scholarly journals Locally Weighted Discriminant Analysis for Hyperspectral Image Classification

2019 ◽  
Vol 11 (2) ◽  
pp. 109 ◽  
Author(s):  
Xiaoyan Li ◽  
Lefei Zhang ◽  
Jane You
Keyword(s):  
Discriminant Analysis ◽  
Dimensionality Reduction ◽  
Image Classification ◽  
Matrix Model ◽  
Hyperspectral Image ◽  
Test Sample ◽  
Training Sample ◽  
Projection Matrix ◽  
Data Partition ◽  
Scatter Matrix

A hyperspectral image (HSI) contains a great number of spectral bands for each pixel, which will limit the conventional image classification methods to distinguish land-cover types of each pixel. Dimensionality reduction is an effective way to improve the performance of classification. Linear discriminant analysis (LDA) is a popular dimensionality reduction method for HSI classification, which assumes all the samples obey the same distribution. However, different samples may have different contributions in the computation of scatter matrices. To address the problem of feature redundancy, a new supervised HSI classification method based on locally weighted discriminant analysis (LWDA) is presented. The proposed LWDA method constructs a weighted discriminant scatter matrix model and an optimal projection matrix model for each training sample, which is on the basis of discriminant information and spatial-spectral information. For each test sample, LWDA searches its nearest training sample with spatial information and then uses the corresponding projection matrix to project the test sample and all the training samples into a low-dimensional feature space. LWDA can effectively preserve the spatial-spectral local structures of the original HSI data and improve the discriminating power of the projected data for the final classification. Experimental results on two real-world HSI datasets show the effectiveness of the proposed LWDA method compared with some state-of-the-art algorithms. Especially when the data partition factor is small, i.e., 0.05, the overall accuracy obtained by LWDA increases by about 20 % for Indian Pines and 17 % for Kennedy Space Center (KSC) in comparison with the results obtained when directly using the original high-dimensional data.

scholarly journals Spatial-Spectral Multiple Manifold Discriminant Analysis for Dimensionality Reduction of Hyperspectral Imagery

2019 ◽  
Vol 11 (20) ◽  
pp. 2414 ◽  
Author(s):  
Guangyao Shi ◽  
Hong Huang ◽  
Jiamin Liu ◽  
Zhengying Li ◽  
Lihua Wang
Keyword(s):  
Discriminant Analysis ◽  
Dimensionality Reduction ◽  
Spatial Information ◽  
Hyperspectral Image ◽  
Feature Learning ◽  
Spatial Domain ◽  
Classification Performance ◽  
Neighborhood Structure ◽  
Scatter Matrix ◽  
Manifold Structure

Hyperspectral images (HSI) possess abundant spectral bands and rich spatial information, which can be utilized to discriminate different types of land cover. However, the high dimensional characteristics of spatial-spectral information commonly cause the Hughes phenomena. Traditional feature learning methods can reduce the dimensionality of HSI data and preserve the useful intrinsic information but they ignore the multi-manifold structure in hyperspectral image. In this paper, a novel dimensionality reduction (DR) method called spatial-spectral multiple manifold discriminant analysis (SSMMDA) was proposed for HSI classification. At first, several subsets are obtained from HSI data according to the prior label information. Then, a spectral-domain intramanifold graph is constructed for each submanifold to preserve the local neighborhood structure, a spatial-domain intramanifold scatter matrix and a spatial-domain intermanifold scatter matrix are constructed for each sub-manifold to characterize the within-manifold compactness and the between-manifold separability, respectively. Finally, a spatial-spectral combined objective function is designed for each submanifold to obtain an optimal projection and the discriminative features on different submanifolds are fused to improve the classification performance of HSI data. SSMMDA can explore spatial-spectral combined information and reveal the intrinsic multi-manifold structure in HSI. Experiments on three public HSI data sets demonstrate that the proposed SSMMDA method can achieve better classification accuracies in comparison with many state-of-the-art methods.

scholarly journals A Robust Rule-Based Ensemble Framework Using Mean-Shift Segmentation for Hyperspectral Image Classification

2019 ◽  
Vol 11 (17) ◽  
pp. 2057 ◽  
Author(s):  
Majid Shadman Roodposhti ◽  
Arko Lucieer ◽  
Asim Anees ◽  
Brett Bryan
Keyword(s):  
Image Classification ◽  
Hyperspectral Image ◽  
Mean Shift ◽  
Training Sample ◽  
Uncertainty Assessment ◽  
Hyperspectral Image Classification ◽  
Rule Based ◽  
Classification Framework ◽  
Rule Sets ◽  
Ensemble Algorithms

This paper assesses the performance of DoTRules—a dictionary of trusted rules—as a supervised rule-based ensemble framework based on the mean-shift segmentation for hyperspectral image classification. The proposed ensemble framework consists of multiple rule sets with rules constructed based on different class frequencies and sequences of occurrences. Shannon entropy was derived for assessing the uncertainty of every rule and the subsequent filtering of unreliable rules. DoTRules is not only a transparent approach for image classification but also a tool to map rule uncertainty, where rule uncertainty assessment can be applied as an estimate of classification accuracy prior to image classification. In this research, the proposed image classification framework is implemented using three world reference hyperspectral image datasets. We found that the overall accuracy of classification using the proposed ensemble framework was superior to state-of-the-art ensemble algorithms, as well as two non-ensemble algorithms, at multiple training sample sizes. We believe DoTRules can be applied more generally to the classification of discrete data such as hyperspectral satellite imagery products.

scholarly journals Hyperspectral Imagery Classification Based on Multiscale Superpixel-Level Constraint Representation

2020 ◽  
Vol 12 (20) ◽  
pp. 3342
Author(s):  
Haoyang Yu ◽  
Xiao Zhang ◽  
Meiping Song ◽  
Jiaochan Hu ◽  
Qiandong Guo ◽  
...  
Keyword(s):  
Image Classification ◽  
Satellite Data ◽  
Hyperspectral Image ◽  
Training Sample ◽  
Decision Fusion ◽  
Spatial Constraint ◽  
Hyperspectral Image Classification ◽  
Object Based ◽  
Sparse Coefficient ◽  
The Individual

Sparse representation (SR)-based models have been widely applied for hyperspectral image classification. In our previously established constraint representation (CR) model, we exploited the underlying significance of the sparse coefficient and proposed the participation degree (PD) to represent the contribution of the training sample in representing the testing pixel. However, the spatial variants of the original residual error-driven frameworks often suffer the obstacles to optimization due to the strong constraints. In this paper, based on the object-based image classification (OBIC) framework, we firstly propose a spectral–spatial classification method, called superpixel-level constraint representation (SPCR). Firstly, it uses the PD in respect to the sparse coefficient from CR model. Then, transforming the individual PD to a united activity degree (UAD)-driven mechanism via a spatial constraint generated by the superpixel segmentation algorithm. The final classification is determined based on the UAD-driven mechanism. Considering that the SPCR is susceptible to the segmentation scale, an improved multiscale superpixel-level constraint representation (MSPCR) is further proposed through the decision fusion process of SPCR at different scales. The SPCR method is firstly performed at each scale, and the final category of the testing pixel is determined by the maximum number of the predicated labels among the classification results at each scale. Experimental results on four real hyperspectral datasets including a GF-5 satellite data verified the efficiency and practicability of the two proposed methods.

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