Alternately Updated Spectral–Spatial Convolution Network for the Classification of Hyperspectral Images

The connection structure in the convolutional layers of most deep learning-based algorithms used for the classification of hyperspectral images (HSIs) has typically been in the forward direction. In this study, an end-to-end alternately updated spectral–spatial convolutional network (AUSSC) with a recurrent feedback structure is used to learn refined spectral and spatial features for HSI classification. The proposed AUSSC includes alternating updated blocks in which each layer serves as both an input and an output for the other layers. The AUSSC can refine spectral and spatial features many times under fixed parameters. A center loss function is introduced as an auxiliary objective function to improve the discrimination of features acquired by the model. Additionally, the AUSSC utilizes smaller convolutional kernels than other convolutional neural network (CNN)-based methods to reduce the number of parameters and alleviate overfitting. The proposed method was implemented on four HSI data sets, as follows: Indian Pines, Kennedy Space Center, Salinas Scene, and Houston. Experimental results demonstrated that the proposed AUSSC outperformed the HSI classification accuracy obtained by state-of-the-art deep learning-based methods with a small number of training samples.

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Change Detection in Hyperspectral Images Using Recurrent 3D Fully Convolutional Networks

Remote Sensing ◽

10.3390/rs10111827 ◽

2018 ◽

Vol 10 (11) ◽

pp. 1827 ◽

Cited By ~ 24

Author(s):

Ahram Song ◽

Jaewan Choi ◽

Youkyung Han ◽

Yongil Kim

Keyword(s):

Deep Learning ◽

Change Detection ◽

Spatial Information ◽

Short Term Memory ◽

Hyperspectral Images ◽

Convolutional Network ◽

Ground Truth Data ◽

Fully Convolutional Network ◽

Training Samples ◽

Multi Temporal

Hyperspectral change detection (CD) can be effectively performed using deep-learning networks. Although these approaches require qualified training samples, it is difficult to obtain ground-truth data in the real world. Preserving spatial information during training is difficult due to structural limitations. To solve such problems, our study proposed a novel CD method for hyperspectral images (HSIs), including sample generation and a deep-learning network, called the recurrent three-dimensional (3D) fully convolutional network (Re3FCN), which merged the advantages of a 3D fully convolutional network (FCN) and a convolutional long short-term memory (ConvLSTM). Principal component analysis (PCA) and the spectral correlation angle (SCA) were used to generate training samples with high probabilities of being changed or unchanged. The strategy assisted in training fewer samples of representative feature expression. The Re3FCN was mainly comprised of spectral–spatial and temporal modules. Particularly, a spectral–spatial module with a 3D convolutional layer extracts the spectral–spatial features from the HSIs simultaneously, whilst a temporal module with ConvLSTM records and analyzes the multi-temporal HSI change information. The study first proposed a simple and effective method to generate samples for network training. This method can be applied effectively to cases with no training samples. Re3FCN can perform end-to-end detection for binary and multiple changes. Moreover, Re3FCN can receive multi-temporal HSIs directly as input without learning the characteristics of multiple changes. Finally, the network could extract joint spectral–spatial–temporal features and it preserved the spatial structure during the learning process through the fully convolutional structure. This study was the first to use a 3D FCN and a ConvLSTM for the remote-sensing CD. To demonstrate the effectiveness of the proposed CD method, we performed binary and multi-class CD experiments. Results revealed that the Re3FCN outperformed the other conventional methods, such as change vector analysis, iteratively reweighted multivariate alteration detection, PCA-SCA, FCN, and the combination of 2D convolutional layers-fully connected LSTM.

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Classification of Hyperspectral Image Based on Double-Branch Dual-Attention Mechanism Network

Remote Sensing ◽

10.3390/rs12030582 ◽

2020 ◽

Vol 12 (3) ◽

pp. 582 ◽

Cited By ~ 4

Author(s):

Rui Li ◽

Shunyi Zheng ◽

Chenxi Duan ◽

Yang Yang ◽

Xiqi Wang

Keyword(s):

Deep Learning ◽

Hyperspectral Image ◽

State Of The Art ◽

Attention Mechanism ◽

Superior Performance ◽

Feature Maps ◽

Spatial Features ◽

Training Samples ◽

Series Of Experiments

In recent years, researchers have paid increasing attention on hyperspectral image (HSI) classification using deep learning methods. To improve the accuracy and reduce the training samples, we propose a double-branch dual-attention mechanism network (DBDA) for HSI classification in this paper. Two branches are designed in DBDA to capture plenty of spectral and spatial features contained in HSI. Furthermore, a channel attention block and a spatial attention block are applied to these two branches respectively, which enables DBDA to refine and optimize the extracted feature maps. A series of experiments on four hyperspectral datasets show that the proposed framework has superior performance to the state-of-the-art algorithm, especially when the training samples are signally lacking.

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Hyperspectral Image Classification Using Spatial and Edge Features Based on Deep Learning

International Journal of Pattern Recognition and Artificial Intelligence ◽

10.1142/s0218001419540272 ◽

2019 ◽

Vol 33 (09) ◽

pp. 1954027 ◽

Cited By ~ 1

Author(s):

Dexiang Zhang ◽

Jingzhong Kang ◽

Lina Xun ◽

Yu Huang

Keyword(s):

Neural Network ◽

Deep Learning ◽

Spatial Information ◽

Hyperspectral Image ◽

Bilateral Filter ◽

Convolution Neural Network ◽

Hyperspectral Images ◽

Data Sets ◽

Edge Information

In recent years, deep learning has been widely used in the classification of hyperspectral images and good results have been achieved. But it is easy to ignore the edge information of the image when using the spatial features of hyperspectral images to carry out the classification experiments. In order to make full use of the advantages of convolution neural network (CNN), we extract the spatial information with the method of minimum noise fraction (MNF) and the edge information by bilateral filter. The combination of the two kinds of information not only increases the useful information but also effectively removes part of the noise. The convolution neural network is used to extract features and classify for hyperspectral images on the basis of this fused information. In addition, this paper also uses another kind of edge-filtering method to amend the final classification results for a better accuracy. The proposed method was tested on three public available data sets: the University of Pavia, the Salinas, and the Indian Pines. The competitive results indicate that our approach can realize a classification of different ground targets with a very high accuracy.

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