Designing a New Framework Using Type-2 FLS and Cooperative-Competitive Genetic Algorithms for Road Detection from IKONOS Satellite Imagery
Abstract
:1. Introduction
2. Related work
- -
- -
- The Michigan approach, in which each chromosome represents a single rule and the whole population forms the rule base [53].
- -
- -
- The cooperative-competitive learning (CCL) approach, in which the chromosomes compete and cooperate simultaneously [58,59,60,61]. This can be understood in two opposite ways: the individuals could collaborate for the same purpose and thus construct the solution together, or they could compete against each other for the same resources. The use of CCL algorithms is recommended when the following issues arise: the search space is huge, the problem may be decomposed into subcomponents or different coding schemes are used [62].
3. Type-2 Fuzzy Logic System (T2 FLS)
4. Proposed Method
4.1. Object-Based Feature Generation
4.2. Feature Selection by the GA
4.3. Designing and Tuning the FLSs
4.3.1. Initial Population Production
4.3.2. Fitness Function
4.3.3. Reproduction, Crossover and Mutation
4.4. Rule Reduction
4.5. Testing and Enhancement
4.6. Validation
5. Implementation and Analysis
5.1. Input Data
5.2. Object-Based Feature Selection
Spectral | Shape | Texture | Combination |
---|---|---|---|
Mean (for 4 bands:NIR,Red,Green,Blue) | Area | GLCM homogeneity (for 4 bands in all.dir) | Intensity/Density |
Brightness | Asymmetry | GLCM contrast (for 4 bands in all.dir) | Saturation/Density |
Max. diff | Shape index | GLCM dissimilarity (for 4 bands in all.dir) | Intensity/compactness |
Standard deviation (for 4 bands) | Roundness | GLCM entropy (for 4 bands in all.dir) | Saturation/shape Index |
Ratio (for 4 bands) | Rectangular fit | GLCM mean (for 4 bands in all.dir) | Intensity/Shape Index |
Contrast to neighbor (for 4 bands) | width | GLCM stddev (for 4 bands in all.dir) | Area × Max diff/ Border length |
Std. deviation to neighbor (for 4 bands) | Border index | GLCM ang. 2nd moment (for 4 bands in all.dir) | Ratio Red/Density |
Mean diff. to neighbors (for 4 bands) | length | Area × Ratio red/Border length | |
Mean diff. to darker neighbors (for 4 bands) | Density | Hue/Density | |
Mean diff. to brighter neighbors (for 4 bands) | Elliptic fit | Hue/Shape Index | |
Hue | Compactness | Max diff/Density | |
Intensity | Length/Width | Max diff/Rectangular Fit | |
saturation | Border length | Max diff/Shape Index | |
NDVI | Ratio Red/Rectangular Fit | ||
NDWI | Ratio Red/Shape Index |
NDVI | Standard deviation Blue | Hue | Brightness |
NDWI | GLCM Homogeneity Blue | Ratio Red | Saturation/Density |
Feature Description | |
---|---|
Brightness | Sum of the mean values of the layers containing spectral information divided by their quantity computed for an image object. |
Standard deviation | Calculated from the layer values of all n pixels forming an image object. |
Hue | The hue value of the HSI color space representing the gradation of color. |
Saturation | The saturation value of the HSI color space representing the intensity of a specific hue. Relative purity of color; pure spectrum colors are fully saturated. |
Ratio | The ratio of layer k reflects the amount that layer k contributes to the total brightness. |
GLCM Homogeneity | It is a measure of the amount of local homogeneity in the image object. |
NDVI | (NIR − Red) / (NIR + Red) |
NDWI | (Green − NIR) / (Green + NIR) |
Density | The density expressed by the area covered by the image object divided by its radius. |
