A Hybrid Kernel-Based Change Detection Method for Remotely Sensed Data in a Similarity Space
"> Figure 1
<p>Block diagram of proposed hybrid kernel-based change detection algorithm.</p> "> Figure 2
<p>Schematic diagram of support vector data description. The hyper-sphere is described by the center a and radius R [<a href="#B39-remotesensing-07-12829" class="html-bibr">39</a>].</p> "> Figure 3
<p>Spectral angle (θ) and distance similarity measures (d). Vector d<sub>i</sub> and q represent the value of bi-temporal pair of images and reference change vector respectively.</p> "> Figure 4
<p>True color images acquired by Landsat satellite over the Prince George area in Northern British Columbia (<b>a</b>), before (<b>b</b>) and after (<b>c</b>) the logging activities on 1990 and 1999, respectively [<a href="#B47-remotesensing-07-12829" class="html-bibr">47</a>].</p> "> Figure 5
<p>True color images acquired by Quickbird satellite over Indonesia (<b>a</b>), before and after the tsunami on April 2004 (<b>b</b>) and January 2005 (<b>c</b>) [<a href="#B49-remotesensing-07-12829" class="html-bibr">49</a>].</p> "> Figure 6
<p>Change maps obtained from (<b>a</b>) DFSS-SIM-Linear, (<b>b</b>) DFSS-SIM-RBF, (<b>c</b>) DFSS-SIM-Sigmoid, (<b>d</b>) DFHS-SIM-Linear, (<b>e</b>) Heap-SPC-Linear, (<b>f</b>) Heap-SPC-Polynomial, (<b>g</b>) Heap-SPC-RBF, (<b>h</b>) Heap-SPC-Sigmoid, (<b>i</b>) SVDD-Linear, (<b>j</b>) SVDD-Polynomial, (<b>k</b>) SVDD-RBF, (<b>l</b>) SVDD-Sigmoid approaches.</p> "> Figure 7
<p>Change maps obtained from (<b>a</b>) DFSS-SIM-Linear, (<b>b</b>) DFSS-SIM-RBF, (<b>c</b>) DFSS-SIM-Sigmoid, (<b>d</b>) DFHS-SIM-Linear, (<b>e</b>) DFHS-SIM-RBF, (<b>f</b>) DFHS-SIM-Sigmoid, (<b>g</b>) Heap-SPC-Linear, (<b>h</b>) Heap-SPC-Polynomial, (<b>i</b>) Heap-SPC-RBF, (<b>j</b>) SVDD-Linear, (<b>k</b>) SVDD-Polynomial, (<b>l</b>) SVDD-RBF approaches.</p> "> Figure 8
<p>Optimal multi-temporal cross kernel parameters selection in DFHS-RBF approach for Quickbird (<b>a</b>) and Landsat (<b>b</b>) data sets.</p> "> Figure 9
<p>Computational cost analysis of classical CD and proposed CD methods for Quickbird and Landsat datasets when thresholding stage made through an operator in classical CD methods (first scenario).</p> "> Figure 10
<p>Computational cost analysis of classical CD and proposed CD methods for Quickbird and Landsat datasets when the thresholding stage was pre-made in classical CD methods (second scenario).</p> ">
Abstract
:1. Introduction
2. Methodology
2.1. Proposed Framework
2.2. Thresholding Scheme
2.3. Kernel K-Means Clustering
2.4. Support Vector Data Description
2.5. Kernel Minimum Distance Classifier
2.6. Similarity Space Transformation
2.7. Improved Kernel Parameter Selection
- (1)
- Local kernels. In this kernel type, only the data that are close or in the proximity of each others have an influence on the kernel values. Basically, all kernels that are based on a distance function are local kernels. Examples of typical local kernels are: Radial Basis, KMOD, and Inverse Multi-quadric.
