Journal of Agricultural Big Data >
Application of Convolutional Neural Networks in High-Resolution Image Classification
Received date: 2018-11-08
Online published: 2019-04-04
[Objective] Convolutional neural networks were applied to the classification of high-resolution remote sensing images and compared with traditional classification methods to assess the classification accuracy and applicability of different classification methods for high-resolution remote image sensing. [Methods] The convolutional neural network classification method was compared with four commonly used traditional ENVI classification methods: maximum likelihood, parallelepiped, K-means mean clustering, and traditional neural network approaches. The confusion results were examined to evaluate the effectiveness of the different classification methods. [Results] Among the four traditional ENVI classification methods, the classification accuracy of the traditional neural network and maximum likelihood methods was relatively good, the classification accuracy of k-means mean clustering and parallelepiped methods was relatively poor, and the convolutional neural networks method achieved higher classification accuracy. [Conclusion] Convolutional neural networks can extract feature information in high-resolution remote sensing image classification, improving the classification accuracy of remote image sensing.
Key words： GF-1; Convolutional Neural Network; Remote Sensing; Deep Learning
Li Xianjiang, Chen Youqi, Zou Jinqiu, Shi Shuqin, Guo Tao, Cai Weimin, Chen Hao . Application of Convolutional Neural Networks in High-Resolution Image Classification[J]. Journal of Agricultural Big Data, 2019 , 1(1) : 67 -77 . DOI: 10.19788/j.issn.2096-6369.190107
| [1] | Pacifici F, Chini M, Emery W J . A neural network approach using multi-scale textural metrics from very high-resolution panchromatic imagery for urban land-use classification.[J]. Remote Sensing of Environment, 2009,113(6):1276-1292. |
| [2] | Luo B, Zhang L . Robust autodual morphological profiles for the classification of high-resolution satellite images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2014,52(2):1451-1462. |
| [3] | 王秋全, 李向, 王岭玲 . 基于不良地质识别的分类方法研究与分析[J]. 地质科技情报, 2014,33(06):203-208. |
| [3] | Wang Q Q, Li X, Wang L L . Research and Analysis of Classification Methods Based on Bad Geological Identification[J]. Geological Science and Technology Information, 2014,33(06):203-208. |
| [4] | 臧淑英, 张策, 张丽娟 , 等. 遗传算法优化的支持向量机湿地遥感分类——以洪河国家级自然保护区为例[J]. 地理科学, 2012,32(04):434-441. |
| [4] | Zan S Y, Zhang C, Zhang L J , et al. Wetland Remote Sensing Classification Using Support Vector Machine Optimized With Genetic Algorithm:Algorithm: A Case Study in Honghe Nature National Reserve[J]. Scientia Geographica Sinica, 2012,32(04):434-441. |
| [5] | Krizhevsky A, Sutskever I, Hinton G E. ImageNet classification with deep convolutional neural networks [C]// International Conference on Neural Information Processing Systems. Curran Associates Inc. 2012: 1097-1105. |
| [6] | Fukushima K . Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position[J]. Biological Cybernetics, 1980,36(4):193-202. |
| [7] | Hu F, Xia G S, Hu J , et al. Transferring Deep Convolutional Neural Networks for the Scene Classification of High-Resolution Remote Sensing Imagery[J]. Remote Sensing, 2015,7(11):14680-14707. |
| [8] | Vakalopoulou M, Karantzalos K, Komodakis N, et al. Building detection in very high resolution multispectral data with deep learning features [C]. Geoscience and Remote Sensing Symposium. IEEE, 2015: 1873-1876. |
| [9] | Maggiori E, Tarabalka Y, Charpiat G , et al. Convolutional Neural Networks for Large-Scale Remote-Sensing Image Classification[J]. IEEE Transactions on Geoscience & Remote Sensing, 2016,55(2):645-657. |
| [10] | Bo Huang, Bei Zhao, Yimeng Song . Urban land-use mapping using a deep convolutional neural network with high spatial resolution multispectral remote sensing imagery [J] Remote Sensing of Environment,Volume 214, 2018,Pages 73-86. |
| [11] | Yu Liu, Xun Chen, Hu Peng, Zengfu Wang . Multi-focus image fusion with a deep convolutional neural network[J]. Information Fusion, 2017,36. |
| [12] | Wang B, Sun Y, Xue B , et al. A Hybrid Differential Evolution Approach to Designing Deep Convolutional Neural Networks for Image Classification[J]. 2018. |
| [13] | Oquab M, Bottou L, Laptev I, et al. Learning and Transferring Mid-level Image Representations Using Convolutional Neural Networks [C]// Computer Vision and Pattern Recognition. IEEE, 2014: 1717-1724. |
| [14] | 吕超 . 基于深度学习的情感分析方法研究[D]. 湖南大学, 2017. |
| [14] | Lu C . Research on Sentiment Analysis Method Based on Deep Learning[D]. Hunan University, 2017. |
| [15] | Maxime Oquab, Leon Bottou, Ivan Laptev , et al. Learning and Transferring Mid-Level Image Representations using Convolutional Neural Networks[J]. 2014: 1717-1724. |
| [16] | Yu K, Salzmann M . Second-order Convolutional Neural Networks[J]. Clinical Immunology & Immunopathology, 2017,66(3):230-8. |
| [17] | Defferrard M, Bresson X, Vandergheynst P . Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering[J]. 2016. |
| [18] | Schlemper J, Caballero J, Hajnal J V, et al. A Deep Cascade of Convolutional Neural Networks for MR Image Reconstruction [C]// International Conference on Information Processing in Medical Imaging. Springer, Cham, 2017: 647-658. |
| [19] | Levie R, Monti F, Bresson X , et al. CayleyNets: Graph Convolutional Neural Networks with Complex Rational Spectral Filters[J]. 2017. |
| [20] | 余春艳, 吴明晖, 吴明 . 遥感图像识别中粗糙集理论与神经网络的结合[J]遥感学报, 2004(04):331-338. |
| [20] | Yu C Y, Wu M H, Wu M . Combining Rough Set Theory with Neural Network for Remote Sensing Image[J]. Journal of Remote Sensing, 2004(04):331-338. |
| [21] | 沈跃, 徐慧, 刘慧 , 等. 基于K-means和近邻回归算法的Kinect植株深度图像修复[J]. 农业工程学报, 2016,32(19):188-194. |
| [21] | Shen Y, Xu H, Liu H , et al. Kinect scanning plant depth image restoration based on K-means and K-nearest neighbor algorithms[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2016,32(19):188-194. |
| [22] | 李贤江, 石淑芹, 蔡为民 , 等. 基于CA-Markov模型的天津滨海新区土地利用变化模拟[J]. 广西师范大学学报(自然科学版), 2018,36(03):133-143. |
| [22] | Li X J, Shi S Q, Cai W M , et al. Simulation of Land Use Change in Tianjin Binhai New Area Based on CA-Markov model[J]. Journal of Uuangxi Normal University (Natural Scicncc Edition), 2018,36(3):133-143. |
/
| 〈 |
|
〉 |
