Lattice Auto-Associative Memories Induced Multivariate Morphology for Hyperspectral Image Spectral-Spatial Classification

2019 ◽  
pp. 316-325
Author(s):  
Manuel Graña
Keyword(s):  
Hyperspectral Image ◽  
Associative Memories ◽  
Spatial Classification

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.

Spectral-Spatial Classification of Hyperspectral Image Based on Support Vector Machine

2021 ◽  
Vol 16 (1) ◽  
pp. 56-74
Author(s):  
Weiwei Yang ◽  
Haifeng Song
Keyword(s):  
Support Vector Machine ◽  
Classification Accuracy ◽  
Spatial Information ◽  
Hyperspectral Image ◽  
State Of The Art ◽  
Support Vector ◽  
Svm Classifier ◽  
Spatial Classification ◽  
Homogeneous Regions

Recent research has shown that integration of spatial information has emerged as a powerful tool in improving the classification accuracy of hyperspectral image (HSI). However, partitioning homogeneous regions of the HSI remains a challenging task. This paper proposes a novel spectral-spatial classification method inspired by the support vector machine (SVM). The model consists of spectral-spatial feature extraction channel (SSC) and SVM classifier. SSC is mainly used to extract spatial-spectral features of HSI. SVM is mainly used to classify the extracted features. The model can automatically extract the features of HSI and classify them. Experiments are conducted on benchmark HSI dataset (Indian Pines). It is found that the proposed method yields more accurate classification results compared to the state-of-the-art techniques.

Spectral-spatial classification for hyperspectral image based on a single GRU

2020 ◽  
Vol 387 ◽  
pp. 150-160 ◽  
Author(s):  
Erting Pan ◽  
Xiaoguang Mei ◽  
Quande Wang ◽  
Yong Ma ◽  
Jiayi Ma
Keyword(s):  
Hyperspectral Image ◽  
Spatial Classification

scholarly journals Spectral and Spatial Classification of Hyperspectral Images Based on Random Multi-Graphs

2018 ◽  
Vol 10 (8) ◽  
pp. 1271 ◽  
Author(s):  
Feng Gao ◽  
Qun Wang ◽  
Junyu Dong ◽  
Qizhi Xu
Keyword(s):  
Image Classification ◽  
Spatial Information ◽  
Hyperspectral Image ◽  
Small Sample ◽  
Hyperspectral Data ◽  
Hyperspectral Images ◽  
High Dimensional ◽  
Hyperspectral Image Classification ◽  
Spatial Classification ◽  
Spatial Features

Hyperspectral image classification has been acknowledged as the fundamental and challenging task of hyperspectral data processing. The abundance of spectral and spatial information has provided great opportunities to effectively characterize and identify ground materials. In this paper, we propose a spectral and spatial classification framework for hyperspectral images based on Random Multi-Graphs (RMGs). The RMG is a graph-based ensemble learning method, which is rarely considered in hyperspectral image classification. It is empirically verified that the semi-supervised RMG deals well with small sample setting problems. This kind of problem is very common in hyperspectral image applications. In the proposed method, spatial features are extracted based on linear prediction error analysis and local binary patterns; spatial features and spectral features are then stacked into high dimensional vectors. The high dimensional vectors are fed into the RMG for classification. By randomly selecting a subset of features to create a graph, the proposed method can achieve excellent classification performance. The experiments on three real hyperspectral datasets have demonstrated that the proposed method exhibits better performance than several closely related methods.

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