- (2)
- Global kernels. In this kernel type, samples that are far away from each other still have an influence on the kernel value. All kernels based on the dot-product are global:
3. Experiments
3.1. Remote Sensing Data
3.2. Experimental Results
3.2.1. Proposed Kernel-Based CD Method
Classifier | Input Bands | Kernel Type | DFSS-SIM | DFHS-SIM | |||||||
---|---|---|---|---|---|---|---|---|---|---|---|
Quickbird | Landsat | Quickbird | Landsat | Quickbird | Landsat | ||||||
Kappa | OA | Kappa | OA | Kappa | OA | Kappa | OA | ||||
KMD | SDACV | SDACV | Linear | 0.85 | 90.47 | 0.89 | 95.38 | 0.86 | 90.62 | 0.90 | 96.74 |
KMD | SDACV | SDACV | Polynomial | 0.68 | 71.44 | 0.71 | 76.49 | 0.70 | 73.63 | 0.73 | 78.29 |
KMD | SDACV | SDACV | RBF | 0.90 | 94.39 | 0.89 | 95.67 | 0.74 | 78.21 | 0.82 | 87.64 |
KMD | SDACV | SDACV | Sigmoid | 0.87 | 91.81 | 0.79 | 85.27 | 0.68 | 71.60 | 0.88 | 94.58 |
Classifier | Input Bands | Kernel Type | Heap-SPC | ||||
---|---|---|---|---|---|---|---|
Quickbird | Landsat | Quickbird | Landsat | ||||
Kappa | O.A. | Kappa | O.A. | ||||
KMD | Bands 1,2,3,4, µ_b1, σ_b1, NDVI | Bands 1,3,4,5,7 NDVI | Linear | 0.90 | 94.89 | 0.78 | 83.63 |
KMD | Bands 1,2,3,4, µ_b1, σ_b1, NDVI | Bands 1,3,4,5,7 NDVI | Polynomial | 0.83 | 87.32 | 0.80 | 86.12 |
KMD | Bands 1,2,3,4, µ_b1, σ_b1, NDVI | Bands 1,3,4,5,7 NDVI | RBF | 0.92 | 96.73 | 0.77 | 83.14 |
KMD | Bands 1,2,3,4, µ_b1, σ_b1, NDVI | Bands 1,3,4,5,7 NDVI | Sigmoid | 0.81 | 85.47 | 0.70 | 75.40 |
3.2.2. Proposed SVDD-Based CD Method
CD Method | Input Bands | Kernel Type | Quickbird | Landsat | |||
---|---|---|---|---|---|---|---|
Quickbird | Landsat | Kappa | O.A. | Kappa | O.A. | ||
SVDD-based | Bands 1,2,3,4, µ_b1, σ_b1, NDVI | Bands 1,3,4,5,7 NDVI | Linear | 0.86 | 90.28 | 0.84 | 90.54 |
SVDD-based | Bands 1,2,3,4, µ_b1, σ_b1, NDVI | Bands 1,3,4,5,7 NDVI | Polynomial | 0.88 | 92.07 | 0.82 | 88.41 |
SVDD-based | Bands 1,2,3,4, µ_b1, σ_b1, NDVI | Bands 1,3,4,5,7 NDVI | RBF | 0.91 | 95.28 | 0.90 | 96.22 |
SVDD-based | Bands 1,2,3,4, µ_b1, σ_b1, NDVI | Bands 1,3,4,5,7 NDVI | Sigmoid | 0.80 | 84.11 | 0.74 | 79.35 |
3.2.3. Conventional CD Method
CD Method | Input Bands | Quickbird | Landsat | |||
---|---|---|---|---|---|---|
Quickbird | Landsat | |||||
Kappa | O.A. | Kappa | O.A. | |||
MNF Transform | Bands 1,2,3,4, µ_b1, σ_b1, NDVI | Bands 1,3,4,5,7 NDVI | 0.84 | 91.60 | 0.64 | 72.97 |
ICA Transform | Bands 1,2,3,4, µ_b1, σ_b1, NDVI | Bands 1,3,4,5,7 NDVI | 0.68 | 85.48 | 0.66 | 75.54 |
Spectral Angle Mapper | Bands 1,2,3,4, µ_b1, σ_b1, NDVI | Bands 1,3,4,5,7 NDVI | 0.60 | 75.38 | 0.77 | 87.80 |
Image Subtraction | Bands 1,2,3,4, µ_b1, σ_b1, NDVI | Bands 1,3,4,5,7 NDVI | 0.69 | 86.57 | 0.61 | 69.40 |
Image Rationing | Bands 1,2,3,4, µ_b1, σ_b1, NDVI | Bands 1,3,4,5,7 NDVI | 0.61 | 76.45 | 0.63 | 72.17 |
DFSS-SIM-RBF | SDACV | SDACV | 0.90 | 94.39 | 0.89 | 95.67 |
DFHS-SIM-RBF | SDACV | SDACV | 0.74 | 78.21 | 0.82 | 87.64 |
Heap-SPC-RBF | Bands 1,2,3,4, µ_b1, σ_b1, NDVI | Bands 1,3,4,5,7 NDVI | 0.92 | 96.73 | 0.77 | 83.14 |
SVDD-RBF | Bands 1,2,3,4, µ_b1, σ_b1, NDVI | Bands 1,3,4,5,7 NDVI | 0.91 | 95.28 | 0.90 | 96.22 |
4. Conclusions
Acknowledgements
Author Contributions
Conflicts of Interest
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Shah-Hosseini, R.; Homayouni, S.; Safari, A. A Hybrid Kernel-Based Change Detection Method for Remotely Sensed Data in a Similarity Space. Remote Sens. 2015, 7, 12829-12858. https://doi.org/10.3390/rs71012829
Shah-Hosseini R, Homayouni S, Safari A. A Hybrid Kernel-Based Change Detection Method for Remotely Sensed Data in a Similarity Space. Remote Sensing. 2015; 7(10):12829-12858. https://doi.org/10.3390/rs71012829
Chicago/Turabian StyleShah-Hosseini, Reza, Saeid Homayouni, and Abdolreza Safari. 2015. "A Hybrid Kernel-Based Change Detection Method for Remotely Sensed Data in a Similarity Space" Remote Sensing 7, no. 10: 12829-12858. https://doi.org/10.3390/rs71012829