Paper Info
Title
Improved Prototypical Network Model For Forest Species Classification In Complex Stand
Abstract
Deep learning has become an effective method for hyperspectral image classification. However, the high band correlation and data volume associated with airborne hyperspectral images, and the insufficiency of training samples, present challenges to the application of deep learning in airborne image classification. Prototypical networks are practical deep learning networks that have demonstrated effectiveness in handling small-sample classification. In this study, an improved prototypical network is proposed (by adding L2 regularization to the convolutional layer and dropout to the maximum pooling layer) to address the problem of overfitting in small-sample classification. The proposed network has an optimal sample window for classification, and the window size is related to the area and distribution of the study area. After performing dimensionality reduction using principal component analysis, the time required for training using hyperspectral images shortened significantly, and the test accuracy increased drastically. Furthermore, when the size of the sample window was 27 x 27 after dimensionality reduction, the overall accuracy of forest species classification was 98.53%, and the Kappa coefficient was 0.9838. Therefore, by using an improved prototypical network with a sample window of an appropriate size, the network yielded desirable classification results, thereby demonstrating its suitability for the fine classification and mapping of tree species.
Year
DOI
Venue
2020
10.3390/rs12223839
REMOTE SENSING
Keywords
DocType
Volume
hyperspectral images, prototypical network, tree species classification, small-sample, dimensionality reduction
Journal
12
Issue
Citations 
PageRank 
22
0
0.34
References 
Authors
0
4
Name
Order
Citations
PageRank
Xiaomin Tian100.34
Long Chen200.68
Xiaoli Zhang311.06
Erxue Chen423.45