Improvement of Classification Using a Joint Spectral Dimensionality Reduction and Lower Rank Spatial Approximation for Hyperspectral Images

2007 ◽  
pp. 132-143 ◽  
Author(s):  
N. Renard ◽  
S. Bourennane ◽  
J. Blanc-Talon
Keyword(s):  
Dimensionality Reduction ◽  
Hyperspectral Images ◽  
Lower Rank ◽  
Spectral Dimensionality ◽  
Spatial Approximation

scholarly journals Investigating the Effects of a Combined Spatial and Spectral Dimensionality Reduction Approach for Aerial Hyperspectral Target Detection Applications

2021 ◽  
Vol 13 (9) ◽  
pp. 1647
Author(s):  
Fraser Macfarlane ◽  
Paul Murray ◽  
Stephen Marshall ◽  
Henry White
Keyword(s):  
Dimensionality Reduction ◽  
Target Detection ◽  
Effective Means ◽  
Reduction Process ◽  
Hyperspectral Images ◽  
Detection Algorithms ◽  
Wide Range ◽  
Reduction Methods ◽  
Spectral Dimensionality ◽  
Spatial Redundancy

Target detection and classification is an important application of hyperspectral imaging in remote sensing. A wide range of algorithms for target detection in hyperspectral images have been developed in the last few decades. Given the nature of hyperspectral images, they exhibit large quantities of redundant information and are therefore compressible. Dimensionality reduction is an effective means of both compressing and denoising data. Although spectral dimensionality reduction is prevalent in hyperspectral target detection applications, the spatial redundancy of a scene is rarely exploited. By applying simple spatial masking techniques as a preprocessing step to disregard pixels of definite disinterest, the subsequent spectral dimensionality reduction process is simpler, less costly and more informative. This paper proposes a processing pipeline to compress hyperspectral images both spatially and spectrally before applying target detection algorithms to the resultant scene. The combination of several different spectral dimensionality reduction methods and target detection algorithms, within the proposed pipeline, are evaluated. We find that the Adaptive Cosine Estimator produces an improved F1 score and Matthews Correlation Coefficient when compared to unprocessed data. We also show that by using the proposed pipeline the data can be compressed by over 90% and target detection performance is maintained.

Automatic configuration of spectral dimensionality reduction methods

2010 ◽  
Vol 31 (12) ◽  
pp. 1720-1727 ◽  
Author(s):  
Michał Lewandowski ◽  
Dimitrios Makris ◽  
Jean-Christophe Nebel
Keyword(s):  
Dimensionality Reduction ◽  
Reduction Methods ◽  
Spectral Dimensionality

Dimensionality Reduction and Classification of Hyperspectral Images Using Object-Based Image Analysis

2018 ◽  
Vol 46 (8) ◽  
pp. 1297-1306 ◽  
Author(s):  
Taskin Kavzoglu ◽  
Hasan Tonbul ◽  
Merve Yildiz Erdemir ◽  
Ismail Colkesen
Keyword(s):  
Image Analysis ◽  
Dimensionality Reduction ◽  
Hyperspectral Images ◽  
Object Based Image Analysis ◽  
Object Based

Patch Tensor-Based Multigraph Embedding Framework for Dimensionality Reduction of Hyperspectral Images

2020 ◽  
Vol 58 (3) ◽  
pp. 1630-1643 ◽  
Author(s):  
Yang-Jun Deng ◽  
Heng-Chao Li ◽  
Xin Song ◽  
Yong-Jian Sun ◽  
Xiang-Rong Zhang ◽  
...  
Keyword(s):  
Dimensionality Reduction ◽  
Hyperspectral Images

Dimensionality Reduction of Hyperspectral Images Using Pooling

2019 ◽  
Vol 29 (1) ◽  
pp. 72-78 ◽  
Author(s):  
Arati Paul ◽  
Nabendu Chaki
Keyword(s):  
Dimensionality Reduction ◽  
Hyperspectral Images
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