357
Views
23
CrossRef citations to date
0
Altmetric
Research Article

Dilated multi-scale cascade forest for satellite image classification

ORCID Icon, , , ORCID Icon &
Pages 7779-7800 | Received 09 Jan 2020, Accepted 26 Mar 2020, Published online: 10 Aug 2020

References

  • An, Z., and Z. Shi. 2017. “Scene Learning for Cloud Detection on Remote-Sensing Images.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 8 (8): 4206–4222. doi:10.1109/JSTARS.2015.2438015.
  • Bai, T., D. Li, K. Sun, and Y. Chen. 2016. “Cloud Detection for High-Resolution Satellite Imagery Using Machine Learning and Multi-Feature Fusion.” Remote Sensing 8 (9): 715. doi:10.3390/rs8090715.
  • Buch, K. A., and C. H. Sun. 1995. “Thorne, Cloud Classification Using Whole-sky Imager Data.” Proceedings of the Fifth Atmospheric Radiation Measurement (ARM) Science Team Meeting, 35–39. doi:10.1016/0273-1177(95)00068-P.
  • Chen, Z., G. Zhang, J. Ning, and X. Tang. 2015. “An Automatic Cloud Detection Method for ZY-3 Satellite.” Acta Geodaetica Et Cartographica Sinica 44 (3): 292–300. doi:10.11947/j.AGCS.2015.20130384.
  • Dai, W., and W. Ji. 2014. “A Mapreduce Implementation of C4.5 Decision Tree Algorithm.” International Journal of Database Theory and Application 7 (1): 49–60. doi:10.14257/ijdta.2014.7.1.05.
  • Dou, P., Y. Chen, and H. Yue. 2018. “Remote-sensing Imagery Classification Using Multiple Classification Algorithm-based AdaBoost.” International Journal of Remote Sensing 39 3: 619. 639. doi:10.1080/01431161.2017.1390276.
  • Ghasemian, N., and M. Akhoondzadeh. 2018. “Introducing Two Random Forest Based Methods for Cloud Detection in Remote Sensing Images.” Advances in Space Research 2018: S0273117718303624. doi:10.1016/j.asr.2018.04.030.
  • Gouk, H. G. R., and A. M. Blake 2014. “Fast Sliding Window Classification with Convolutional Neural Networks.” Proceedings of the 29th International Conference on Image and Vision Computing, 114–118. doi:10.1145/2683405.2683429
  • Gu, Y., B. Wylie, S. Boyte, J. Picotte, D. Howard, K. Smith, and K. Nelson. 2016. “An Optimal Sample Data Usage Strategy to Minimize Overfitting and Underfitting Effects in Regression Tree Models Based on Remotely-sensed Data.” Remote Sensing 8 (11): 943. doi:10.3390/rs8110943.
  • Guo, Z., C. Li, Z. Wang, E. Kwok, and X. Wei. 2018. “A Cloud Boundary Detection Scheme Combined with Asic and CNN Using ZY3, GF1/2 Satellite Imagery.” International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3: 455–458. doi:10.5194/isprs-archives-XLII-3-455-2018.
  • Han, J., X. Yao, G. Cheng, X. Feng, and D. Xu. 2019. “P-CNN: Part-Based Convolutional Neural Networks for Fine-Grained Visual Categorization.” IEEE Transactions on Pattern Analysis and Machine Intelligence. doi:10.1109/TPAMI.2019.2933510.
  • Han, J., D. Zhang, G. Cheng, L. Guo, and J. Ren. 2014. “Object Detection in Optical Remote Sensing Images Based on Weakly Supervised Learning and High-level Feature Learning.” IEEE Transactions on Geoscience and Remote Sensing 53 (6): 3325–3337. doi:10.1109/TGRS.2014.2374218.
  • Haut, J. M., M. Paoletti, J. Plaza, and A. Plaza. 2017. “Cloud Implementation of the K-means Algorithm for Hyperspectral Image Analysis.” The Journal of Supercomputing 73 (1): 514–529. doi:10.1007/s11227-016-1896-3.
