Feature Selection and Mislabeled Waveform Correction for Water–Land Discrimination Using Airborne Infrared Laser
Abstract
:1. Introduction
2. Materials and Methods
2.1. Research Area and Data Acquisition
2.2. Feature Selection
2.2.1. Single Waveform Feature
2.2.2. Combination of Waveform Features
2.3. Dual-Clustering Method for Mislabeled Waveform Correction
2.3.1. K-Means Clustering
- Step 1:
- Randomly select two points from X as the initial centroids of C1 and C2.
- Step 2:
- Calculate the Euclidean distance dj = ∥xi−μj∥ between point xi and centroids μ1 and μ2 and assign point xi to the nearest cluster.
- Step 3:
- Recalculate the centroids of all clusters using the reassigned points.
- Step 4:
- Repeat Steps 2 and 3 until μ1 and μ2 are stable.
2.3.2. DBSCAN Clustering
- Step 1
- Initialize the core point set Ω = ϕ, the number of clusters k = 0, the sample set unvisited Γ = D, and the cluster division C = ϕ.
- Step 2
- All core points can be found for i = 1, 2, …, m by following the steps:
- (1)
- The Eps-neighborhood subsample set NEps(xi) of the sample xi should be determined through distance measurement;
- (2)
- If the number of samples in the subsample set satisfies |NEps(xi)| ≥ MinPts, sample xi will be added to the core point set, that is, Ω = Ω ∪ {xi}.
- Step 3
- If the core point set Ω is equal to ϕ, then the algorithm would end. Subsequently, the cluster division C = {C1, C2, …, Ck}. Otherwise go to step 4.
- Step 4
- A core point o needs to be randomly selected from the core point set Ω. Then, the current core point queue Ωcur = {o}, the serial number k = k + 1, and the current cluster sample set Ck = {o}. The data set unvisited Γ is updated to Γ−{o}.
- Step 5
- If the current core point queue Ωcur is ϕ, cluster C would be {C1, C2, …, Ck} and the core point set Ω would be Ω−Ck. Then, go to Step 3. Otherwise, the core point set Ω would be Ω−Ck.
- Step 6
- A core point o’ is taken from the current core point queue Ωcur. Then, all Eps-neighborhood subsample sets NEps(o’) can be found through the Eps-neighborhood distance threshold Eps. Let Δ equal to NEps (o’) ∩ Γ. Ck updates to Ck ∪ Δ, Γ updates to Γ−Δ, and Ωcur updates to Ωcur ∪ (Δ ∩ Ω)−o’. Then, go to step 5.
2.4. Reference Water–Land Interface
2.5. Flowchart of the Proposed Water–Land Discrimination Procedure
3. Results
3.1. Optimal Feature Selection
3.1.1. Single Waveform Feature
3.1.2. Combination of Waveform Features
3.2. Dual-Clustering Method
3.2.1. K-Means Clustering
3.2.2. DBSCAN Clustering
3.2.3. Accuracy Analysis
4. Discussion
4.1. Influencing Factors for Water–Land Discrimination
4.2. Detection of Aquaculture Rafts Using Waveform Width
4.3. Applications
5. Conclusions
- (1)
- The performance of water–land discrimination using single waveform features and combinations of waveform features are evaluated and compared through experimental analysis. The overall accuracy rates of water–land discrimination using amplitude; FWHM; area; width; a combination of amplitude and FWHM; a combination of amplitude and area; a combination of amplitude and width; a combination of amplitude, FWHM, area, and width; and a combination of amplitude, FWHM, area, and width after PCA reduction based on K-means clustering are 99.482%, 86.313%, 99.352%, 95.105%, 99.482%, 99.353%, 99.476%, 99.353%, and 99.353%, respectively. The results show that waveform amplitude is the optimal feature for water–land discrimination using IR laser waveforms.
