scholarly journals Attention-Based Spatial and Spectral Network with PCA-Guided Self-Supervised Feature Extraction for Change Detection in Hyperspectral Images

2021 ◽  
Vol 13 (23) ◽  
pp. 4927
Author(s):  
Zhao Wang ◽  
Fenlong Jiang ◽  
Tongfei Liu ◽  
Fei Xie ◽  
Peng Li
Keyword(s):  
Feature Extraction ◽  
Change Detection ◽  
Weighting Factor ◽  
Hyperspectral Images ◽  
Joint Analysis ◽  
Spectral Features ◽  
Spatial Features ◽  
Central Pixel ◽  
Spectral Network ◽  
Supervised Feature Extraction

Joint analysis of spatial and spectral features has always been an important method for change detection in hyperspectral images. However, many existing methods cannot extract effective spatial features from the data itself. Moreover, when combining spatial and spectral features, a rough uniform global combination ratio is usually required. To address these problems, in this paper, we propose a novel attention-based spatial and spectral network with PCA-guided self-supervised feature extraction mechanism to detect changes in hyperspectral images. The whole framework is divided into two steps. First, a self-supervised mapping from each patch of the difference map to the principal components of the central pixel of each patch is established. By using the multi-layer convolutional neural network, the main spatial features of differences can be extracted. In the second step, the attention mechanism is introduced. Specifically, the weighting factor between the spatial and spectral features of each pixel is adaptively calculated from the concatenated spatial and spectral features. Then, the calculated factor is applied proportionally to the corresponding features. Finally, by the joint analysis of the weighted spatial and spectral features, the change status of pixels in different positions can be obtained. Experimental results on several real hyperspectral change detection data sets show the effectiveness and advancement of the proposed method.

scholarly journals A Multi-Scale and Multi-Level Spectral-Spatial Feature Fusion Network for Hyperspectral Image Classification

2020 ◽  
Vol 12 (1) ◽  
pp. 125 ◽  
Author(s):  
Mu ◽  
Guo ◽  
Liu
Keyword(s):  
Neural Network ◽  
Hyperspectral Image ◽  
Feature Fusion ◽  
Hyperspectral Images ◽  
Spectral Features ◽  
Hyperspectral Image Classification ◽  
Spatial Feature ◽  
Multi Scale ◽  
Spatial Features ◽  
Multi Level

Extracting spatial and spectral features through deep neural networks has become an effective means of classification of hyperspectral images. However, most networks rarely consider the extraction of multi-scale spatial features and cannot fully integrate spatial and spectral features. In order to solve these problems, this paper proposes a multi-scale and multi-level spectral-spatial feature fusion network (MSSN) for hyperspectral image classification. The network uses the original 3D cube as input data and does not need to use feature engineering. In the MSSN, using different scale neighborhood blocks as the input of the network, the spectral-spatial features of different scales can be effectively extracted. The proposed 3D–2D alternating residual block combines the spectral features extracted by the three-dimensional convolutional neural network (3D-CNN) with the spatial features extracted by the two-dimensional convolutional neural network (2D-CNN). It not only achieves the fusion of spectral features and spatial features but also achieves the fusion of high-level features and low-level features. Experimental results on four hyperspectral datasets show that this method is superior to several state-of-the-art classification methods for hyperspectral images.

scholarly journals DIMENSIONALITY REDUCTION OF HYPERSPECTRAL IMAGES BY COMBINATION OF NON-PARAMETRIC WEIGHTED FEATURE EXTRACTION (NWFE) AND MODIFIED NEIGHBORHOOD PRESERVING EMBEDDING (NPE)

2014 ◽  
Vol XL-2/W3 ◽  
pp. 31-34
Author(s):  
T. Alipour Fard ◽  
H. Arefi
Keyword(s):  
Feature Extraction ◽  
Extraction Method ◽  
Extraction Methods ◽  
Hyperspectral Images ◽  
Scatter Matrix ◽  
Feature Extraction Method ◽  
Conventional Extraction ◽  
Supervised Feature Extraction ◽  
Number Of Classes ◽  
Selection Of

This paper combine two conventional feature extraction methods (NWFE&NPE) in a novel framework and present a new semi-supervised feature extraction method called Adjusted Semi supervised Discriminant Analysis (ASEDA). The advantage of this method is dominating the Hughes phenomena, automatic selection of unlabelled pixels, extraction of more than L-1(L: number of classes) features and avoidance of singularity or near singularity of within-class scatter matrix. Experimental results on well-known hyperspectral dataset demonstrate that compared to conventional extraction algorithms the overall accuracy of the classification increased.

scholarly journals A Spectral Spatial Attention Fusion with Deformable Convolutional Residual Network for Hyperspectral Image Classification

2021 ◽  
Vol 13 (18) ◽  
pp. 3590
Author(s):  
Tianyu Zhang ◽  
Cuiping Shi ◽  
Diling Liao ◽  
Liguo Wang
Keyword(s):  
Image Classification ◽  
Spatial Attention ◽  
Hyperspectral Image ◽  
Spectral Feature ◽  
Hyperspectral Images ◽  
Spectral Features ◽  
Hyperspectral Image Classification ◽  
Residual Network ◽  
Spatial Features ◽  
Training Samples

