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

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

10.20944/preprints201912.0059.v2 ◽

2020 ◽

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

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

10.20944/preprints201912.0059.v1 ◽

2019 ◽

Author(s):

Li Rui ◽

Zheng Shunyi ◽

Duan Chenxi ◽

Yang Yang ◽

Wang Xiqi

Keyword(s):

Spatial Information ◽

Hyperspectral Image ◽

State Of The Art ◽

Empirical Studies ◽

The State ◽

Attention Mechanism ◽

Spatial Features ◽

Better Than

In recent years, more and more researchers have gradually paid attention to Hyperspectral Image (HSI) classification. It is significant to implement researches on how to use HSI's sufficient spectral and spatial information to its fullest potential. To capture spectral and spatial features, we propose a Double-Branch Dual-Attention mechanism network (DBDA) for HSI classification in this paper, Two branches aer designed to extract spectral and spatial features separately to reduce the interferences between these two kinds of features. What is more, because distinguishing characteristics exist in the two branches, two types of attention mechanisms are applied in two branches above separately, ensuring to exploit spectral and spatial features more discriminatively. Finally, the extracted features are fused for classification. A series of empirical studies have been conducted on four hyperspectral datasets, and the results show that the proposed method performs better than the state-of-the-art method.

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Multiscale Deep Spatial Feature Extraction Using Virtual RGB Image for Hyperspectral Imagery Classification

Remote Sensing ◽

10.3390/rs12020280 ◽

2020 ◽

Vol 12 (2) ◽

pp. 280 ◽

Cited By ~ 3

Author(s):

Liqin Liu ◽

Zhenwei Shi ◽

Bin Pan ◽

Ning Zhang ◽

Huanlin Luo ◽

...

Keyword(s):

Feature Extraction ◽

Deep Learning ◽

Hyperspectral Image ◽

Feature Fusion ◽

Hyperspectral Data ◽

Learning Technology ◽

Learning Networks ◽

Spatial Features ◽

Training Samples ◽

Rgb Image

In recent years, deep learning technology has been widely used in the field of hyperspectral image classification and achieved good performance. However, deep learning networks need a large amount of training samples, which conflicts with the limited labeled samples of hyperspectral images. Traditional deep networks usually construct each pixel as a subject, ignoring the integrity of the hyperspectral data and the methods based on feature extraction are likely to lose the edge information which plays a crucial role in the pixel-level classification. To overcome the limit of annotation samples, we propose a new three-channel image build method (virtual RGB image) by which the trained networks on natural images are used to extract the spatial features. Through the trained network, the hyperspectral data are disposed as a whole. Meanwhile, we propose a multiscale feature fusion method to combine both the detailed and semantic characteristics, thus promoting the accuracy of classification. Experiments show that the proposed method can achieve ideal results better than the state-of-art methods. In addition, the virtual RGB image can be extended to other hyperspectral processing methods that need to use three-channel images.

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

Remote Sensing ◽

10.3390/rs11151794 ◽

2019 ◽

Vol 11 (15) ◽

pp. 1794 ◽

Cited By ~ 3

Author(s):

Wenju Wang ◽

Shuguang Dou ◽

Sen Wang

Keyword(s):

Deep Learning ◽

Forward Direction ◽

Hyperspectral Images ◽

Data Sets ◽

Convolutional Network ◽

Spatial Features ◽

Training Samples ◽

Feedback Structure ◽

Kennedy Space

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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CSR-Net: Camera Spectral Response Network for Dimensionality Reduction and Classification in Hyperspectral Imagery

Remote Sensing ◽

10.3390/rs12203294 ◽

2020 ◽

Vol 12 (20) ◽

pp. 3294

Author(s):

Yunhao Zou ◽

Ying Fu ◽

Yinqiang Zheng ◽

Wei Li

Keyword(s):

Hyperspectral Image ◽

State Of The Art ◽

Spectral Response ◽

Curse Of Dimensionality ◽

Classification Problems ◽

Spectral Bands ◽

Spatial Features ◽

Response Network ◽

Training Samples ◽

Limited Training Samples

Hyperspectral image (HSI) classification has become one of the most significant tasks in the field of hyperspectral analysis. However, classifying each pixel in HSI accurately is challenging due to the curse of dimensionality and limited training samples. In this paper, we present an HSI classification architecture called camera spectral response network (CSR-Net), which can learn the optimal camera spectral response (CSR) function for HSI classification problems and effectively reduce the spectral dimensions of HSI. Specifically, we design a convolutional layer to simulate the capturing process of cameras, which learns the optimal CSR function for HSI classification. Then, spectral and spatial features are further extracted by spectral and spatial attention modules. On one hand, the learned CSR can be implemented physically and directly used to capture scenes, which makes the image acquisition process more convenient. On the other hand, compared with ordinary HSIs, we only need images with far fewer bands, without sacrificing the classification precision and avoiding the curse of dimensionality. The experimental results of four popular public hyperspectral datasets show that our method, with only a few image bands, outperforms state-of-the-art HSI classification methods which utilize the full spectral bands of images.

