yang, H., Yu, B., Luo, J. and Chen, F. (2019). Semantic segmentation of high spatial resolution images with deep neural networks, GIScience & Remote Sensing56(5): 1–20.10.1080/15481603.2018.1564499]Search in Google Scholar
[Zhang, C., Xin, P., Li, H., Gardiner, A. and Atkinson, P.M. (2018a). A hybrid MLP-CNN classifier for very fine resolution remotely sensed image classification, ISPRS Journal of Photogrammetry & Remote Sensing140(7): 133–144.10.1016/j.isprsjprs.2017.07.014]Search in Google Scholar
[Zhang, P., Ke, Y., Zhang, Z., Wang, M., Li, P. and Zhang, S. (2018b). Urban land use and land cover classification using novel deep learning models based on high spatial resolution satellite imagery, IEEE Transactions on Geo-science & Remote Sensing18(11): 3717.10.3390/s18113717626352830388781]Search in Google Scholar
[Zhao, H., Shi, J., Qi, X., Wang, X. and Jia, J. (2017). Pyramid scene parsing network, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA, pp. 6230–6239.]Search in Google Scholar
[Zhao, W. and Du, S. (2016). Learning multiscale and deep representations for classifying remotely sensed imagery, ISPRS Journal of Photogrammetry & Remote Sensing113(3): 155–165.10.1016/j.isprsjprs.2016.01.004]Search in Google Scholar
[Zhou, B., Hang, Z., Puig, X., Fidler, S., Barriuso, A. and Torralba, A. (2017). Scene parsing through ADE20K dataset, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA, pp. 633–641.]Search in Google Scholar
[Zhuowen, T. and Xiang, B. (2010). Auto-context and its application to high-level vision tasks and 3D brain image segmentation, IEEE Transactions on Pattern Analysis & Machine Intelligence32(10): 1744–1757.10.1109/TPAMI.2009.18620724753]Search in Google Scholar