基于自编码器的高光谱与激光雷达数据融合地物分类

Autoencoder-Based Fusion Classification of Hyperspectral and LiDAR Data

高光谱与激光雷达数据融合在地物分类领域具有广泛的应用潜力。然而,由于休斯现象的干扰,从高光谱与激光雷达数据中提取完整的联合特征仍然存在挑战。为了克服现有数据融合算法在消除异构数据差异和实现跨模态特征充分融合方面的局限性,提出一种基于自编码器的高光谱与激光雷达数据融合分类方法。首先,利用卷积神经网络模型从高光谱和激光雷达数据中提取深层特征,从而有效消除异构数据在原始数据空间上的差异。然后,通过构建卷积自编码器进行数据特征融合,并采用跨模态的重建策略更充分地学习不同遥感数据源的特征信息。最后,采用休斯敦与特伦托数据集对该模型进行有效性验证。实验结果表明,所提方法在总体分类精度、平均准确率等评价指标上明显优于SVM、ELM、EndNet以及MML等4种对比方法,从而证实了该方法在城市地物分类领域的优越性。

The fusion of hyperspectral and LiDAR data has broad application potential in the field of ground object classification.However,because of the Hughes phenomenon,extracting complete joint features from hyperspectral and LiDAR data is difficult.Therefore,this paper proposes a hyperspectral and LiDAR data fusion classification method based on an autoencoder to overcome the limitations in existing data fusion algorithms by eliminating heterogeneous data differences and achieving full fusion of cross-modal features.First,convolutional neural network models are used to extract deep features from hyperspectral and LiDAR data,effectively eliminating the differences in the original data space of heterogeneous data.Then,the feature information of different remote sensing data sources is fully learned by constructing a convolutional autoencoder for data feature fusion and adopting a cross-modal reconstruction strategy.Finally,the effectiveness of the proposed model is verified using the Houston and Trento datasets.Results show that the proposed method is remarkably superior to four methods,namely,SVM,ELM,EndNet,and MML,in terms of overall classification accuracy,average accuracy,and other evaluation indicators,thus confirming the superiority of the proposed method in urban land classification.