Convolutional neural networks (CNNs) have exhibited excellent performance in hyperspectral image classification. However, due to the lack of labeled hyperspectral data, it is difficult to achieve high classification accuracy of hyperspectral images with fewer training samples. In addition, although some deep learning techniques have been used in hyperspectral image classification, due to the abundant information of hyperspectral images, the problem of insufficient spatial spectral feature extraction still exists. To address the aforementioned issues, a spectral–spatial attention fusion with a deformable convolution residual network (SSAF-DCR) is proposed for hyperspectral image classification. The proposed network is composed of three parts, and each part is connected sequentially to extract features. In the first part, a dense spectral block is utilized to reuse spectral features as much as possible, and a spectral attention block that can refine and optimize the spectral features follows. In the second part, spatial features are extracted and selected by a dense spatial block and attention block, respectively. Then, the results of the first two parts are fused and sent to the third part, and deep spatial features are extracted by the DCR block. The above three parts realize the effective extraction of spectral–spatial features, and the experimental results for four commonly used hyperspectral datasets demonstrate that the proposed SSAF-DCR method is superior to some state-of-the-art methods with very few training samples.

scholarly journals Unsupervised and Supervised Feature Extraction Methods for Hyperspectral Images Based on Mixtures of Factor Analyzers

2020 ◽  
Vol 12 (7) ◽  
pp. 1179
Author(s):  
Bin Zhao ◽  
Magnus O. Ulfarsson ◽  
Johannes R. Sveinsson ◽  
Jocelyn Chanussot
Keyword(s):  
Feature Extraction ◽  
Density Estimation ◽  
Single Layer ◽  
Gaussian Mixture ◽  
Extraction Methods ◽  
Optimal Number ◽  
Hyperspectral Images ◽  
Different Types ◽  
Low Dimensionality ◽  
Supervised Feature Extraction

This paper proposes three feature extraction (FE) methods based on density estimation for hyperspectral images (HSIs). The methods are a mixture of factor analyzers (MFA), deep MFA (DMFA), and supervised MFA (SMFA). The MFA extends the Gaussian mixture model to allow a low-dimensionality representation of the Gaussians. DMFA is a deep version of MFA and consists of a two-layer MFA, i.e, samples from the posterior distribution at the first layer are input to an MFA model at the second layer. SMFA consists of single-layer MFA and exploits labeled information to extract features of HSI effectively. Based on these three FE methods, the paper also proposes a framework that automatically extracts the most important features for classification from an HSI. The overall accuracy of a classifier is used to automatically choose the optimal number of features and hence performs dimensionality reduction (DR) before HSI classification. The performance of MFA, DMFA, and SMFA FE methods are evaluated and compared to five different types of unsupervised and supervised FE methods by using four real HSIs datasets.

scholarly journals Hyperspectral Images Classification Based on Dense Convolutional Networks with Spectral-Wise Attention Mechanism

2019 ◽  
Vol 11 (2) ◽  
pp. 159 ◽  
Author(s):  
Bei Fang ◽  
Ying Li ◽  
Haokui Zhang ◽  
Jonathan Chan
Keyword(s):  
Classification Performance ◽  
Attention Mechanism ◽  
Experimental Results ◽  
Hyperspectral Images ◽  
Spectral Features ◽  
Competitive Performance ◽  
Feature Maps ◽  
Convolutional Network ◽  
Convolutional Networks ◽  
Spatial Features

Hyperspectral images (HSIs) data that is typically presented in 3-D format offers an opportunity for 3-D networks to extract spectral and spatial features simultaneously. In this paper, we propose a novel end-to-end 3-D dense convolutional network with spectral-wise attention mechanism (MSDN-SA) for HSI classification. The proposed MSDN-SA exploits 3-D dilated convolutions to simultaneously capture the spectral and spatial features at different scales, and densely connects all 3-D feature maps with each other. In addition, a spectral-wise attention mechanism is introduced to enhance the distinguishability of spectral features, which improves the classification performance of the trained models. Experimental results on three HSI datasets demonstrate that our MSDN-SA achieves competitive performance for HSI classification.

scholarly journals Hierarchical Spatial-Spectral Feature Extraction with Long Short Term Memory (LSTM) for Mineral Identification Using Hyperspectral Imagery

Sensors ◽  
2020 ◽  
Vol 20 (23) ◽  
pp. 6854
Author(s):  
Huijie Zhao ◽  
Kewang Deng ◽  
Na Li ◽  
Ziwei Wang ◽  
Wei Wei
Keyword(s):  
Feature Extraction ◽  
Short Term Memory ◽  
Spectral Feature ◽  
Spectral Features ◽  
Short Term ◽  
Term Memory ◽  
Spatial Features ◽  
Proposed Model ◽  
Long Short Term Memory ◽  
Mineral Identification

Deep learning models are widely employed in hyperspectral image processing to integrate both spatial features and spectral features, but the correlations between them are rarely taken into consideration. However, in hyperspectral mineral identification, not only the spectral and spatial features of minerals need to be considered, but also the correlations between them are crucial to further promote identification accuracy. In this paper, we propose hierarchical spatial-spectral feature extraction with long short term memory (HSS-LSTM) to explore correlations between spatial features and spectral features and obtain hierarchical intrinsic features for mineral identification. In the proposed model, the fusion spatial-spectral feature is primarily extracted by stacking local spatial features obtained by a convolution neural network (CNN)-based model and spectral information together. To better exploit spatial features and spectral features, an LSTM-based model is proposed to capture correlations and obtain hierarchical features for accurate mineral identification. Specifically, the proposed model shares a uniform objective function, so that all the parameters in the network can be optimized in the meantime. Experimental results on the hyperspectral data collected by the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) in the Nevada mining area show that HSS-LSTM achieves an overall accuracy of 94.70% and outperforms other commonly used identification methods.

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