Abstract
Recent research has shown that using spectral–spatial information can considerably improve the performance of hyperspectral image (HSI) classification. HSI data is typically presented in the format of 3D cubes. Thus, 3D spatial filtering naturally offers a simple and effective method for simultaneously extracting the spectral–spatial features within such images. In this paper, a 3D convolutional neural network (3D-CNN) framework is proposed for accurate HSI classification. The proposed method views the HSI cube data altogether without relying on any preprocessing or post-processing, extracting the deep spectral–spatial-combined features effectively. In addition, it requires fewer parameters than other deep learning-based methods. Thus, the model is lighter, less likely to over-fit, and easier to train. For comparison and validation, we test the proposed method along with three other deep learning-based HSI classification methods—namely, stacked autoencoder (SAE), deep brief network (DBN), and 2D-CNN-based methods—on three real-world HSI datasets captured by different sensors. Experimental results demonstrate that our 3D-CNN-based method outperforms these state-of-the-art methods and sets a new record.
Highlights
By capturing digital images in hundreds of continuous narrow spectral bands spanning the visible to infrared wavelengths, hyperspectral remote sensors produce 3D hyperspectral imagery (HSI)containing both spectral and spatial information
Four deep learning-based classification methods, including stacked autoencoder (SAE)-LR, deep brief network (DBN)-LR, 2D-convolutional neural networks (CNN), and deep learning toolbox [45]. 2D-CNN and 3D convolutional neural network (3D-CNN) were implemented based on MatConvNet [46], a weretoolbox evaluated and compared
3D-CNN was trained for about 8000 iterations, and 20 samples were randomly taken from each iteration
Summary
By capturing digital images in hundreds of continuous narrow spectral bands spanning the visible to infrared wavelengths, hyperspectral remote sensors produce 3D hyperspectral imagery (HSI)containing both spectral and spatial information. Conventional HSI classification methods are often based only on spectral information. The classification accuracy of these methods is usually unsatisfactory due to the well-known “small-sample problem”: a sufficient number of training samples may not be available for the high number of spectral bands. This unbalance between the high dimensionality of spectral bands and the limited number of training samples is known as the Hughes phenomenon [8]. Classification algorithms exploiting only the spectral information fail to capture the important spatial variability perceived for high-resolution data, generally resulting
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