An Integrated Change Detection Method Based on Spectral Unmixing and the CNN for Hyperspectral Imagery
"> Figure 1
<p>The architecture of the HSI-CD method based on MSUJMC.</p> "> Figure 2
<p>The flow chart of the MSU method.</p> "> Figure 3
<p>The flow chart of the information fusion.</p> "> Figure 4
<p>Diagram of the network frame.</p> "> Figure 5
<p>Simulated multitemporal image dataset: False-colour composite image (bands: R: 40, G: 30, B: 20): (<b>a</b>) Salinas original hyperspectral image; (<b>b</b>) simulated image with Gaussian white noise (mean = 0, variance = 0.001); and (<b>c</b>) reference image.</p> "> Figure 6
<p>Real multitemporal image dataset-1 and dataset-2: False-colour composites (bands: R: 23, G: 13, B: 6) images: Farmland images acquired on (<b>a</b>) 3 May 2006, and (<b>b</b>) 23 April 2007, and (<b>c</b>) reference image. False-colour composites (bands: R: 23, G: 13, B: 6) images: Agricultural irrigated images acquired on (<b>d</b>) 1 May 2004 and (<b>e</b>) 8 May 2007, and (<b>f</b>) reference image.</p> "> Figure 7
<p>The experimental results of Simulated multitemporal image dataset: (<b>a</b>) reference image; (<b>b</b>) K-means; (<b>c</b>) RF; (<b>d</b>) SVM; (<b>e</b>) SU; (<b>f</b>) MSU; (<b>g</b>) MSUC; (<b>h</b>) SUJMC; and (<b>i</b>) MSUJMC.</p> "> Figure 8
<p>Spectral curves at different noise levels: (<b>a</b>) without noise; (<b>b</b>) with Gaussian white noise (variance = 0.001); (<b>c</b>) with Gaussian white noise (variance = 0.003); (<b>d</b>) with Gaussian white noise (variance = 0.005). Experimental results of the simulated dataset under different levels of noise: (<b>e</b>) the OA of each method for the simulation dataset under different levels of noise; (<b>f</b>) the KAPPA of each method for the simulation dataset under different levels of noise.</p> "> Figure 9
<p>The experimental results of real multitemporal image dataset-1: (<b>a</b>) reference image; (<b>b</b>) K-means; (<b>c</b>) RF; (<b>d</b>) SVM; (<b>e</b>) SU; (<b>f</b>) MSU; (<b>g</b>) MSUC; (<b>h</b>) SUJMC; (<b>i</b>) MSUJMC.</p> "> Figure 9 Cont.
<p>The experimental results of real multitemporal image dataset-1: (<b>a</b>) reference image; (<b>b</b>) K-means; (<b>c</b>) RF; (<b>d</b>) SVM; (<b>e</b>) SU; (<b>f</b>) MSU; (<b>g</b>) MSUC; (<b>h</b>) SUJMC; (<b>i</b>) MSUJMC.</p> "> Figure 10
<p>The experimental results of real multitemporal image dataset-2: (<b>a</b>) reference image; (<b>b</b>) K-means; (<b>c</b>) RF; (<b>d</b>) SVM; (<b>e</b>) SU; (<b>f</b>) MSU; (<b>g</b>) MSUC; (<b>h</b>) SUJMC; (<b>i</b>) MSUJMC.</p> ">
Abstract
:1. Introduction
2. Related Works
2.1. Spectral Unmixing
2.2. Machine Learning
2.3. Deep Learning
3. Methodology
3.1. MSU Method for Acquiring Abundance Images
3.2. JM Algorithm for Information Fusion
3.3. CNN for Detecting Multiple Changes
4. Experiment
4.1. Dataset Description
4.2. Evaluation Measures
4.3. Results and Discussion
4.3.1. Simulation Dataset
4.3.2. Real HSI Dataset-1
4.3.3. Real HSI Dataset-2
4.4. Computational Cost Analysis
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
