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Design and Implementation of Novel Agricultural Remote Sensing Image Classification Framework through Deep Neural Network and Multi-Feature Analysis

文献类型： 外文期刊

作者： Zhang, Youzhi 1 ;

作者机构： 1.Heilongjiang Acad Agr Sci, Remote Sensing Tech Ctr, Baicheng 150086, Heilongjiang, Peoples R China

关键词： Agricultural Remote Sensing;Image Classification;Deep Neural Network;Feature Selection

期刊名称：PROCEEDINGS OF THE 2015 INTERNATIONAL CONFERENCE ON APPLIED SCIENCE AND ENGINEERING INNOVATION

ISSN： 2352-5401

年卷期： 2015 年 12 卷

页码：

收录情况： SCI

摘要： With the rapid and bursting development of computer science and sensor technology, efficient remote sensing (RS) image classification algorithm is ur-gently needed. There are plenty of applications of re-mote sensing image processing techniques. In this paper, we propose a new agricultural remote sensing image classification and recognition method based on sparse auto-encoder deep neural network. Using an unsuper-vised learning algorithm features a large number of small pieces of sparse auto-encoder learning from some deep unlabeled images have already completed the training neural networks, and then learn features. The experiment and simulation prove the correctness of our model compared with other methods.

  • 相关文献

[1]How do temporal and spectral features matter in crop classification in Heilongjiang Province, China?. Hu Qiong,Wu Wen-bin,Song Qian,Lu Miao,Chen Di,Yu Qiang-yi,Tang Hua-jun,Song Qian. 2017

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