深度学习解锁遥感科学：挑战、进展与融合

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"这篇文章是关于深度学习在遥感领域的应用综述，由Xiao Xiang Zhu等人撰写。文章探讨了深度学习在遥感数据分析中所面临的挑战，回顾了该领域的最新进展，并提供了一些资源，旨在简化遥感中的深度学习入门。作者强调了将遥感专家的专业知识与深度学习相结合的重要性，以应对气候变化和城市化等大规模挑战。" 深度学习是一种基于人工神经网络的机器学习方法，其强大的特征学习和模式识别能力使其在图像识别、自然语言处理、语音识别等多个领域取得了显著成果。在遥感领域，深度学习的应用主要集中在以下几个方面： 1. 图像分类与目标检测：深度学习模型如卷积神经网络（CNN）可以自动提取遥感图像的特征，进行土地覆盖分类、建筑物检测、车辆识别等任务，提高识别的准确性和效率。 2. 监测与变化检测：利用深度学习可以有效地监测环境变化，如森林覆盖、冰川退缩、城市扩张等，通过时间序列数据分析，及时发现并量化这些变化。 3. 地形重建与三维建模：通过深度学习，可以从多视角遥感图像中恢复地形信息，构建高精度的数字高程模型（DEM）和三维城市模型。 4. 灾害预测与响应：深度学习模型可以用于自然灾害的预警，如洪水、火灾、台风等，通过对历史数据的学习，预测灾害的发生概率和影响范围。 5. 气候模型的改进：将深度学习应用于气候模型，可以提高模型的预测精度，更准确地模拟气候变化趋势。 6. 数据融合与增强：深度学习可以帮助整合来自不同传感器的数据，提升数据质量和可用性，比如将光学与雷达数据融合，增强图像的解析能力。尽管深度学习带来了诸多优势，但也存在一些挑战。例如，遥感数据通常具有高维度、大尺度和复杂场景，需要大量的计算资源和训练数据。此外，模型的可解释性较差，被称为“黑箱”，这在需要科学理解的遥感领域是个问题。因此，作者鼓励遥感专家将他们的专业知识融入深度学习，以创建更具解释性的模型，并解决实际世界的问题。为了推动遥感领域的深度学习研究，文章可能提供了教程、开源代码库、预训练模型等资源，使得研究者和从业者能够更容易地入门和应用深度学习技术。这将有助于加速遥感科学的进步，更好地应对全球变化带来的挑战。

IEEE GEOSCIENCE AND REMOTE SENSING MAGAZINE, IN PRESS. 10

Fig. 4. Statistics on papers related to deep learning in remote sensing. [source: ISI web of Science; status: September 2017]

2020, and DESIS, planned in 2017, hyperspectral image analysis has been one of the most

active research directions in the remote sensing community over the last decade.

Inspired by the success of deep learning in computer vision, preliminary studies have been

carried out on deep learning in hyperspectral data analysis, which brings new momentum into

this ﬁeld. In this section, we would like to review two application cases, namely, land cover/use

classiﬁcation (III-A1) and anomaly detection (III-A2).

Fig. 5. Flowchart of the 3D CNN architecture proposed in [19] for spectral-spatial hyperspectral image classiﬁcation.

1) Hyperspectral Image Classiﬁcation: Supervised classiﬁcation is probably the most active

research area in hyperspectral data analysis. There is a vast literature on this topic using the con-

ventional supervised machine learning models, such as decision trees, random forests, and support

vector machines (SVMs) [20]. With the investigation of hyperspectral image classiﬁcation [21],

October 12, 2017 DRAFT

IEEE GEOSCIENCE AND REMOTE SENSING MAGAZINE, IN PRESS. 11

a major ﬁnding was that various atmospheric scattering conditions, complicated light scattering

mechanisms, inter-class similarity, and intra-class variability result in the hyperspectral imaging

procedure being inherently nonlinear. It is believed that, in comparison to the aforementioned

“shallow” models, deep learning architectures are able to extract high-level, hierarchical, and

abstract features, which are generally more robust to the nonlinear input data.

i) Autoencoders for hyperspectral data classiﬁcation: A ﬁrst attempt in this direction can be

found in [22], where authors make use of a stacked autoencoder to extract hierarchical features

in the spectral domain. Subsequently, in [23], authors employ DBM. Similarly, Tao et al. [24]

use sparse stacked autoencoder to learn an effective feature representation from unlabeled data,

and then the learned features are fed into a linear SVM for hyperspectral data classiﬁcation.

ii) Supervised CNNs: In [25], authors train a simple 1D CNN that contains ﬁve layers, namely,

an input layer, a convolutional layer, a max pooling layer, a fully connected layer, and an output

layer – and directly classify the hyperspectral images in spectral domain.

Makantasis et al. [26] exploited a 2D CNN to encode spectral and spatial information, followed

by a multi-layer perceptron performing the actual classiﬁcation. In [27], authors attempt to carry

out classiﬁcation of crop types using 1D CNN and 2D CNN. They concluded that the 2D

CNNs can outperform the 1D CNNs, but some small objects in the ﬁnal classiﬁcation map

provided by 2D CNN are smoothed and misclassiﬁed. To avoid overﬁtting, Zhao and Du [28]

propose a spectral-spatial feature-based classiﬁcation framework, which jointly makes use of

a local discriminant embedding-based dimension reduction algorithm and a 2D CNN. In [21],

authors propose a self-improving CNN model, which combines a 2D CNN with a fractional

order Darwinian particle swarm optimization algorithm to iteratively select the most informative

bands that are suitable for training the designed CNN. Santara et al. [29] propose an end-to-end

band-adaptive spectral-spatial feature learning network to address the problems of the curse of

dimensionality. In [30], to allow CNN appropriately trained using limited labeled data, authors

present a novel pixel-pair CNN to signiﬁcantly augment the number of training samples.

Following recent vision developments in 3D CNNs [31], in which the third dimension usually

refers to the time axis, such architecture has also been employed in hyperspectral classiﬁcation.

In other words, in 3D CNN, convolution operations are performed spatial-spectrally while in

2D CNNs they are done only spatially. Compared to 1D and 2D CNNs, 3D CNNs can model

spectral information better owing to 3D convolution operations. Authors in [19] introduced a

supervised, `

regularized 3D CNN-based model (see Fig. 5). While authors of [32] followed a

October 12, 2017 DRAFT

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