Authors:
Safa A. Najim
1
and
Ik Soo Lim
2
Affiliations:
1
Bangor University and Basrah University, United Kingdom
;
2
Bangor University, United Kingdom
Keyword(s):
Visualization, Dimensionality Reduction, Remote Sensing Imagery, Graphics Processing Unit (GPU).
Related
Ontology
Subjects/Areas/Topics:
Abstract Data Visualization
;
Computer Vision, Visualization and Computer Graphics
;
Gpu-Based Visualization
;
High-Dimensional Data and Dimensionality Reduction
;
Spatial Data Visualization
Abstract:
This paper introduces a new technique called Sequential Dimensionality Reduction (SDR), to visualize remote
sensing imagery. The DR methods are introduced to project directly the high dimensional dataset into a low
dimension space. Although they work very well when original dimensions are small, their visualizations are
not efficient enough with large input dimensions. Unlike DR, SDR redefines the problem of DR as a sequence
of multiple dimensionality reduction problems, each of which reduces the dimensionality by a small amount.
The SDR can be considered as a generalized idea which can be applied to any method, and the stochastic
proximity embedding (SPE) method is chosen in this paper because its speed and efficiency compared to other
methods. The superiority of SDR over DR is demonstrated experimentally. Moreover, as most DR methods
also employ DR ideas in their projection, the performance of SDR and 20 DR methods are compared, and the
superiority of the proposed method in both co
rrelation and stress is shown. Graphics processing unit (GPU)
is the best way to speed up the SDR method, where the speed of execution has been increased by 74 times in
comparison to when it was run on CPU.
(More)