Journal
REMOTE SENSING LETTERS
Volume 8, Issue 9, Pages 839-848Publisher
TAYLOR & FRANCIS LTD
DOI: 10.1080/2150704X.2017.1331053
Keywords
-
Funding
- State Key Laboratory of Geo-information Engineering [SKLGIE2015-M-3-1, SKLGIE2015-M-3-2]
- National Natural Science Foundation of China [41201477]
- Scientific and Technological Project in Henan Province [152102210014]
Ask authors/readers for more resources
Convolutional neural network (CNN) for hyperspectral image classification can provide excellent performance when the number of labeled samples for training is sufficiently large. Unfortunately, a small number of labeled samples are available for training in hyperspectral images. In this letter, a novel semi-supervised convolutional neural network is proposed for the classification of hyperspectral image. The proposed network can automatically learn features from complex hyperspectral image data structures. Furthermore, skip connection parameters are added between the encoder layer and decoder layer in order to make the network suitable for semi-supervised learning. Semi-supervised method is adopted to solve the problem of limited labeled samples. Finally, the network is trained to simultaneously minimize the sum of supervised and unsupervised cost functions. The proposed network is conducted on a widely used hyperspectral image data. The experimental results demonstrate that the proposed approach provides competitive results to state-of-the-art methods.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available