3D Iterative Spatiotemporal Filtering for Classification of
Multitemporal Satellite Data Sets
- URL: http://arxiv.org/abs/2107.00590v1
- Date: Thu, 1 Jul 2021 16:26:52 GMT
- Title: 3D Iterative Spatiotemporal Filtering for Classification of
Multitemporal Satellite Data Sets
- Authors: Hessah Albanwan, Rongjun Qin, Xiaohu Lu, Mao Li, Desheng Liu,
Jean-Michel Guldmann
- Abstract summary: 3D geometric features have been shown to be stable for assessing differences across the temporal data set.
In this article we investigate he use of a multitemporal orthophoto and digital surface model derived from satellite data fortemporal classification.
- Score: 4.6998356311022285
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The current practice in land cover/land use change analysis relies heavily on
the individually classified maps of the multitemporal data set. Due to varying
acquisition conditions (e.g., illumination, sensors, seasonal differences), the
classification maps yielded are often inconsistent through time for robust
statistical analysis. 3D geometric features have been shown to be stable for
assessing differences across the temporal data set. Therefore, in this article
we investigate he use of a multitemporal orthophoto and digital surface model
derived from satellite data for spatiotemporal classification. Our approach
consists of two major steps: generating per-class probability distribution maps
using the random-forest classifier with limited training samples, and making
spatiotemporal inferences using an iterative 3D spatiotemporal filter operating
on per-class probability maps. Our experimental results demonstrate that the
proposed methods can consistently improve the individual classification results
by 2%-6% and thus can be an important postclassification refinement approach.
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