Dual-Structure Elements Morphological Filtering and Local Z-Score Normalization for Infrared Small Target Detection against Heavy Clouds
<p>Flowchart of the proposed MMDLTH method.</p> "> Figure 2
<p>Illustration of dual-structure elements in eight directions.</p> "> Figure 3
<p>Fusion of multidirectional background suppression result.</p> "> Figure 4
<p>Schematic representation of the grayscale distribution of a weak target and clouds.</p> "> Figure 5
<p>Schematic representation of local variance in local Z-score.</p> "> Figure 6
<p>A dim target and 3D maps of the neighborhood size of <math display="inline"><semantics> <mrow> <mn>40</mn> <mo>×</mo> <mn>40</mn> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mn>20</mn> <mo>×</mo> <mn>20</mn> </mrow> </semantics></math> centered locally on it.</p> "> Figure 7
<p>Three-dimensional (3D) display before and after cloud suppression by local Z-score normalization.</p> "> Figure 8
<p>Schematic of the target and its local background.</p> "> Figure 9
<p>The PSCR and MSCR of the six tested sequences are presented in <a href="#remotesensing-16-02343-t002" class="html-table">Table 2</a>. (<b>a</b>–<b>f</b>) correspond to Seq1–Seq6, respectively.</p> "> Figure 10
<p>Background suppression ability on homogenized sky backgrounds. (<b>a</b>) Original infrared images (1)–(5); (<b>b</b>) 3D display of original images; (<b>c</b>) 3D display of background suppression results; (<b>d</b>) original infrared images (6)–(10); (<b>e</b>) 3D display of original images; (<b>f</b>) 3D display of background suppression results.</p> "> Figure 11
<p>Background suppression ability on complex sky backgrounds with heavy clouds. (<b>a</b>) Original infrared images (1)–(5); (<b>b</b>) 3D display of original image; (<b>c</b>) 3D display of background suppression results; (<b>d</b>) original infrared images (6)–(10); (<b>e</b>) 3D display of original image; (<b>f</b>) 3D display of background suppression results.</p> "> Figure 12
<p>Background suppression ability on sky backgrounds with the presence of multiple targets. (<b>a</b>) Original infrared image (1)–(5); (<b>b</b>) 3D display of original image; (<b>c</b>) 3D display of background suppression results; (<b>d</b>) original infrared image (6)–(10); (<b>e</b>) 3D display of original image; (<b>f</b>) 3D display of background suppression results.</p> "> Figure 13
<p>Background suppression ability on sky backgrounds with low SCR (1 < MSCR < 2, d = 20) of small targets. (<b>a</b>) Original infrared image; (<b>b</b>) 3D display of original image; (<b>c</b>) 3D display of background suppression results; (<b>d</b>) original infrared image; (<b>e</b>) 3D display of original image; (<b>f</b>) 3D display of background suppression results.</p> "> Figure 14
<p>Background suppression ability on sky backgrounds with low SCR (MSCR < 1, d = 20) of small targets. (<b>a</b>) Original infrared image; (<b>b</b>) 3D display of original image; (<b>c</b>) 3D display of background suppression results; (<b>d</b>) original infrared image; (<b>e</b>) 3D display of original image; (<b>f</b>) 3D display of background suppression results.</p> "> Figure 15
<p>Four representative infrared images with low SCR targets.</p> "> Figure 16
<p>The detection results of the proposed algorithm and 9 baseline methods of images from <a href="#remotesensing-16-02343-f015" class="html-fig">Figure 15</a>.</p> "> Figure 17
<p>ROC curves of different algorithms in <a href="#remotesensing-16-02343-t002" class="html-table">Table 2</a>. (<b>a</b>–<b>f</b>) correspond to Seq1–Seq6, respectively.</p> "> Figure 18
<p>Four representative images with different features.</p> "> Figure A1
<p>The detection results of the first group of 8 images.</p> "> Figure A2
<p>The detection results of the second group of 8 images.</p> "> Figure A3
<p>The detection results of images (1) to (3).</p> "> Figure A4
<p>The detection results of images (4) to (6).</p> "> Figure A5
<p>The detection results of images (7) to (9).</p> "> Figure A6
<p>The 3D map of the proposed algorithm and 9 baseline methods for images (1) to (3).</p> "> Figure A7
<p>The 3D map of the proposed algorithm and 9 baseline methods for images (4) to (6).</p> "> Figure A8
<p>The 3D map of the proposed algorithm and 9 baseline methods for images (7) to (9).</p> ">
Abstract
:1. Introduction
- We introduce center-invariant rotating dual-structure elements as background suppression templates, which can effectively distinguish small targets from cloud backgrounds with multiple morphological structures.
- We employ a dynamically aware neighborhood scaling strategy to address the limitations of traditional methods that use predefined consistent neighborhood sizes, which often result in the loss of targets near clouds.
- We use a sub-region local normalization method similar to the Z-score to transform the global absolute grayscale difference to a relative grayscale difference. This method effectively suppresses anisotropic and high-variation clouds, highlighting extremely weak targets in homogeneous regions. Compared to traditional linear normalization methods, it is more robust in assessing background clutter complexity and is less affected by outliers, reducing their interference with algorithm performance.
