Built-up Area Extraction from PolSAR Imagery with Model-Based Decomposition and Polarimetric Coherence
"> Figure 1
<p>Flowchart of the proposed built-up area extraction method.</p> "> Figure 2
<p>Study area and the PolSAR image acquired with the Phased Array type L-band Synthetic Aperture Radar (PALSAR) system: (<b>a</b>) San Francisco map, where the red rectangle indicates the imaging area; and (<b>b</b>) PolSAR image with Pauli color-coding (Red: |HH−VV|, Green: |HV|, Blue: |HH+VV|).</p> "> Figure 3
<p>Ground reference image (NLCD 2006 land cover 30 m) with seventeen classes.</p> "> Figure 4
<p>Results of our multiple-component decomposition method: (<b>a</b>) double-bounce scattering power; (<b>b</b>) helix scattering power; (<b>c</b>) cross scattering power; and (<b>d</b>) color-coded decomposition result. All of the scattering powers are recalculated in decibels by <math display="inline"> <semantics> <mrow> <mn>10</mn> <mo>×</mo> <msub> <mrow> <mi>log</mi> </mrow> <mrow> <mn>10</mn> </mrow> </msub> <mrow> <mo>(</mo> <mrow> <mtext>power</mtext> </mrow> <mo>)</mo> </mrow> </mrow> </semantics> </math>.</p> "> Figure 5
<p>Quantitative comparison among the double-bounce, helix and cross scattering powers from our decomposition for four selected areas in <a href="#remotesensing-08-00685-f004" class="html-fig">Figure 4</a>d.</p> "> Figure 6
<p>Magnitudes of correlation coefficients and histograms of selected patches: (<b>a</b>–<b>c</b>) the magnitudes of <math display="inline"> <semantics> <mrow> <msub> <mi>ρ</mi> <mrow> <mtext>HHHV</mtext> </mrow> </msub> </mrow> </semantics> </math>, <math display="inline"> <semantics> <mrow> <msub> <mi>ρ</mi> <mrow> <mtext>HHVV</mtext> </mrow> </msub> </mrow> </semantics> </math> , and <math display="inline"> <semantics> <mrow> <msub> <mi>ρ</mi> <mrow> <mrow> <mo>(</mo> <mrow> <mtext>HH</mtext> <mo>−</mo> <mtext>VV</mtext> </mrow> <mo>)</mo> </mrow> <mtext>HV</mtext> </mrow> </msub> </mrow> </semantics> </math> , respectively; and (<b>d</b>–<b>f</b>) the histograms of the four selected patches in (<b>a</b>–<b>c</b>), respectively.</p> "> Figure 7
<p>The proposed coherence coefficient ratio: (<b>a</b>) <math display="inline"> <semantics> <mrow> <msub> <mi>ρ</mi> <mrow> <mtext>ratio</mtext> </mrow> </msub> </mrow> </semantics> </math>; (<b>b</b>) <math display="inline"> <semantics> <mover accent="true"> <mi>ρ</mi> <mo>¯</mo> </mover> </semantics> </math>; and (<b>c</b>,<b>d</b>) histograms of the four selected patches in (<b>a</b>,<b>b</b>), respectively.</p> "> Figure 8
<p>Built-up area extraction using the scattering powers, the average coherence coefficient ratio and the FCP fusion, where the white represents the building areas and the black areas are the non-buildings: (<b>a</b>) detection results using <span class="html-italic">Condition 1</span>; (<b>b</b>) detection results using <span class="html-italic">Condition 2</span>; (<b>c</b>) fused detection results; and (<b>d</b>) urban ground truth from NLCD 2006 land cover data, where the red rectangle represents the study area.</p> "> Figure 9
<p>Built-up area extraction using the method in [<a href="#B9-remotesensing-08-00685" class="html-bibr">9</a>], where the white represents the building areas and the black areas are the non-buildings: (<b>a</b>) detection results; and (<b>b</b>) urban ground truth from NLCD 2006 land cover data, where the red rectangle represents the study area.</p> ">
Abstract
:1. Introduction
2. Methodology
2.1. Multiple-Component Decomposition with Cross Scattering Model
2.2. Sub-Aperture Decomposition
- Fourier transformation in azimuth direction.2D Fourier transform is utilized to transform a PolSAR image into the spectral domain.
- Frequency spectrum segmentation using a window function.The total frequency spectrum is divided into several regions centered on the specific spectral locations using a window function such as Hamming window.
- Each part of the spectrum is transformed back into the spatial domain using 2D inverse Fourier transform.Using a 2D inverse Fourier transform, every sub-spectrum is transformed back into the spatial domain, and thus we can get a sub-aperture image representing the focused PolSAR response around a specific spectral location.
2.3. Polarimetric Coherence Coefficient Ratio Based on Sub-Aperture Images
2.4. Built-up Area Extraction Method
- (1)
- Sub-aperture decomposition on the SLC PolSAR data to obtain sub-aperture images.
- (2)
- Multilooking and despecking on the original SLC PolSAR data and sub-aperture images to estimate accurate sample coherency matrix.
- (3)
- Polarimetric target decomposition on original PolSAR data using our method to get the double-bounce scattering power and cross scattering power. Coherence coefficient ratio calculation using the sub-aperture images.
- (4)
- Built-up area detection using Condition 1 and Condition 2. Fusion of two detection results using the FCP algorithm.
3. Experimental Data Description
4. Results and Discussions
4.1. Analysis of the Decomposed Scattering Powers on Built-up Area Detection
4.2. Analysis of the Average Coherence Coefficient Ratio on Built-up Area Detection
4.3. Comparison of the Detection Results with and without Fusion
4.4. Discussion on the Detection Thresholds
5. Conclusions
Acknowledgments
Author Contributions
Conflicts of Interest
References
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Date | Flight Direction | Incidence Angle | Band | Looks Number | Data Type |
---|---|---|---|---|---|
11 November 2009 | Ascending | 22° | L | Azimuth: 1 | Complex |
Range: 1 |
Area | ||
---|---|---|
A | 3.7284 | 3.9541 |
B | 1.3572 | 2.1536 |
C | 0.5425 | 0.3287 |
D | 0.1152 | 0.0852 |
Method | OA (%) | KC | UA (%) | PA (%) |
---|---|---|---|---|
Scattering powers | 83.12 | 0.6624 | 83.21 | 83.15 |
Average coherence coefficient ratio | 80.13 | 0.6025 | 80.24 | 80.63 |
FCP fusion | 86.91 | 0.7381 | 87.34 | 86.60 |
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Xiang, D.; Tang, T.; Hu, C.; Fan, Q.; Su, Y. Built-up Area Extraction from PolSAR Imagery with Model-Based Decomposition and Polarimetric Coherence. Remote Sens. 2016, 8, 685. https://doi.org/10.3390/rs8080685
Xiang D, Tang T, Hu C, Fan Q, Su Y. Built-up Area Extraction from PolSAR Imagery with Model-Based Decomposition and Polarimetric Coherence. Remote Sensing. 2016; 8(8):685. https://doi.org/10.3390/rs8080685
Chicago/Turabian StyleXiang, Deliang, Tao Tang, Canbin Hu, Qinghui Fan, and Yi Su. 2016. "Built-up Area Extraction from PolSAR Imagery with Model-Based Decomposition and Polarimetric Coherence" Remote Sensing 8, no. 8: 685. https://doi.org/10.3390/rs8080685