  • Hu, C., Y. Bai, and P. Tang. 2018. “Automatic Cloud Detection for GF-4 Series Images.” Journal of Remote Sensing 22 (1): 132–142. doi:10.11834/jrs.20186401.
  • Kang, X., G. Gao, Q. Hao, and S. Li. 2018. “A Coarse-to-Fine Method for Cloud Detection in Remote Sensing Images.” IEEE Geoscience and Remote Sensing Letters 16 (1): 110–114. doi:10.1109/LGRS.2018.2866499.
  • Karlgran, K., and N. Hkansson. 2018. “Characterization of AVHRR Global Cloud Detection Sensitivity Based on CALIPSO-CALIOP Cloud Optical Thickness Information: Demonstration of Results Based on the CM SAF CLARA-A2 Climate Data Record.” Atmospheric Measurement Techniques 11 (1): 633–649. doi:10.5194/amt-11-633-2018.
  • Kohavi, R. 1995. “A Study of Cross-validation and Bootstrap for Accuracy Estimation and Model Selection.” Ijcai 14 (2): 1137–1145.
  • Kui, W., Z. Rong, Y. Dong, and H. Zhang. 2013. “Cloud Detection for Remote Sensing Image Based on Edge Features and AdaBoost Classifier.” Remote Sensing Technology and Application 28 (2): 263–268. doi:10.11873/j..1004-0323.2013.2.263.
  • Lee, J. 1990. “A Neural Network Approach to Cloud Classification.” IEEE Transactions on Geoscience and Remote Sensing 28 (5): 846–855. doi:10.1109/36.58972.
  • Lerman, R. I., and S. Yitzhaki. 1984. “A Note on the Calculation and Interpretation of the Gini Index.” Economics Letters 15 (3–4): 363–368. doi:10.1016/0165-1765(84)90126-5.
  • Li, P., L. Dong, H. Xiao, and M. Xu. 2015. “A Cloud Image Detection Method Based on SVM Vector Machine.” Neurocomputing 169: 34–42. doi:10.1016/j.neucom.2014.09.102.
  • Liu, D., L. Han, and X. Han. 2016. “High Spatial Resolution Remote Sensing Image Classification Based on Deep Learning.” Acta Optica Sinica 36 (4): 0428001. doi:10.3788/AOS201636.0428001.
  • Liu, S., and M. Li. 2018. “Deep Multimodal Fusion for Ground-based Cloud Classification in Weather Station Networks.” EURASIP Journal on Wireless Communications and Networking 2018: 48. doi:10.1186/s13638-018-1062-0.
  • Lv, X., D. Ming, Y. Chen, and M. Wang. 2019. “Very High Resolution Remote Sensing Image Classification with SEEDS-CNN and Scale Effect Analysis for Superpixel CNN Classification.” International Journal of Remote Sensing 40 (2): 506–531. doi:10.1080/01431161.2018.1513666.
  • Maxwell, A. E., T. A. Warner, and F. Fang. 2017. “Implementation of Machine-learning Classification in Remote Sensing: An Applied Review.” International Journal of Remote Sensing 39 (9): 2784–2817. doi:10.1080/01431161.2018.1433343.
  • Pal, M. 2005. “Random Forest Classifier for Remote Sensing Classification.” International Journal of Remote Sensing 26 (1): 217–222. doi:10.1080/01431160412331269698.
  • Rényi, A. 1961. “On Measures of Entropy and Information.” In Proceedings of the Fourth Berkeley Symposium on Mathematical Statistics and Probability, Volume 1: Contributions to the Theory of Statistics. Regents of the University of California, Berkeley, California.
  • Safavian, S. R., and D. Landgrebe. 1991. “A Survey of Decision Tree Classifier Methodology.” IEEE Transactions on Systems, Man, and Cybernetics 21 (3): 660–674. doi:10.1109/21.97458.
  • Shen, H., Y. Lin, Q. Tian, K. Xu, and J. Jiao. 2018. “A Comparison of Multiple Classifier Combinations Using Different Voting-weights for Remote Sensing Image Classification.” International Journal of Remote Sensing 39 (11): 3705–3722. doi:10.1080/01431161.2018.1446566.