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Weakly Supervised Classification of Hyperspectral Image Based on Complementary Learning

Remote Sensing ◽

10.3390/rs13245009 ◽

2021 ◽

Vol 13 (24) ◽

pp. 5009

Author(s):

Lingbo Huang ◽

Yushi Chen ◽

Xin He

Keyword(s):

Supervised Classification ◽

Data Augmentation ◽

Hyperspectral Image ◽

State Of The Art ◽

The State ◽

Weak Supervision ◽

Training Samples ◽

Weakly Supervised ◽

Weakly Supervised Classification

In recent years, supervised learning-based methods have achieved excellent performance for hyperspectral image (HSI) classification. However, the collection of training samples with labels is not only costly but also time-consuming. This fact usually causes the existence of weak supervision, including incorrect supervision where mislabeled samples exist and incomplete supervision where unlabeled samples exist. Focusing on the inaccurate supervision and incomplete supervision, the weakly supervised classification of HSI is investigated in this paper. For inaccurate supervision, complementary learning (CL) is firstly introduced for HSI classification. Then, a new method, which is based on selective CL and convolutional neural network (SeCL-CNN), is proposed for classification with noisy labels. For incomplete supervision, a data augmentation-based method, which combines mixup and Pseudo-Label (Mix-PL) is proposed. And then, a classification method, which combines Mix-PL and CL (Mix-PL-CL), is designed aiming at better semi-supervised classification capacity of HSI. The proposed weakly supervised methods are evaluated on three widely-used hyperspectral datasets (i.e., Indian Pines, Houston, and Salinas datasets). The obtained results reveal that the proposed methods provide competitive results compared to the state-of-the-art methods. For inaccurate supervision, the proposed SeCL-CNN has outperformed the state-of-the-art method (i.e., SSDP-CNN) by 0.92%, 1.84%, and 1.75% in terms of OA on the three datasets, when the noise ratio is 30%. And for incomplete supervision, the proposed Mix-PL-CL has outperformed the state-of-the-art method (i.e., AROC-DP) by 1.03%, 0.70%, and 0.82% in terms of OA on the three datasets, with 25 training samples per class.

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Hyperspectral Image Classification with Capsule Network Using Limited Training Samples

Sensors ◽

10.3390/s18093153 ◽

2018 ◽

Vol 18 (9) ◽

pp. 3153 ◽

Cited By ~ 32

Author(s):

Fei Deng ◽

Shengliang Pu ◽

Xuehong Chen ◽

Yusheng Shi ◽

Ting Yuan ◽

...

Keyword(s):

Neural Networks ◽

Deep Learning ◽

Network Architecture ◽

Hyperspectral Image ◽

Machine Learning Algorithms ◽

Superior Performance ◽

Support Vector ◽

Complex Data ◽

Training Samples ◽

Limited Training Samples

Deep learning techniques have boosted the performance of hyperspectral image (HSI) classification. In particular, convolutional neural networks (CNNs) have shown superior performance to that of the conventional machine learning algorithms. Recently, a novel type of neural networks called capsule networks (CapsNets) was presented to improve the most advanced CNNs. In this paper, we present a modified two-layer CapsNet with limited training samples for HSI classification, which is inspired by the comparability and simplicity of the shallower deep learning models. The presented CapsNet is trained using two real HSI datasets, i.e., the PaviaU (PU) and SalinasA datasets, representing complex and simple datasets, respectively, and which are used to investigate the robustness or representation of every model or classifier. In addition, a comparable paradigm of network architecture design has been proposed for the comparison of CNN and CapsNet. Experiments demonstrate that CapsNet shows better accuracy and convergence behavior for the complex data than the state-of-the-art CNN. For CapsNet using the PU dataset, the Kappa coefficient, overall accuracy, and average accuracy are 0.9456, 95.90%, and 96.27%, respectively, compared to the corresponding values yielded by CNN of 0.9345, 95.11%, and 95.63%. Moreover, we observed that CapsNet has much higher confidence for the predicted probabilities. Subsequently, this finding was analyzed and discussed with probability maps and uncertainty analysis. In terms of the existing literature, CapsNet provides promising results and explicit merits in comparison with CNN and two baseline classifiers, i.e., random forests (RFs) and support vector machines (SVMs).

