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Open AccessArticle

Automatic Kernel Size Determination for Deep Neural Networks Based Hyperspectral Image Classification

Shaanxi Key Lab of Speech & Image Information Processing (SAIIP), School of Computer Science and Engineering, Northwestern Polytechnical University, Xi’an 710129, China
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Remote Sens. 2018, 10(3), 415; https://doi.org/10.3390/rs10030415
Received: 6 December 2017 / Revised: 28 February 2018 / Accepted: 6 March 2018 / Published: 8 March 2018
Considering kernels in Convolutional Neural Networks (CNNs) as detectors for local patterns, K-means neural network proposes to cluster local patches extracted from training images and then fixate those kernels as the representative patches in each cluster without further training. Thus the amount of labeled samples necessitated for training can be greatly reduced. One key property of those kernels is their spatial size which determines their capacity in detecting local patterns and is expected to be task-specific. However, most of literatures determine the spatial size of those kernels in a heuristic way. To address this problem, we propose to automatically determine the kernel size in order to better adapt the K-means neural network for hyperspectral imagery classification. Specifically, a novel kernel-size determination scheme is developed by measuring the clustering performance of local patches with different sizes. With the kernel of determined size, more discriminative local patterns can be detected in the hyperspectral imagery, with which the classification performance of K-means neural network can be obviously improved. Experimental results on two datasets demonstrate the effectiveness of the proposed method. View Full-Text
Keywords: hyperspectral imagery; Convolutional Neural Networks; K-means; pre-learned kernels; automatic kernel size determination hyperspectral imagery; Convolutional Neural Networks; K-means; pre-learned kernels; automatic kernel size determination
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MDPI and ACS Style

Ding, C.; Li, Y.; Xia, Y.; Zhang, L.; Zhang, Y. Automatic Kernel Size Determination for Deep Neural Networks Based Hyperspectral Image Classification. Remote Sens. 2018, 10, 415.

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