Thiết kế Mạng Nơron Tích Chập Sâu cho phân loại chữ ký theo loại của hình ảnh vệ tinh panchromatic thô

Bhosle K, Musande V (2019) Evaluation of deep learning CNN model for land use land cover classification and crop identification using hyperspectral remote sensing images. J Indian Soc Remote Sens 47:1949–1958. https://doi.org/10.1007/s12524-019-01041-2 Bishop CM (2006) Pattern recognition and machine learning. Springer, New York Chen Y, Wang Q, Wang Y, Duan S, Xu M, Li Z (2016) A spectral signature shape-based algorithm for Landsat image classification. ISPRS Int J Geo Inf 5:154 Chen G, Zhang X, Tan X, Cheng Y, Dai F, Zhu K, Gong Y, Wang Q (2018) Training small networks for the scene classification of remote sensing images via knowledge distillation. Remote Sens 10(5):719 Chen S, Jin M, Ding J (2020) Hyperspectral remote sensing image classification based on dense residual three-dimensional convolutional neural network. Multimed Tools Appl. https://doi.org/10.1007/s11042-020-09480-7 Cheng G, Han J, Lu X (2017) Remote sensing image scene classification: Benchmark and state of the art. Proc IEEE 105(10):1865–1883 Cheng G, Yang C, Yao X, Guo L, Han J (2018) When deep learning meets metric learning: remote sensing image scene classification via learning discriminative CNN. IEEE Trans Geosci Remote Sens 56(5):2811–2821 Dalal N, Triggs B (2005) Histograms of oriented gradients for human detection, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) 1:886–893. https://doi.org/10.1109/CVPR.2005.177 Ding L, Li H, Hu C, Zhang W, Wang S (2018) Alexnet feature extraction and multi-kernel learning for object oriented classification. ISPRS -Iint Arch Photogramm, Remote Sens Spatial Inform Sci XLII-3:277–281. https://doi.org/10.5194/isprs-archives-XLII-3-277-2018 Djerriri K, Sofia A, Karoui MS, Adjoudf R (2018) Enhancing the Classification of Remote Sensing Data Using Multiband Compact Texture Unit Descriptor and Deep Convolutional Neural Network. IGARSS 2018–2018 IEEE international geoscience and remote sensing symposium, Valencia, pp 2479–2482. https://doi.org/10.1109/IGARSS.2018.8518501 Dong C et al (2015) Image super-resolution using deep convolutional networks. TPAMI 38(2):295–307 Gao H, Liu Z, van der Maaten L, Weinberger KQ (2018) Densely Connected Convolutional Networks, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2261–2269. https://doi.org/10.1109/CVPR.2017.243 Garzonio R, Di Mauro B, Colombo R, Cogliati S (2017) Surface reflectance and Sun-induced fluorescence spectroscopy measurements using a small hyperspectral UAS. Remote Sens 9(5):472. https://doi.org/10.3390/rs9050472 Ghazouani F, Farah IR, Basel Solaiman, (2018), Semantic Remote Sens Scenes Interpretation Change Interpretation, in Ontology in Information Science. https://doi.org/10.5772/intechopen.72730 Gu Y, Wang Y, Li Y (2019) A survey on deep learning-driven remote sensing image scene understanding: scene classification, scene retrieval, and scene-guided object detection. Appl Sci 9:2110 Haralick RM, Shanmugam K, Dinstein IH (1973) Textural features for image classification. IEEE Trans Syst Man Cybernet 6:610–621 He K, Zhang X, Ren S, Sun J (2016) Deep Residual Learning for Image Recognition, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770–778. https://doi.org/10.1109/CVPR.2016.90 Hua Y, Mou L, Lin J, Heidler K, Zhu XX (2021) Aerial scene understanding in the wild: Multi-scene recognition via prototype-based memory networks, ISPRS Journal of Photogrammetry and Remote Sensing 177:89–102. https://doi.org/10.1016/j.isprsjprs.2021.04.006 Hung S-C, Wu H-C, Tseng M-H (2020) Remote Sensing Scene Classification and Explanation Using RSSCNet and LIME. Appl Sci 10:6151. https://doi.org/10.3390/app10186151 Jain AK, Ratha NK, Lakshmanan S (1997) Object detection using Gabor filters. Pattern Recogn 30(2):295–309 Jegou H, Perronnin F, Douze M, Sanchez J, Perez P, Schmid C (2011) Aggregating local image descriptors into compact codes. IEEE Trans Pattern Anal Mach Intell 34(9):1704–1716 Jiang J, Feng X, Liu F, Xu Y, and Huang H (2019) Multi-Spectral RGB-NIR Image Classification Using Double-Channel