Spatio-Temporal Sub-Pixel Land Cover Mapping of Remote Sensing Imagery Using Spatial Distribution Information From Same-Class Pixels
| Autor: | Giles M. Foody, Yun Du, Xiaodong Li, Lihui Wang, Feng Ling, Rui Chen, Xiaohong Yang |
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| Rok vydání: | 2020 |
| Předmět: |
same-class pixel
sub-pixel mapping spatial distribution 010504 meteorology & atmospheric sciences Pixel Computer science 0211 other engineering and technologies 02 engineering and technology Land cover 01 natural sciences Ancillary data Data set Temporal resolution General Earth and Planetary Sciences lcsh:Q Spatial dependence lcsh:Science Image resolution Spatial analysis 021101 geological & geomatics engineering 0105 earth and related environmental sciences Remote sensing |
| Zdroj: | Remote Sensing, Vol 12, Iss 3, p 503 (2020) Remote Sensing; Volume 12; Issue 3; Pages: 503 |
| ISSN: | 2072-4292 |
| Popis: | The generation of land cover maps with both fine spatial and temporal resolution would aid the monitoring of change on the Earth’s surface. Spatio-temporal sub-pixel land cover mapping (STSPM) uses a few fine spatial resolution (FR) maps and a time series of coarse spatial resolution (CR) remote sensing images as input to generate FR land cover maps with a temporal frequency of the CR data set. Traditional STSPM selects spatially adjacent FR pixels within a local window as neighborhoods to model the land cover spatial dependence, which can be a source of error and uncertainty in the maps generated by the analysis. This paper proposes a new STSPM using FR remote sensing images that pre- and/or post-date the CR image as ancillary data to enhance the quality of the FR map outputs. Spectrally similar pixels within the locality of a target FR pixel in the ancillary data are likely to represent the same land cover class and hence such same-class pixels can provide spatial information to aid the analysis. Experimental results showed that the proposed STSPM predicted land cover maps more accurately than two comparative state-of-the-art STSPM algorithms. |
| Databáze: | OpenAIRE |
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