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DEEP NO LEARNING APPROACH FOR UNSUPERVISED CHANGE DETECTION IN HYPERSPECTRAL IMAGES

机译:在高光谱图像中无监督变化检测的深度没有学习方法

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摘要

Unsupervised deep transfer-learning based change detection (CD) methods require pre-trained feature extractor that can be used to extract semantic features from the target bi-temporal scene. However, it is difficult to obtain such feature extractors for hyperspectral images. Moreover, it is not trivial to reuse the models trained with the multispectral images for the hyperspectral images due to the significant difference in number of spectral bands. While hyperspectral images show large number of spectral bands, they generally show much less spatial complexity, thus reducing the requirement of large receptive fields of convolution filters. Recent works in the computer vision have shown that even untrained networks can yield remarkable result in different tasks like super-resolution and surface reconstruction. Motivated by this, we make a bold proposition that untrained deep model, initialized with some weight initialization strategy can be used to extract useful semantic features from bi-temporal hyperspectral images. Thus, we couple an untrained network with Deep Change Vector Analysis (DCVA), a popular method for unsupervised CD, to propose an unsupervised CD method for hyperspectral images. We conduct experiments on two hyperspectral CD data sets, and the results demonstrate advantages of the proposed unsupervised method over other competitors.
机译:无监督的深度传输学习基于改变检测(CD)方法需要预先训练的特征提取器,可用于从目标双时间场景中提取语义特征。然而,难以获得超细图像的这种特征提取器。此外,由于频谱频带数量的显着差异,它不会降低利用多光谱图像的多光谱图像训练的模型。虽然高光谱图像显示出大量的光谱带,但它们通常显示得多的空间复杂性,从而减少了卷积滤波器的大容器的要求。最近在计算机愿景中的作品表明,即使未经训练的网络也可以在超级分辨率和表面重建等方面产生显着的结果。由此激励,我们提出了一个大胆的命题,即未经训练的深度模型,用一些重量初始化策略初始化,可用于从双时效高光谱图像中提取有用的语义特征。因此,我们将未经训练的网络与深变化载体分析(DCVA)耦合,这是一个无监督CD的流行方法,提出了一种用于高光谱图像的无监督CD方法。我们对两个高光谱CD数据集进行实验,结果表明了拟议的无监督方法在其他竞争对手上的优势。

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