Multiview Deep Learning for Land-Use Classification
Multiview Deep Learning for Land-Use Classification
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DOI：
10.1109/lgrs.2015.2483680
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发表时间：
2015-12-01
影响因子：
4.8
通讯作者：
Maharaj, B. T. J.
中科院分区：
文献类型：
--
作者：
Luus, F. P. S.;Salmon, B. P.;Maharaj, B. T. J.
提出了一种多视角深度学习的多尺度输入策略，用于监督式多光谱土地利用分类，并在一个知名数据集上进行了验证。假设同时多尺度的意见，可以提高基于组合的推理类包含大小不同的对象相比，单尺度多视图进行了研究。端到端学习系统在卷积层的帮助下学习分层特征表示，将特征确定的负担从手工工程转移到深度卷积神经网络（DCNN）。这允许分类器获得对于最小化多项式逻辑回归目标而言最优的问题特定特征，而不是以最优性换取一般性的用户定义特征。基于经验性能证据，使用启发式方法来优化DCNN超参数。结果表明，单个DCNN可以与多尺度视图同时训练，以提高多个单尺度视图的预测精度。UC默塞德数据集实现了具有竞争力的性能，其中多视图深度学习的93.48%准确率优于基于SIFT的方法的85.37%准确率和无监督特征学习的90.26%准确率。
A multiscale input strategy for multiview deep learning is proposed for supervised multispectral land-use classification, and it is validated on a well-known data set. The hypothesis that simultaneous multiscale views can improve composition-based inference of classes containing size-varying objects compared to single-scale multiview is investigated. The end-to-end learning system learns a hierarchical feature representation with the aid of convolutional layers to shift the burden of feature determination from hand-engineering to a deep convolutional neural network (DCNN). This allows the classifier to obtain problem-specific features that are optimal for minimizing the multinomial logistic regression objective, as opposed to user-defined features which trade optimality for generality. A heuristic approach to the optimization of the DCNN hyperparameters is used, based on empirical performance evidence. It is shown that a single DCNN can be trained simultaneously with multiscale views to improve prediction accuracy over multiple single-scale views. Competitive performance is achieved for the UC Merced data set, where the 93.48% accuracy of multiview deep learning outperforms the 85.37% accuracy of SIFT-based methods and the 90.26% accuracy of unsupervised feature learning.