2. Related Work
2.1. Methods Based on Background Modeling
2.2. Methods Based on Target Saliency
2.3. Methods Based on Low Rank and Sparse Assumption
3. Proposed Method
3.1. Background Suppression Based on Multidirectional Dual-Structure Elements Top Hat
3.2. Target Enhancement Based on Multi-Directional Fusion
3.3. Local Z-Score Normalization
3.4. Dynamic Awareness of Scale Strategy
3.5. Threshold Segmentation
4. Experimental Results and Analysis
4.1. Evaluation Metrics
4.2. Baseline Methods
4.3. Datasets
4.4. Qualitative Evaluation
4.4.1. Robustness to Various Scenes
4.4.2. Robustness to Multiple Targets
4.4.3. Robustness to Low SCR Targets
4.4.4. Visual Comparison with Baselines
4.5. Quantitative Evaluation
5. Discussion
6. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
Appendix A
Appendix B
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Algorithm Name | Parameter Settings |
---|---|
TOPHAT | Window size: , Window shape: disk |
MNWTH | Window shape: disk, |
MDRTH | Window shape: disk, |
LCM | Size of u: |
MPCM | |
IPI | Sliding step: 10, Patch size: |
NRAM | Sliding step: 10, Patch size: |
RIPT | Sliding step: 10, Patch size: , |
PSTNN | Sliding step: 40, Patch size: , |
PROPOSED | Window size: |
Sequences | Length | Image Size | Target Size | Description |
---|---|---|---|---|
Seq1 | 315 | – | Images in SIRST dataset. Targets with different characteristics and backgrounds for different scenarios. | |
Seq2 | 76 | Sequential images of scene 1 with a complex background structure and a target moving in the background. | ||
Seq3 | 71 | Sequential images of scene 2 with a complex background structure and a target moving in the background. | ||
Seq4 | 90 | Cloudy backgrounds for different scenes, with larger but more gentle clouds. | ||
Seq5 | 80 | Cloudy background for different scenes with more undulating clouds and weaker targets. | ||
Seq6 | 80 | Cloud background for different scenes with more types of cloud morphology structures and weaker targets. |
Methods | Metrics | Seq1 | Seq2 | Seq 3 | Seq4 | Seq5 | Seq6 |
---|---|---|---|---|---|---|---|
TOPHAT | SCRG | 13.20 | 9.11 | 9.35 | 5.73 | 8.78 | 2.15 |
BSF | 19.89 | 2.98 | 5.63 | 136.75 | 4.55 | 5.88 | |
MNWTH | SCRG | 35.10 | 29.45 | 20.13 | 31.78 | 19.33 | 7.09 |
BSF | 21.78 | 9.45 | 11.36 | 8.12 | 3.95 | 4.66 | |
MRWTH | SCRG | INF | INF | INF | INF | INF | INF |
BSF | 2456.42 | 4982.77 | 4211.22 | 7209.91 | 1221.30 | 588.21 | |
LCM | SCRG | 1.75 | 1.33 | 1.98 | 0.45 | 1.77 | 2.03 |
BSF | 0.78 | 1.56 | 0.87 | 1.36 | 0.27 | 0.91 | |
MPCM | SCRG | 188.76 | 365.90 | 91.31 | 56.68 | 45.89 | 77.98 |
BSF | 29.25 | 56.33 | 31.88 | 39.62 | 41.13 | 25.34 | |
IPI | SCRG | INF | INF | INF | INF | INF | INF |
BSF | 1235.79 | 58.34 | 78.46 | 133.55 | 27.90 | 29.45 | |
NRAM | SCRG | INF | – | – | – | – | – |
BSF | 3998.45 | – | – | – | – | – | |
RIPT | SCRG | INF | INF | INF | INF | INF | INF |
BSF | INF | INF | INF | INF | INF | INF | |
PSTNN | SCRG | INF | INF | INF | INF | INF | INF |
BSF | INF | INF | INF | INF | INF | INF | |
PROPOSED | SCRG | INF | INF | INF | INF | INF | INF |
BSF | 82,457.33 | 61,225.76 | 59,987.24 | 87,734.01 | 39,128.50 | 96,342.12 |
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Peng, L.; Lu, Z.; Lei, T.; Jiang, P. Dual-Structure Elements Morphological Filtering and Local Z-Score Normalization for Infrared Small Target Detection against Heavy Clouds. Remote Sens. 2024, 16, 2343. https://doi.org/10.3390/rs16132343
Peng L, Lu Z, Lei T, Jiang P. Dual-Structure Elements Morphological Filtering and Local Z-Score Normalization for Infrared Small Target Detection against Heavy Clouds. Remote Sensing. 2024; 16(13):2343. https://doi.org/10.3390/rs16132343
Chicago/Turabian StylePeng, Lingbing, Zhi Lu, Tao Lei, and Ping Jiang. 2024. "Dual-Structure Elements Morphological Filtering and Local Z-Score Normalization for Infrared Small Target Detection against Heavy Clouds" Remote Sensing 16, no. 13: 2343. https://doi.org/10.3390/rs16132343