- (2)
- Dual-clustering has two levels. The first level removes outliers in the waveform amplitudes. The second level removes geographic outliers to correct the mislabeled waveforms derived by the first level. The proposed dual-clustering method can correct mislabeled water or land waveforms and reduce the number of mislabeled waveforms by 48% with respect to the number obtained through traditional K-means clustering. Water–land discrimination using IR waveform amplitude and the proposed dual-clustering method can reach an overall accuracy of 99.730%. The proposed dual-clustering method can correct and reduce mislabeled waveforms with respect to the traditional feature clustering methods.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Parameter | Value |
---|---|
Flight altitude | 400 m (nominal) |
Flight velocity | 140 kts (nominal) |
Laser pulse repetition rate | 10 kHz |
Scanner frequency | 27 Hz |
Laser wavelength | IR: 1064 nm; green: 532 nm |
Maximum measurable depth | 4.2/Kd (bottom power reflectivity > 15%) |
Minimum measurable depth | 0.15 m |
Depth accuracy | (0.32 + (0.013 d)2)½ m, 2σ |
Horizontal accuracy | (3.5 + 0.05 d) m, 2σ |
Scan angle | 20° (circular scan) |
Cross-track swath width | 294 m (nominal) |
Water (Reference Label) | Land (Reference Label) | |
---|---|---|
Water (discriminated label) | TN | FN |
Land (discriminated label) | FP | TP |
Feature | Confusion Matrix | Overall Accuracy | ||
---|---|---|---|---|
Reference Water | Reference Land | |||
Amplitude | Discriminated water | 839,274 | 1826 | 99.48% |
Discriminated land | 3416 | 166,616 | ||
FWHM | Discriminated water | 840,597 | 136,299 | 86.31% |
Discriminated land | 2093 | 32,143 | ||
Area | Discriminated water | 841,276 | 5139 | 99.35% |
Discriminated land | 1414 | 163,303 | ||
Width | Discriminated water | 802,969 | 9769 | 95.11% |
Discriminated land | 39,721 | 158,673 |
Feature | Confusion Matrix | Overall Accuracy | ||
---|---|---|---|---|
Reference Water | Reference Land | |||
Amplitude and FWHM | Discriminated water | 839,274 | 1826 | 99.48% |
Discriminated land | 3416 | 166,616 | ||
Amplitude and area | Discriminated water | 841,275 | 5130 | 99.35% |
Discriminated land | 1415 | 163,312 | ||
Amplitude and width | Discriminated water | 839,219 | 1826 | 99.48% |
Discriminated land | 3471 | 166,616 | ||
Combination of amplitude, FWHM, area, and width | Discriminated water | 841,275 | 5130 | 99.35% |
Discriminated land | 1415 | 163,312 | ||
Combination of amplitude, FWHM, area, and width after PCA reduction | Discriminated water | 841,276 | 5129 | 99.35% |
Discriminated land | 1414 | 163,313 |
Method | Confusion Matrix | Overall Accuracy | ||
---|---|---|---|---|
Reference Water | Reference Land | |||
K-means | Discriminated water | 839,274 | 1826 | 99.48% |
Discriminated land | 3416 | 166,616 | ||
Dual clustering | Discriminated water | 841,615 | 1659 | 99.73% |
Discriminated land | 1075 | 166,783 |
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Liang, G.; Zhao, X.; Zhao, J.; Zhou, F. Feature Selection and Mislabeled Waveform Correction for Water–Land Discrimination Using Airborne Infrared Laser. Remote Sens. 2021, 13, 3628. https://doi.org/10.3390/rs13183628
Liang G, Zhao X, Zhao J, Zhou F. Feature Selection and Mislabeled Waveform Correction for Water–Land Discrimination Using Airborne Infrared Laser. Remote Sensing. 2021; 13(18):3628. https://doi.org/10.3390/rs13183628
Chicago/Turabian StyleLiang, Gang, Xinglei Zhao, Jianhu Zhao, and Fengnian Zhou. 2021. "Feature Selection and Mislabeled Waveform Correction for Water–Land Discrimination Using Airborne Infrared Laser" Remote Sensing 13, no. 18: 3628. https://doi.org/10.3390/rs13183628
APA StyleLiang, G., Zhao, X., Zhao, J., & Zhou, F. (2021). Feature Selection and Mislabeled Waveform Correction for Water–Land Discrimination Using Airborne Infrared Laser. Remote Sensing, 13(18), 3628. https://doi.org/10.3390/rs13183628