Object-Based Window Strategy in Thermal Sharpening
"> Figure 1
<p>Window strategies. The background image is a 100 m-resolution LST derived from Landsat 8 data.</p> "> Figure 2
<p>Three different study areas and corresponding satellite data. (<b>a</b>) Land cover map of China obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS) yearly land cover product in 2017 (the MODIS image was downloaded from <a href="https://ladsweb.modaps.eosdis.nasa.gov/" target="_blank">https://ladsweb.modaps.eosdis.nasa.gov/</a>); (<b>b</b>) Land cover map of Hebei Province, where the black circles indicate the locations of the three study areas; (<b>c</b>) Study areas of different land cover types ranging from spring to winter; the images were obtained from the RGB true-color composite of Landsat 8.</p> "> Figure 3
<p>Simulated NDVI and LST data. The 10 m, 40 m and 100 m notations indicate the spatial resolutions of the simulated data. The 40 m and 100 m-resolution data were aggregated from the 10 m-resolution data. The image size of the 10 m-resolution data is 1000 × 1000.</p> "> Figure 4
<p>RMSEs of the OWS at different segmentation scales. The x-axis is the pixel count in an object-based window and represents the segmentation scales. The black point indicates the minimum RMSE value.</p> "> Figure 5
<p>Correlation between the downscaling ratio and the optimal number of pixels in the object-based window.</p> "> Figure 6
<p>Scatter plot of the RMSEs determined by the fitted line and the simplified fitted line. “sim line” in the y-axis means the simplified fitted line.</p> "> Figure 7
<p>RMSEs of the results of the GWS and LWS with the optimal accuracy and those of the OWS with the optimal accuracy. The black point indicates the minimum RMSE value among the GWS, LWS and OWS.</p> "> Figure 8
<p>Results of DLST for the urban area on May 7, 2017. (<b>a</b>) RGB true-color composite of Landsat 8; (<b>b</b>) subsets of (<b>a</b>); (<b>c</b>) reference LST; (<b>d</b>) GWS (300 m→100 m); (<b>e</b>) LWS (300 m→100 m); (<b>f</b>) OWS: (300 m→100 m); (<b>g</b>) GWS (600 m→100 m); (<b>h</b>) LWS (600 m→100 m); (<b>i</b>) OWS (600 m→100 m); (<b>j</b>) GWS (900 m→100 m); (<b>k</b>) LWS (900 m→100 m); (<b>l</b>) OWS (900 m→100 m).</p> "> Figure 9
<p>Results of DLST based on the simulated data. (<b>a</b>) GWS (40 m→10 m); (<b>b</b>) LWS (40 m→10 m); (<b>c</b>) OWS (40 m→10 m); (<b>d</b>) simulated LST; (<b>e</b>) GWS (100 m→10 m); (<b>f</b>) LWS (100 m→10 m); (<b>g</b>) OWS (100 m→10 m).</p> ">
Abstract
:1. Introduction
2. Method
2.1. Algorithm for DLST over Different Land Covers
2.2. Object-Based Window Strategy
3. Study Area and Data
4. Results
4.1. Relationship between the Optimal Window Size and the Downscaling Ratio
4.2. Comparisons with the LWS and GWS (Test with the Landsat 8 Data)
4.3. Comparisons with the LWS and GWS (Test with the Simulated Data)
5. Discussion
5.1. Advantages of the OWS
5.2. Other Issues
6. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
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Study Area | Acquisition Date (YY-MM-DD) | Path/Row | Altitude (m) |
---|---|---|---|
Forest | 2017-05-07, 2017-07-10, 2017-09-12, 2017-10-30 | 123/32 | 60–2000 |
Urban | 2017-05-07, 2017-07-10, 2017-09-12, 2017-10-30 | 123/32 | 20–2000 |
Cropland | 2016-04-18, 2017-07-10, 2017-10-30, 2017-12-17 | 123/34 | 10–50 |
Object | a0 | a1 | a2 |
---|---|---|---|
Circle | 38.5 | −10.0 | −6.0 |
Line | 37.4 | −9.7 | −6.1 |
Rectangle | 34.4 | −5.7 | −5.1 |
Background | 33.4 | −4.5 | −5.6 |
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Xia, H.; Chen, Y.; Quan, J.; Li, J. Object-Based Window Strategy in Thermal Sharpening. Remote Sens. 2019, 11, 634. https://doi.org/10.3390/rs11060634
Xia H, Chen Y, Quan J, Li J. Object-Based Window Strategy in Thermal Sharpening. Remote Sensing. 2019; 11(6):634. https://doi.org/10.3390/rs11060634
Chicago/Turabian StyleXia, Haiping, Yunhao Chen, Jinling Quan, and Jing Li. 2019. "Object-Based Window Strategy in Thermal Sharpening" Remote Sensing 11, no. 6: 634. https://doi.org/10.3390/rs11060634