5.3. Type 2 FLS Designing and Tuning
Accuracy | T2 FLS | T1 FLS | ||
---|---|---|---|---|
Road | Non Road | Road | Non Road | |
Completeness | 0.97 | 0.9733 | 0.97 | 0.9667 |
correctness | 0.9479 | 0.9848 | 0.9357 | 0.9848 |
Kappa | 0.9379 | 0.9282 |
5.4. Testing and Enhancement
Class | Test Images | T2 FLS | T1 FLS | |||||
---|---|---|---|---|---|---|---|---|
Completeness | Correctness | Kappa | Completeness | Correctness | Kappa | |||
Road | Kish | 0.8577 | 0.7926 | 0.8173 | 0.8587 | 0.7526 | 0.7946 | |
Hobart | 0.8897 | 0.8036 | 0.8323 | 0.8852 | 0.7773 | 0.8141 | ||
Non Road | Kish | 0.9920 | 0.9949 | 0.8173 | 0.9899 | 0.9949 | 0.7946 | |
Hobart | 0.9839 | 0.9918 | 0.8323 | 0.9812 | 0.9914 | 0.8141 |
Class | Test Images | T2 FLS | T1 FLS | ||||
---|---|---|---|---|---|---|---|
Completeness | Correctness | Kappa | Completeness | Correctness | Kappa | ||
Road | Kish | 0.8615 | 0.8433 | 0.8454 | 0.8635 | 0.8081 | 0.8273 |
Hobart | 0.8933 | 0.8455 | 0.8581 | 0.8897 | 0.8272 | 0.8457 | |
Non Road | Kish | 0.9943 | 0.9950 | 0.8454 | 0.9927 | 0.9951 | 0.8273 |
Hobart | 0.9879 | 0.9921 | 0.8581 | 0.9862 | 0.9918 | 0.8457 |
5.5. Validation
Class | Validation Images | T2 FLS | T1 FLS | ||||
---|---|---|---|---|---|---|---|
Completeness | Correctness | Kappa | Completeness | Correctness | Kappa | ||
Road | Shiraz | 0.8012 | 0.6961 | 0.7201 | 0.7840 | 0.5420 | 0.6018 |
Yazd | 0.8433 | 0.6675 | 0.7138 | 0.8307 | 0.4863 | 0.5589 | |
Hamadan1 | 0.7354 | 0.6801 | 0.6780 | 0.6561 | 0.6589 | 0.6255 | |
Hamadan2 | 0.8084 | 0.6464 | 0.6922 | 0.7218 | 0.5736 | 0.6056 | |
Non Road | Shiraz | 0.9683 | 0.9817 | 0.7201 | 0.9400 | 0.9796 | 0.6018 |
Yazd | 0.9543 | 0.9825 | 0.7138 | 0.9046 | 0.9801 | 0.5589 | |
Hamadan1 | 0.9676 | 0.9750 | 0.6780 | 0.9682 | 0.9678 | 0.6255 | |
Hamadan2 | 0.9634 | 0.9838 | 0.6922 | 0.9556 | 0.9765 | 0.6056 |
Class | Validation images | T2 FLS | T1 FLS | |||||
---|---|---|---|---|---|---|---|---|
Completeness | Correctness | Kappa | Completeness | Correctness | Kappa | |||
Road | Shiraz | 0.8034 | 0.7455 | 0.7520 | 0.7883 | 0.6167 | 0.6604 | |
Yazd | 0.8559 | 0.7043 | 0.7453 | 0.8357 | 0.5309 | 0.6015 | ||
Hamadan1 | 0.7406 | 0.7021 | 0.6939 | 0.6587 | 0.6870 | 0.6426 | ||
Hamadan2 | 0.8192 | 0.6813 | 0.7206 | 0.7326 | 0.6307 | 0.6490 | ||
Non Road | Shiraz | 0.9751 | 0.9821 | 0.7520 | 0.9556 | 0.9803 | 0.6604 | |
Yazd | 0.9609 | 0.9840 | 0.7453 | 0.9197 | 0.9810 | 0.6015 | ||
Hamadan1 | 0.9706 | 0.9756 | 0.6939 | 0.9719 | 0.9682 | 0.6426 | ||
Hamadan2 | 0.9683 | 0.9848 | 0.7206 | 0.9645 | 0.9776 | 0.6490 |
6. Conclusions
Acknowledgments
Author Contributions
Conflicts of Interest
References
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Nikfar, M.; Zoej, M.J.V.; Mokhtarzade, M.; Shoorehdeli, M.A. Designing a New Framework Using Type-2 FLS and Cooperative-Competitive Genetic Algorithms for Road Detection from IKONOS Satellite Imagery. Remote Sens. 2015, 7, 8271-8299. https://doi.org/10.3390/rs70708271
Nikfar M, Zoej MJV, Mokhtarzade M, Shoorehdeli MA. Designing a New Framework Using Type-2 FLS and Cooperative-Competitive Genetic Algorithms for Road Detection from IKONOS Satellite Imagery. Remote Sensing. 2015; 7(7):8271-8299. https://doi.org/10.3390/rs70708271
Chicago/Turabian StyleNikfar, Maryam, Mohammad Javad Valadan Zoej, Mehdi Mokhtarzade, and Mahdi Aliyari Shoorehdeli. 2015. "Designing a New Framework Using Type-2 FLS and Cooperative-Competitive Genetic Algorithms for Road Detection from IKONOS Satellite Imagery" Remote Sensing 7, no. 7: 8271-8299. https://doi.org/10.3390/rs70708271