Design and Implementation of Novel Agricultural Remote Sensing Image Classification Framework through Deep Neural Network and Multi-Feature Analysis
- 10.2991/asei-15.2015.201How to use a DOI?
- Agricultural Remote Sensing; Image Classification; Deep Neural Network; Feature Selection.
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 remote 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 unsupervised 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.
- © 2015, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Youzhi Zhang PY - 2015/05 DA - 2015/05 TI - Design and Implementation of Novel Agricultural Remote Sensing Image Classification Framework through Deep Neural Network and Multi-Feature Analysis BT - Proceedings of the 2015 International conference on Applied Science and Engineering Innovation PB - Atlantis Press SP - 1025 EP - 1031 SN - 2352-5401 UR - https://doi.org/10.2991/asei-15.2015.201 DO - 10.2991/asei-15.2015.201 ID - Zhang2015/05 ER -