An Enhanced IT2FCM* Algorithm Integrating Spectral Indices and Spatial Information for Multi-Spectral Remote Sensing Image Clustering
Abstract
:1. Introduction
2. Preliminaries
2.1. Spatial Information in FCM
2.2. The Interval Type-2 Fuzzy Set
2.3. IT2 FCM*
3. Methodology
3.1. Spectral Indices
3.2. Spatial Information Measure
3.3. The enhanced IT2FCM*
4. Experimental Results and Discussion
4.1. Data Set and Study Area
4.2. The Effect of Spatial Information
4.3. The Effect of Spectral Indices
4.4. Combination Effect of Spatial Information and Spectral Indices
4.5. Discussion
5. Conclusions
Acknowledgments
Author Contributions
Conflicts of Interest
References
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Index | Equation | Remarks |
---|---|---|
NDVI [23] | ||
EVI [47] | ||
SAVI [24] | L = 0.5 | |
NDWI [25] | ||
MNDWI [48] | ||
AWEInsh [28] | ||
AWEIsh [28] | ||
NDBI [49] | ||
NDBaI [50] | Thermal Infrared |
Main Steps | Detail Steps |
---|---|
Step 1. Initialization | 1.1 Select suitable spectral indices according to the sensor type, research area, and major surface types, and then calculate these indices by suitable bands. |
1.2 Select the local window size W, usually , and then set values of control parameters and . | |
1.3 Select the parameters and () and the termination criterion value ε. | |
1.4 Initialize the lower and upper membership grade matrix using a random method. | |
Step 2. Compute all centroids of the original data and selected spectral indices and then calculate the spatial information and update the membership grade matrix. | 2.1 Calculate all centroids of the original data = 1, 2, …, C and spectral indices and determine their lower and upper bands and , respectively, via the KM algorithm. |
2.2 Calculate the comprehensive distance using Equation (18) and spatial information using Equation (19). | |
2.3 Calculate the new distance between the pixel k and the centroid i using Equation (20). | |
2.4 Update the lower and upper membership grade matrix using Equations (15) and (16). | |
2.5 Calculate the objective function via Equation (3). If , go to step 3; otherwise, go to step 2. | |
Step 3. Classify each sample using the interval number ranking method. | 3.1 Calculate the possibility matrix using Equation (16) and then obtain the ranking vector. |
3.2 Assign a sample to a cluster. | |
3.3 Report the clustering results. |
Index | FCM | IT2FCM | IT2FCM* | FGFCM | IIT2-FCM | IT2FCM*_S |
---|---|---|---|---|---|---|
PC- | 0.258 | 0.261 | 0.295 | 0.274 | 0.267 | 0.308 |
PE- | 1.478 | 1.415 | 1.405 | 1.446 | 1.395 | 1.380 |
XB- | 0.633 | 0.280 | 0.305 | 0.599 | 0.257 | 0.282 |
FS- | −6.212 × 106 | −8.433 × 106 | −1.279 × 107 | −6.990 × 107 | −1.173 × 107 | −1.707 × 107 |
ID | Spectral Indices | PC- | PE- | Overall Accuracy | Kappa Coefficient |
---|---|---|---|---|---|
1 | SAVI, AWEIsh | 0.301 | 1.393 | 87.92% | 0.843 |
2 | EVI, AWEIsh | 0.314 | 1.369 | 78.06% | 0.698 |
3 | EVI, SAWEIsh, MBI | 0.304 | 1.393 | 77.13% | 0.700 |
4 | SAVI, AWEIsh, MBI | 0.299 | 1.398 | 87.65% | 0.839 |
5 | SAVI, AWEIsh, NDBaI | 0.293 | 1.409 | 86.49% | 0.825 |
6 | NDVI, SAVI, NDWI | 0.320 | 1.355 | 73.22% | 0.653 |
7 | EVI, SAVI, NDWI, MBI, AWEInsh, AWEIsh, NDBaI | 0.288 | 1.419 | 74.25% | 0.666 |
8 | NDVI, NDWI, NDBaI | 0.310 | 1.373 | 68.65% | 0.591 |
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Guo, J.; Huo, H. An Enhanced IT2FCM* Algorithm Integrating Spectral Indices and Spatial Information for Multi-Spectral Remote Sensing Image Clustering. Remote Sens. 2017, 9, 960. https://doi.org/10.3390/rs9090960
Guo J, Huo H. An Enhanced IT2FCM* Algorithm Integrating Spectral Indices and Spatial Information for Multi-Spectral Remote Sensing Image Clustering. Remote Sensing. 2017; 9(9):960. https://doi.org/10.3390/rs9090960
Chicago/Turabian StyleGuo, Jifa, and Hongyuan Huo. 2017. "An Enhanced IT2FCM* Algorithm Integrating Spectral Indices and Spatial Information for Multi-Spectral Remote Sensing Image Clustering" Remote Sensing 9, no. 9: 960. https://doi.org/10.3390/rs9090960