  • Xia, M., W. Liu, B. Shi, L. Weng, and J. Liu. 2019. “Cloud/snow Recognition for Multispectral Satellite Imagery Based on a Multidimensional Deep Residual Network.” International Journal of Remote Sensing 40 (1): 156–170. doi:10.1080/01431161.2018.1508917.
  • Xia, M., J. Qian, X. Zhang, J. Liu, and Y. Xu. 2020. “River Segmentation Based on Separable Attention Residual Network.” Journal of Applied Remote Sensing 14 (3): 032602. doi:10.1117/1.JRS.14.032602.
  • Xie, F., M. Shi, Z. Shi, J. Yin, and D. Zhao. 2017. “Multilevel Cloud Detection in Remote Sensing Images Based on Deep Learning.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 10 (8): 3631–3640. doi:10.1109/JSTARS.2017.2686488.
  • Xu, L., R. Niu, S. Fang, and Y. Dong. 2013. “Cloud Detection Based on Decision Tree over Tibetan Plateau with MODIS Data.” MIPPR 2013: Remote Sensing Image Processing, Geographic Information Systems, and Other Applications. doi:10.1117/12.2030399.
  • Xu, L., A. Wong, and D. A. Clausi. 2017. “A Novel Bayesian Spatial-Temporal Random Field Model Applied to Cloud Detection from Remotely Sensed Imagery.” IEEE Transactions on Geoscience and Remote Sensing 55 (9): 4913–4924. doi:10.1109/TGRS.2017.2692264.
  • Yang, J., W. Lu, Y. Ma, and W. Yao. 2012. “An Automated Cirrus Cloud Detection Method for a Ground-Based Cloud Image.” Journal of Atmospheric and Oceanic Technology 29 (4): 527–537. doi:10.1175/JTECH-D-11-00002.1.
  • Yao, X., J. Guo, J. Hu, and Q. Cao. 2019. “Using Deep Learning in Semantic Classification for Point Cloud Data.” IEEE Access 7: 37121–37130. doi:10.1109/ACCESS.2019.2905546.
  • Yao, X., J. Han, G. Cheng, X. Qian, and L. Guo. 2016. “Semantic Annotation of High-resolution Satellite Images via Weakly Supervised Learning.” IEEE Transactions on Geoscience and Remote Sensing 54 (6): 3660–3667. doi:10.1109/TGRS.2016.2523563.
  • Ye, L., Z. Cao, and Y. Xiao. 2017. “DeepCloud: Ground-Based Cloud Image Categorization Using Deep Convolutional Features.” IEEE Transactions on Geoscience and Remote Sensing 55 (10): 5729–5740. doi:10.1109/TGRS.2017.2712809.
  • Yu, F., and V. Koltun 2015. “Multi-scale Context Aggregation by Dilated Convolutions.” arXiv preprint arXiv:1511.07122.
  • Zhan, Y., W. Jian, J. Shi, G. Cheng, L. Yao, and W. Sun. 2017. “Distinguishing Cloud and Snow in Satellite Images via Deep Convolutional Network.” IEEE Geoscience and Remote Sensing Letters 14 (10): 1785–1789. doi:10.1109/LGRS.2017.2735801.
  • Zhou, Z. H. 2012. Ensemble Methods: Foundations and Algorithms. London: Chapman and Hall/CRC. doi:10.1109/MCI.2012.2228600.
  • Zhou, Z. H., and J. Feng. 2017. “Deep Forest: Towards an Alternative to Deep Neural Networks.” arXiv preprint arXiv:1702.08835.
  • Zhuge, X. Y., X. Zou, and Y. Wang. 2017. “A Fast Cloud Detection Algorithm Applicable to Monitoring and Nowcasting of Daytime Cloud Systems.” IEEE Transactions on Geoscience and Remote Sensing 55 (11): 6111–6119. doi:10.1109/tgrs.2017.2720664.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.