Map** Urban Impervious Surface by Fusing Optical and SAR Data at the Decision Level
Abstract
:1. Introduction
2. Data and the Study Area
2.1. The Study Area
2.2. Data Sources and Preprocessing
3. Methodology
3.1. Feature Extraction
3.2. Random Forest
- The RF does not over-fit to the training set.
- Compared to other classification algorithms, the RF can deal with the noise in the dataset.
- The RF can handle data of high dimensions and does not require the feature selection. It can process the discrete data as well as the continuous data and non-standardized datasets.
3.3. The Dempster–Shafer (D-S) Theory
3.3.1. The Construction of the Basic Probability Assignment (BPA) and Uncertainty Interval
3.3.2. Dempster’s Combinational Rule
3.4. Accuracy Assessment
4. Results
4.1. Land Cover Classification from the GF-1/Sentinel-1A Image/DS-Fusion
4.1.1. Land Cover Classification from the GF-1/Sentinel-1A Image
4.1.2. Fusion of Land Covers Derived from the GF-1 Image and the Sentinel-1A Image
4.2. Land Cover Classification from the GF-1 Image/Sentinel-1A Image with Features/D-S Fusion
4.2.1. Land Cover Classification from the GF-1 Image/Sentinel-1A Image with Features
4.2.2. Fusion of Land Covers Derived from the GF-1 and Sentinel-1A Images with Features
5. Discussion
5.1. The Classification Accuracy for Impervious Surfaces and Uncertainty Analysis
5.1.1. The Classification Accuracy for Impervious Surfaces
5.1.2. Uncertainty Analysis
5.2. Future Work
6. Conclusions
Acknowledgments
Author Contributions
Conflicts of Interest
References
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Texture | Equations | Description |
---|---|---|
Mean | Mean is the average value in the local window [50]. | |
Correlation | Correlation measures the gray level linear dependencies in the image. , are the variance values in the local window [50,51]. | |
Variance | It is the variance in the local window [51,52]. | |
Homogeneity | Homogeneity is the smoothness of the image texture [50,51]. | |
Contrast | Contrast measures the variations in the GLCM [50,51]. | |
Dissimilarity | Dissimilarity is similar to the contrast measurement [50,51]. | |
Entropy | Entropy is a measure of the degree of disorderliness in an image [50,52]. | |
Angular Second Moment | ASM is a measure of textural uniformity [50,52]. |
Source | IS_H | IS_L | W | VE | BL_H | BL_L | M(Θ) | |
---|---|---|---|---|---|---|---|---|
Classes | ||||||||
Optical (m1()) | 0 | 0 | 0.91 | 0 | 0 | 0 | 0.09 | |
SAR (m2()) | 0.02 | 0 | 0.29 | 0.02 | 0.03 | 0.42 | 0.22 | |
m1(A1) m2(A2) | 0 | 0 | 0.89 | 0 | 0 | 0.07 | 0.04 | |
Combination Results | A W |
Classes | IS_H | IS_L | W | VE | BL_H | BL_L |
---|---|---|---|---|---|---|
(a) GF-1 | ||||||
IS_H | 67 | 0 | 0 | 0 | 0 | 0 |
IS_L | 9 | 75 | 6 | 0 | 4 | 17 |
W | 0 | 0 | 49 | 0 | 0 | 0 |
VE | 0 | 0 | 0 | 56 | 0 | 0 |
BL_H | 4 | 0 | 0 | 0 | 59 | 0 |
BL_L | 0 | 11 | 0 | 0 | 4 | 46 |
OA | 86.49% | KAPPA | 0.84 | |||
(b) Sentinel-1A | ||||||
