# 3D Iterative Spatiotemporal Filtering for Classification of Multitemporal Satellite Data Sets

arXiv: https://arxiv.org/abs/2107.00590 · HTML: https://mlsanity.com/paper/2107.00590/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 38 references that are in the map.
- First posted on arXiv 1 Jul 2021.

## Builds on (most cited first, 1 of 1)

- [A Spatial and Temporal Non-Local Filter Based Data Fusion](https://mlsanity.com/paper/1611.07231/) (2016)

## Built on by (most cited first, 3 of 3)

- [Image Fusion in Remote Sensing: An Overview and Meta Analysis](https://mlsanity.com/paper/2401.08837/) (2024)
- [A Comparative Study on Deep-Learning Methods for Dense Image Matching of Multi-angle and Multi-date Remote Sensing Stereo Images](https://mlsanity.com/paper/2210.14031/) (2022)
- [Spatiotemporal Fusion in Remote Sensing](https://mlsanity.com/paper/2107.02701/) (2021)

## Abstract (arXiv)

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.