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首页> 外文期刊>IEEE Transactions on Geoscience and Remote Sensing >Unsupervised Deep Feature Extraction for Remote Sensing Image Classification
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Unsupervised Deep Feature Extraction for Remote Sensing Image Classification

机译：用于遥感影像分类的无监督深度特征提取

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摘要

This paper introduces the use of and convolutional networks for remote sensing data analysis. Direct application to multi- and hyperspectral imagery of supervised (shallow or deep) convolutional networks is very challenging given the high input data dimensionality and the relatively small amount of available labeled data. Therefore, we propose the use of coupled with a highly efficient algorithm for unsupervised learning of sparse features. The algorithm is rooted on and enforces both population and lifetime sparsity of the extracted features, simultaneously. We successfully illustrate the expressive power of the extracted representations in several scenarios: classification of aerial scenes, as well as land-use classification in very high resolution or land-cover classification from multi- and hyperspectral images. The proposed algorithm clearly outperforms standard principal component analysis (PCA) and its kernel counterpart (kPCA), as well as current state-of-the-art algorithms of aerial classification, while being extremely computationally efficient at learning representations of data. Results show that single-layer convolutional networks can extract powerful discriminative features when the receptive field accounts for neighboring pixels and are preferred when the classification requires high resolution and detailed results. However, deep architectures significantly outperform single-layer variants, capturing increasing levels of abstraction and complexity throughout the feature hierarchy.
机译：本文介绍了卷积网络在遥感数据分析中的应用。鉴于高输入数据维数和相对较少的可用标记数据，直接应用于监督（浅或深）卷积网络的多光谱和高光谱图像非常具有挑战性。因此，我们建议结合使用高效算法对稀疏特征进行无监督学习。该算法基于并且同时强制了提取特征的总体稀疏性和生命周期稀疏性。我们成功地说明了在几种情况下提取的表示形式的表达能力：空中场景的分类，以及非常高分辨率的土地利用分类或多光谱和高光谱图像的土地覆盖分类。所提出的算法明显优于标准主成分分析（PCA）及其内核对等（kPCA），以及当前最新的航空分类算法，同时在学习数据表示方面具有极高的计算效率。结果表明，当接收场考虑相邻像素时，单层卷积网络可以提取强大的判别特征；当分类需要高分辨率和详细结果时，首选单层卷积网络。但是，深层架构的性能明显优于单层变体，从而在整个功能层次结构中捕获了越来越高的抽象水平和复杂性。

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