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Double-Branch Network with Pyramidal Convolution and Iterative Attention for Hyperspectral Image Classification

Remote Sensing ◽

10.3390/rs13071403 ◽

2021 ◽

Vol 13 (7) ◽

pp. 1403

Author(s):

Hao Shi ◽

Guo Cao ◽

Zixian Ge ◽

Youqiang Zhang ◽

Peng Fu

Keyword(s):

Hyperspectral Image ◽

State Of The Art ◽

Classification Performance ◽

Attention Mechanism ◽

Hyperspectral Images ◽

Hyperspectral Image Classification ◽

Excellent Performance ◽

Common Procedure ◽

Spatial Features ◽

First Choice

Deep-learning methods, especially convolutional neural networks (CNN), have become the first choice for hyperspectral image (HSI) classification to date. It is a common procedure that small cubes are cropped from hyperspectral images and then fed into CNNs. However, standard CNNs find it difficult to extract discriminative spectral–spatial features. How to obtain finer spectral–spatial features to improve the classification performance is now a hot topic of research. In this regard, the attention mechanism, which has achieved excellent performance in other computer vision, holds the exciting prospect. In this paper, we propose a double-branch network consisting of a novel convolution named pyramidal convolution (PyConv) and an iterative attention mechanism. Each branch concentrates on exploiting spectral or spatial features with different PyConvs, supplemented by the attention module for refining the feature map. Experimental results demonstrate that our model can yield competitive performance compared to other state-of-the-art models.

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Deep&Dense Convolutional Neural Network for Hyperspectral Image Classification

Remote Sensing ◽

10.3390/rs10091454 ◽

2018 ◽

Vol 10 (9) ◽

pp. 1454 ◽

Cited By ~ 24

Author(s):

Mercedes Paoletti ◽

Juan Haut ◽

Javier Plaza ◽

Antonio Plaza

Keyword(s):

Neural Networks ◽

Hyperspectral Image ◽

Training Data ◽

Current Layer ◽

Feature Maps ◽

Slowing Down ◽

Spatial Features ◽

Current State ◽

Learning Rates

Deep neural networks (DNNs) have emerged as a relevant tool for the classification of remotely sensed hyperspectral images (HSIs), with convolutional neural networks (CNNs) being the current state-of-the-art in many classification tasks. However, deep CNNs present several limitations in the context of HSI supervised classification. Although deep models are able to extract better and more abstract features, the number of parameters that must be fine-tuned requires a large amount of training data (using small learning rates) in order to avoid the overfitting and vanishing gradient problems. The acquisition of labeled data is expensive and time-consuming, and small learning rates forces the gradient descent to use many small steps to converge, slowing down the runtime of the model. To mitigate these issues, this paper introduces a new deep CNN framework for spectral-spatial classification of HSIs. Our newly proposed framework introduces shortcut connections between layers, in which the feature maps of inferior layers are used as inputs of the current layer, feeding its own output to the rest of the the upper layers. This leads to the combination of various spectral-spatial features across layers that allows us to enhance the generalization ability of the network with HSIs. Our experimental results with four well-known HSI datasets reveal that the proposed deep&dense CNN model is able to provide competitive advantages in terms of classification accuracy when compared to other state-of-the-methods for HSI classification.

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Residual Group Channel and Space Attention Network for Hyperspectral Image Classification

Remote Sensing ◽

10.3390/rs12122035 ◽

2020 ◽

Vol 12 (12) ◽

pp. 2035 ◽

Cited By ~ 4

Author(s):

Peida Wu ◽

Ziguan Cui ◽

Zongliang Gan ◽

Feng Liu

Keyword(s):

Hyperspectral Image ◽

Three Dimensional ◽

Classification Performance ◽

Training Process ◽

Feature Maps ◽

Attention Network ◽

Spatial Features ◽

Network Training ◽

Training Samples ◽

Classification Tasks

Recently, deep learning methods based on three-dimensional (3-D) convolution have been widely used in the hyperspectral image (HSI) classification tasks and shown good classification performance. However, affected by the irregular distribution of various classes in HSI datasets, most previous 3-D convolutional neural network (CNN)-based models require more training samples to obtain better classification accuracies. In addition, as the network deepens, which leads to the spatial resolution of feature maps gradually decreasing, much useful information may be lost during the training process. Therefore, how to ensure efficient network training is key to the HSI classification tasks. To address the issue mentioned above, in this paper, we proposed a 3-DCNN-based residual group channel and space attention network (RGCSA) for HSI classification. Firstly, the proposed bottom-up top-down attention structure with the residual connection can improve network training efficiency by optimizing channel-wise and spatial-wise features throughout the whole training process. Secondly, the proposed residual group channel-wise attention module can reduce the possibility of losing useful information, and the novel spatial-wise attention module can extract context information to strengthen the spatial features. Furthermore, our proposed RGCSA network only needs few training samples to achieve higher classification accuracies than previous 3-D-CNN-based networks. The experimental results on three commonly used HSI datasets demonstrate the superiority of our proposed network based on the attention mechanism and the effectiveness of the proposed channel-wise and spatial-wise attention modules for HSI classification. The code and configurations are released at Github.com.

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