CNN, in IEEE Access 7:20607–20613. https://doi.org/10.1109/ACCESS.2019.2896128 Kingma D, Ba J (2014) Adam: A Method for Stochastic Optimization. Proceedings of the 3rd International Conference on Learning Representations (ICLR 2015) Krizhevsky A, Sutskever I, Hinton GE (2012) ImageNet classification with deep convolutional neural networks, in Proc Adv Neural Inf Process Syst pp. 1097–1105 Lazebnik S, Schmid C, Ponce J (2006) Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06), pp. 2169-2178. https://doi.org/10.1109/CVPR.2006.68 Li Y, Zhang H, Xue X, Jiang Y, Shen Q (2018) Deep learning for remote sensing image classification: a survey. WIREs Data Min Knowl Discov 8:e1264. https://doi.org/10.1002/widm.1264 Li T, Leng J, Kong L et al (2019) DCNR: deep cube CNN with random forest for hyperspectral image classification. Multimed Tools Appl 78:3411–3433. https://doi.org/10.1007/s11042-018-5986-5 Li H, Dou X, Tao C, Wu Z, Chen J, Peng J, Deng M, Zhao L (2020) RSI-CB: a large-scale remote sensing image classification benchmark using crowdsourced data. Sensors. 20:1594. https://doi.org/10.3390/s20061594 Lin L, Chen C, Xu T (2020) Spatial-spectral hyperspectral image classification based on information measurement and CNN EURASIP. J Wireless Commun Netw. https://doi.org/10.1186/s13638-020-01666-9 Lowe DG (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vis 60(2):91–110 Merugu S, Tiwari A, Sharma SK (2020) Spatial–spectral image classification with edge preserving method. J Indian Soc Remote Sens. https://doi.org/10.1007/s12524-020-01265-7 Mhangara P, Mapurisa W, Mudau N (2020) Comparison of Image Fusion Techniques Using Satellite Pour l’Observation de la Terre (SPOT) 6 Satellite Imagery. Appl Sci 10:1881. https://doi.org/10.3390/app10051881 Minetto R, Segundo MP, Sarkar S (2019) Hydra: An ensemble of convolutional neural networks for geospatial land classification. IEEE Trans Geosci Remote Sens 57(9):6530–6541 Nagi J, et al (2011) Maxpooling convolutional neural networks for vision-based hand gesture recognition, 2011 IEEE International Conference on Signal and Image Processing Applications (ICSIPA), pp. 342–347. https://doi.org/10.1109/ICSIPA.2011.6144164 Nair V, Hinton GE.(2010) Rectified linear units improve restricted Boltzmann machines. In Proceedings of the 27th international conference on machine learning (ICML-10), pp. 807–814 Ojala T, Pietikainen M, Maenpaa T (2002) Multiresolution grayscale and rotation invariant texture classification with local binary patterns. IEEE Trans Pattern Anal Mach Intell 24(7):971–987 Oliva A, Torralba A (2001) Modeling the shape of the scene: a holistic representation of the spatial envelope. Int J Comput Vis 42(3):145–175 Paul A, Bhoumik S, Chaki N (2020) SSNET: an improved deep hybrid network for hyperspectral image classification. Neural Comput Applic. https://doi.org/10.1007/s00521-020-05069-1 Penatti OA, Nogueira K, Dos Santos JA (2015) Do deep features generalize from everyday objects to remote sensing and aerial scenes domains?, 2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 44-51. https://doi.org/10.1109/CVPRW.2015.7301382 Perronnin F, Sanchez J, Mensink T (2010) Improving the Fisher Kernel for Large-Scale Image Classification. In: Daniilidis K., Maragos P., Paragios N. (eds) Computer Vision – ECCV 2010. ECCV 2010. Lecture Notes in Computer Science 6314. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15561-1_11 Rohith G, Kumar LS (2020) Remote sensing signature classification of agriculture detection using deep convolution network models. In: Bhattacharjee A, Borgohain S, Soni B, Verma G, Gao XZ (eds) Machine learning, image processing, network security, and data sciences. MIND 2020. Communications in Computer and Information Science, 1240th edn. Springer, Singapore. https://doi.org/10.1007/978-981-15-6315-7_28 Rohith G, Kumar LS (2021) Super-resolution decision-making tool using deep convolution neural Networks for panchromatic images. Multimed Tools Appl. https://doi.org/10.1007/s11042-021-10861-9 Romero A, Gatta C, Camps-Valls G (2016) Unsupervised deep feature extraction for remote sensing