IS_H | 23 | 9 | 3 | 7 | 12 | 12 |
IS_L | 23 | 42 | 0 | 7 | 15 | 2 |
W | 3 | 1 | 41 | 1 | 1 | 4 |
VE | 14 | 15 | 1 | 16 | 7 | 10 |
BL_H | 9 | 12 | 8 | 8 | 13 | 19 |
BL_L | 8 | 7 | 2 | 17 | 19 | 16 |
OA | 37.10% | KAPPA | 0.24 | |||
(c) DS-fusion | ||||||
IS_H | 70 | 2 | 0 | 0 | 3 | 3 |
IS_L | 7 | 81 | 0 | 0 | 5 | 10 |
W | 0 | 0 | 55 | 0 | 0 | 0 |
VE | 1 | 0 | 0 | 56 | 0 | 0 |
BL_H | 2 | 0 | 0 | 0 | 54 | 0 |
BL_L | 0 | 3 | 0 | 0 | 5 | 50 |
OA | 89.93% | KAPPA | 0.88 |
Classes | IS_H | IS_L | W | VE | BL_H | BL_L |
---|---|---|---|---|---|---|
(a) GF-1 and features | ||||||
IS_H | 71 | 0 | 0 | 0 | 0 | 1 |
IS_L | 9 | 75 | 0 | 0 | 4 | 16 |
W | 0 | 0 | 55 | 0 | 0 | 0 |
VE | 0 | 0 | 0 | 56 | 0 | 0 |
BL_H | 0 | 0 | 0 | 0 | 58 | 0 |
BL_L | 0 | 11 | 0 | 0 | 5 | 46 |
OA | 88.70% | KAPPA | 0.86 | |||
(b) Sentinel-1A and features | ||||||
IS_H | 20 | 4 | 2 | 4 | 9 | 8 |
IS_L | 34 | 58 | 0 | 10 | 19 | 1 |
W | 6 | 0 | 53 | 0 | 0 | 5 |
VE | 9 | 15 | 0 | 17 | 6 | 8 |
BL_H | 7 | 9 | 0 | 17 | 20 | 32 |
BL_L | 4 | 0 | 0 | 8 | 13 | 9 |
OA | 43.49% | KAPPA | 0.32 | |||
(c) DS-fusion and features | ||||||
IS_H | 71 | 0 | 0 | 0 | 0 | 1 |
IS_L | 9 | 86 | 0 | 0 | 4 | 14 |
W | 0 | 0 | 55 | 0 | 0 | 0 |
VE | 0 | 0 | 0 | 56 | 0 | 0 |
BL_H | 0 | 0 | 0 | 0 | 60 | 0 |
BL_L | 0 | 0 | 0 | 0 | 3 | 48 |
OA | 92.38% | KAPPA | 0.91 |
GF-1 | Sentinel-1A | DS-Fusion | GF-1 and Features | Sentinel-1A and Features | DS Fusion and Features | |||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
Classes | IS | NIS | IS | NIS | IS | NIS | IS | NIS | IS | NIS | IS | NIS |
IS | 151 | 27 | 97 | 58 | 160 | 21 | 155 | 21 | 116 | 53 | 166 | 19 |
NIS | 15 | 214 | 69 | 183 | 6 | 220 | 11 | 220 | 50 | 188 | 0 | 222 |
Kappa | 0.79 | 0.35 | 0.87 | 0.84 | 0.48 | 0.91 | ||||||
OA | 89.68% | 68.80% | 93.37% | 92.14% | 74.70% | 95.33% |
Minimum | Maximum | Mean | Standard Deviation | |
---|---|---|---|---|
Fusing the GF-1 and Sentinel-1A images | 0 | 0.25 | 0.11 | 0.088 |
Fusing the GF-1 and Sentinel-1A images and their features | 0 | 0.25 | 0.11 | 0.093 |
Uncertainty Value Range | Fusing the GF-1 and Sentinel-1A Images | Fusing the GF-1 and Sentinel-1A Images and Their Features |
---|---|---|
The Number of Pixels | ||
0.00–0.10 | 2,888,513 | 2,944,986 |
0.10–0.20 | 2,546,273 | 2,297,449 |
0.20–0.25 | 1,098,349 | 1,290,700 |
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Shao, Z.; Fu, H.; Fu, P.; Yin, L. Map** Urban Impervious Surface by Fusing Optical and SAR Data at the Decision Level. Remote Sens. 2016, 8, 945. https://doi.org/10.3390/rs8110945
Shao Z, Fu H, Fu P, Yin L. Map** Urban Impervious Surface by Fusing Optical and SAR Data at the Decision Level. Remote Sensing. 2016; 8(11):945. https://doi.org/10.3390/rs8110945
Chicago/Turabian StyleShao, Zhenfeng, Huyan Fu, Peng Fu, and Li Yin. 2016. "Map** Urban Impervious Surface by Fusing Optical and SAR Data at the Decision Level" Remote Sensing 8, no. 11: 945. https://doi.org/10.3390/rs8110945