An overview on spectral and spatial information fusion for hyperspectral image classification: Current trends and challenges

2020 ◽  
Vol 59 ◽  
pp. 59-83 ◽  
Author(s):  
Maryam Imani ◽  
Hassan Ghassemian
Keyword(s):  
Image Classification ◽  
Information Fusion ◽  
Spatial Information ◽  
Hyperspectral Image ◽  
Hyperspectral Image Classification ◽  
Current Trends

scholarly journals Hyperspectral Image Classification Based on Fusion of Curvature Filter and Domain Transform Recursive Filter

2019 ◽  
Vol 11 (7) ◽  
pp. 833 ◽  
Author(s):  
Jianshang Liao ◽  
Liguo Wang
Keyword(s):  
Image Classification ◽  
Spatial Information ◽  
Hyperspectral Image ◽  
Principal Component ◽  
Hyperspectral Images ◽  
Hyperspectral Image Classification ◽  
Recursive Filter ◽  
Texture Information ◽  
Margin Distribution ◽  
Correlation Information

In recent decades, in order to enhance the performance of hyperspectral image classification, the spatial information of hyperspectral image obtained by various methods has become a research hotspot. For this work, it proposes a new classification method based on the fusion of two spatial information, which will be classified by a large margin distribution machine (LDM). First, the spatial texture information is extracted from the top of the principal component analysis for hyperspectral images by a curvature filter (CF). Second, the spatial correlation information of a hyperspectral image is completed by using domain transform recursive filter (DTRF). Last, the spatial texture information and correlation information are fused to be classified with LDM. The experimental results of hyperspectral images classification demonstrate that the proposed curvature filter and domain transform recursive filter with LDM(CFDTRF-LDM) method is superior to other classification methods.

scholarly journals Spectral-Spatial Attention Networks for Hyperspectral Image Classification

2019 ◽  
Vol 11 (8) ◽  
pp. 963 ◽  
Author(s):  
Xiaoguang Mei ◽  
Erting Pan ◽  
Yong Ma ◽  
Xiaobing Dai ◽  
Jun Huang ◽  
...  
Keyword(s):  
Neural Network ◽  
Image Classification ◽  
Spatial Attention ◽  
Spatial Information ◽  
Hyperspectral Image ◽  
Spatial Dimension ◽  
Hyperspectral Image Classification ◽  
Attention Networks ◽  
Land Covers

Many deep learning models, such as convolutional neural network (CNN) and recurrent neural network (RNN), have been successfully applied to extracting deep features for hyperspectral tasks. Hyperspectral image classification allows distinguishing the characterization of land covers by utilizing their abundant information. Motivated by the attention mechanism of the human visual system, in this study, we propose a spectral-spatial attention network for hyperspectral image classification. In our method, RNN with attention can learn inner spectral correlations within a continuous spectrum, while CNN with attention is designed to focus on saliency features and spatial relevance between neighboring pixels in the spatial dimension. Experimental results demonstrate that our method can fully utilize the spectral and spatial information to obtain competitive performance.

scholarly journals Spectral-Spatial Hyperspectral Image Classification Based on Homogeneous Minimum Spanning Forest

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
F. Poorahangaryan ◽  
H. Ghassemian
Keyword(s):  
Image Classification ◽  
Spatial Information ◽  
Hyperspectral Image ◽  
Feature Space ◽  
Support Vector ◽  
Hyperspectral Image Classification ◽  
Spatial Classification ◽  
Spanning Forest ◽  
Minimum Spanning Forest ◽  
Initial Classification

The combination of spectral and spatial information is known as a suitable way to improve the accuracy of hyperspectral image classification. In this paper, we propose a spectral-spatial hyperspectral image classification approach composed of the following stages. Initially, the support vector machine (SVM) is applied to obtain the initial classification map. Then, we present a new index called the homogeneity order and, using that with K-nearest neighbors, we select some pixels in feature space. The extracted pixels are considered as markers for Minimum Spanning Forest (MSF) construction. The class assignment to the markers is done using the initial classification map results. In the final stage, MSF is applied to these markers, and a spectral-spatial classification map is obtained. Experiments performed on several real hyperspectral images demonstrate that the classification accuracies obtained by the proposed scheme are improved when compared to MSF-based spectral-spatial classification approaches.

scholarly journals Synergistic 2D/3D Convolutional Neural Network for Hyperspectral Image Classification

2020 ◽  
Vol 12 (12) ◽  
pp. 2033 ◽  
Author(s):  
Xiaofei Yang ◽  
Xiaofeng Zhang ◽  
Yunming Ye ◽  
Raymond Y. K. Lau ◽  
Shijian Lu ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Image Classification ◽  
Spatial Information ◽  
Hyperspectral Image ◽  
Feature Learning ◽  
Hyperspectral Image Classification ◽  
3 Dimensional ◽  
Recent Success ◽  
Hybrid Module

Accurate hyperspectral image classification has been an important yet challenging task for years. With the recent success of deep learning in various tasks, 2-dimensional (2D)/3-dimensional (3D) convolutional neural networks (CNNs) have been exploited to capture spectral or spatial information in hyperspectral images. On the other hand, few approaches make use of both spectral and spatial information simultaneously, which is critical to accurate hyperspectral image classification. This paper presents a novel Synergistic Convolutional Neural Network (SyCNN) for accurate hyperspectral image classification. The SyCNN consists of a hybrid module that combines 2D and 3D CNNs in feature learning and a data interaction module that fuses spectral and spatial hyperspectral information. Additionally, it introduces a 3D attention mechanism before the fully-connected layer which helps filter out interfering features and information effectively. Extensive experiments over three public benchmarking datasets show that our proposed SyCNNs clearly outperform state-of-the-art techniques that use 2D/3D CNNs.

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