image classification. IEEE Trans Geosci Remote Sens 54(3):1349–1362. https://doi.org/10.1109/TGRS.2015.2478379 Simonyan K, Zisserman A (2015) Very Deep Convolutional Networks for Large-Scale Image Recognition. The 3rd International Conference on Learning Representations (ICLR2015). https://arxiv.org/abs/1409.1556 Song J, Gao S, Zhu Y, Ma C (2019) A survey of remote sensing image classification based on CNNs. Big Earth Data 3:1–23. https://doi.org/10.1080/20964471.2019.1657720 Su T (2019) Superpixel-based principal component analysis for high-resolution remote sensing image classification. Multimed Tools Appl 78:34173–34191. https://doi.org/10.1007/s11042-019-08224-6 Sumbul G, Charfuelan M, Demir B, and Markl V, (2019) “Bigearthnet: A large-scale benchmark archive for remote sensing image understanding,” in Proceedings of the IEEE International Geoscience and Remote Sensing Symposium, pp. 5901–5904 Sun H, Li S, Zheng X, Lu X, (2020) Remote Sensing Scene Classification by Gated Bidirectional Network, in IEEE Transactions on Geoscience and Remote Sensing 58(1):82–96. https://doi.org/10.1109/TGRS.2019.2931801 Swain MJ, Ballard DH (1991) Color indexing. Int J Comput Vis 7(1):11–32 Unnikrishnan A, Sowmya V, Soman KP (2019) Deep learning architectures for land cover classification using red and near-infrared satellite images. Multimed Tools Appl 78:18379–18394. https://doi.org/10.1007/s11042-019-7179-2 Wang G, Fan B, Xiang S, Pan C (2017) Aggregating rich hierarchical features for scene classification in remote sensing imagery. IEEE J Sel Topics Appl Earth Observ Remote Sens 10(9):4104–4115 Wang Q, Liu S, Chanussot J, Li X (2018) Scene classification with recurrent attention of vhr remote sensing images. IEEE Trans Geosci Remote Sens 57(2):1155–1167 Wang Q, Liu S, Chanussot J, Li X (2019) Scene Classification With Recurrent Attention of VHR Remote Sensing Images, in IEEE Transactions on Geoscience and Remote Sensing 57(2):1155–1167. https://doi.org/10.1109/TGRS.2018.2864987 Wang Q, Huang W, Zhang X, Li X (2021) Word–Sentence Framework for Remote Sensing Image Captioning," in IEEE Transactions on Geoscience and Remote Sensing 59(12):10532–10543. https://doi.org/10.1109/TGRS.2020.3044054 Wang Q, Huang W, Xiong Z, Li X (2020) Looking Closer at the Scene: Multiscale Representation Learning for Remote Sensing Image Scene Classification, in IEEE Transactions on Neural Networks and Learning Systems. https://doi.org/10.1109/TNNLS.2020.3042276 Xia G-S, Hu J, Hu F, Shi B, Bai X, Zhong Y, Zhang L, Lu X (2017) Aid: a benchmark data set for performance evaluation of aerial scene classification. IEEE Trans Geosci Remote Sens 55(7):3965–3981 Yang Y, Newsam S (2010) Bag-of-visual-words and spatial extensions for land-use classification 270–279. https://doi.org/10.1145/1869790.1869829 Yang S, Ramanan D (2015) Multi-scale Recognition with DAG-CNNs, 2015 IEEE International Conference on Computer Vision (ICCV), pp. 1215–1223. https://doi.org/10.1109/ICCV.2015.144 You Y, Cao J, Zhou W (2020) A Survey of Change Detection Methods Based on Remote Sensing Images for Multi-Source and Multi-Objective Scenarios. Remote Sens 12:2460. https://doi.org/10.3390/rs12152460 Zhang F, Du B, Zhang L (2015) Scene classification via a gradient boosting random convolutional network framework. IEEE Trans Geosci Remote Sens 54(3):1793–1802 Zhao B, Zhong Y, Xia G-S, Zhang L (2015) Dirichlet-derived multiple topic scene classification model for high spatial resolution remote sensing imagery. IEEE Trans Geosci Remote Sens 54(4):2108–2123 Zhao L, Tang P, Huo L (2016) Feature significance-based multi-bag of-visual-words model for remote sensing image scene classification. J Appl Remote Sens 10(3):035004 Zhao L, Zhang W, Tang P (2019) Analysis of the inter-dataset representation ability of deep features for high spatial resolution remote sensing image scene classification. Multimed Tools Appl 78:9667–9689. https://doi.org/10.1007/s11042-018-6548-6 Zou Q, Ni L, Zhang T, Wang Q (2015) Deep learning-based feature selection for remote sensing scene classification. IEEE Geosci Remote Sens Lett 12(11):2321–2325