Sandwich Convolutional Neural Network for Hyperspectral Image Classification Using Spectral Feature Enhancement

Abstract

Recently, convolutional neural networks (CNNs) has been used to extract spectral and spatial features of hyperspectral images (HSIs) for hyperspectral image classification (HSIC) because of their excellent performance in extracting and analyzing complex data. However, due to the limited labeled samples and existing mixed pixels, it is difficult to extract features effectively, which further leads to the problem of overfitting of the model. On the other hand, to improve the extraction ability of the CNN, the depth of the model, and the complexity of the convolution kernel often need to be increased. In this paper, a sandwich CNN based on spectral feature enhancement (SFE-SCNN) is proposed for HSIC. The proposed method, SFE-SCNN, introduces the spectral feature enhancement operation, which makes the data reflect more discriminative spectral feature details to suppress the interference of mixed pixels. Furthermore, according to the preprocessed data structure features, a lightweight sandwich convolution neural network is proposed. To fully extract the spectral features, the spectral feature re-extraction operation is used for the first time. Experimental results on three real hyperspectral data sets demonstrate that the proposed method achieves better classification performance than other state-of-the-art methods.

Depthwise Separable Relation Network for Small Sample Hyperspectral Image Classification

“…The convolutional neural network obtains the information of different feature maps at different positions through neurons, so that the obtained images have richer features. With the deepening of the number of network layers, the convolutional neural network can deal with more complex actual environments; however, it also faces some problems, such as increasing computational cost, difficult convergence of the network, and much dependence on samples [35]. Therefore, on the basis of the premise that the convolutional neural network can obtain richer images, this paper introduces the lightweight depthwise separable convolutional network model to reduce the network model parameters and improve the model training speed, and introduces the Leaky-ReLU activation function to effectively activate the neurons in each layer of neural network and increase the processing ability of the model to complex environment.…”

NFANet: A Novel Method for Weakly Supervised Water Extraction from High-Resolution Remote Sensing Imagery

“…The rapid development of deep learning technology and the improvement of computer hardware performance have enabled deep learning, especially convolutional neural network (CNN) [5]- [7], to be successfully applied to many important tasks, such as image classification [8], [9], target detection [10], [11], and semantic segmentation [12], [13]. In addition, due to its excellent performance, deep learning has been widely used in the remote sensing image fields, such as remote sensing image classification [14]- [18], change detection [19], [20], and ground object extraction [21], [22]. Hong et al [23] proposed a variety of fusion architectures to solve the special case of multi-modal learning and cross-modality learning that is widely used in remote sensing image classification applications.…”

A Novel 2D-3D CNN with Spectral-Spatial Multi-Scale Feature Fusion for Hyperspectral Image Classification

“…In recent years, all kinds of deep learning models have been carefully explored to further extract more discriminative and richer hierarchical features for HSI classification [10,[27][28][29][30][31][32][33][34][35][36], such as auto-encoders (AEs), deep belief networks (DBNs), recurrent neural networks (RNNs) and convolutional neural networks (CNNs). Among these methods, CNNs gradually become more popular since end-to-end CNNs have been proved that have a capability to automatically extract high-level features from HSI data.…”