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Layers | Type | Channels | Kernel Size |
---|---|---|---|
Conv1 | Convolution + BN Activation (relu) | 16 | 3 × 3 |
Pool1 | MaxPooling | - | 2 × 2 |
Conv2 | Convolution + BN Activation (relu) | 32 | 3 × 3 |
Pool2 | MaxPooling | - | 2 × 2 |
Conv3 | Convolution + BN Activation (relu) | 64 | 3 × 3 |
Pool3 | MaxPooling | - | 2 × 2 |
Conv4 | Convolution + BN Activation (relu) | 128 | 3 × 3 |
Pool4 | MaxPooling | - | 2 × 2 |
FC1 | Fully Connected + BN Activation (relu) | 128 | - |
FC2 | Fully Connected + BN Activation (softmax) | nchange + 1 | - |
K-means | RF | SVM | SU | MSU | MSUC | SUJMC | MSUJMC | ||
---|---|---|---|---|---|---|---|---|---|
OA (%) | 96.07 | 97.06 | 98.41 | 97.81 | 99.01 | 99.37 | 99.80 | 99.95 | |
Kappa | 0.74 | 0.80 | 0.88 | 0.84 | 0.92 | 0.95 | 0.98 | 0.996 | |
unchanged | Precision | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 | 1.00 |
Recall | 0.96 | 0.97 | 0.98 | 0.98 | 0.99 | 0.99 | 1.00 | 1.00 | |
change 1 | Precision | 0.80 | 1.00 | 1.00 | 0.86 | 1.00 | 0.95 | 1.00 | 1.00 |
Recall | 0.99 | 1.00 | 1.00 | 0.99 | 1.00 | 1.00 | 1.00 | 1.00 | |
change 2 | Precision | 0.48 | 0.80 | 0.91 | 0.68 | 0.80 | 1.00 | 0.99 | 1.00 |
Recall | 0.91 | 1.00 | 1.00 | 0.98 | 0.95 | 1.00 | 1.00 | 1.00 | |
change 3 | Precision | 0.54 | 0.80 | 0.88 | 0.66 | 0.72 | 0.95 | 1.00 | 1.00 |
Recall | 0.96 | 1.00 | 1.00 | 0.82 | 0.97 | 0.97 | 1.00 | 1.00 | |
change 4 | Precision | 0.75 | 1.00 | 1.00 | 0.82 | 1.00 | 0.95 | 1.00 | 1.00 |
Recall | 0.99 | 1.00 | 1.00 | 1.00 | 0.99 | 1.00 | 1.00 | 1.00 | |
change 5 | Precision | 0.21 | 0.10 | 0.17 | 0.23 | 0.41 | 0.76 | 1.00 | 1.00 |
Recall | 0.72 | 1.00 | 1.00 | 0.40 | 0.84 | 0.95 | 0.16 | 0.84 | |
change 6 | Precision | 0.09 | 0.07 | 0.13 | 0.32 | 0.36 | 0.72 | 1.00 | 1.00 |
Recall | 0.44 | 1.00 | 1.00 | 0.72 | 1.00 | 0.75 | 0.12 | 0.76 |
K-means | RF | SVM | SU | MSU | MSUC | SUJMC | MSUJMC | ||
---|---|---|---|---|---|---|---|---|---|
OA (%) | 89.33 | 85.99 | 88.84 | 88.31 | 94.60 | 96.73 | 97.36 | 98.63 | |
Kappa | 0.74 | 0.73 | 0.78 | 0.76 | 0.89 | 0.93 | 0.95 | 0.97 | |
unchanged | Precision | 0.89 | 1.00 | 1.00 | 0.97 | 0.99 | 0.98 | 0.98 | 0.99 |
Recall | 0.98 | 0.86 | 0.87 | 0.86 | 0.93 | 0.97 | 0.98 | 0.99 | |
change 1 | Precision | 0.86 | 0.52 | 0.60 | 0.79 | 0.77 | 0.97 | 0.95 | 0.99 |
Recall | 0.97 | 0.94 | 0.94 | 0.96 | 0.98 | 0.91 | 0.91 | 0.95 | |
change 2 | Precision | 0.99 | 0.78 | 0.79 | 0.70 | 0.91 | 0.92 | 0.97 | 0.98 |
Recall | 0.53 | 0.83 | 0.92 | 0.94 | 0.99 | 0.99 | 0.97 | 0.98 |
K-means | RF | SVM | SU | MSU | MSUC | SUJMC | MSUJMC | ||
---|---|---|---|---|---|---|---|---|---|
OA (%) | 94.16 | 94.94 | 96.89 | 95.26 | 96.69 | 97.47 | 98.45 | 98.89 | |
Kappa | 0.75 | 0.80 | 0.86 | 0.80 | 0.86 | 0.88 | 0.93 | 0.95 | |
unchanged | Precision | 0.99 | 0.99 | 0.99 | 0.99 | 0.99 | 0.99 | 0.99 | 1.00 |
Recall | 0.99 | 0.97 | 0.99 | 0.99 | 0.99 | 0.99 | 0.99 | 0.99 | |
change 1 | Precision | 0.90 | 0.96 | 0.89 | 0.98 | 0.95 | 0.90 | 0.96 | 0.97 |
Recall | 0.65 | 0.92 | 0.92 | 0.71 | 0.86 | 0.93 | 0.96 | 0.96 | |
change 2 | Precision | 0.31 | 0.39 | 0.84 | 0.48 | 0.66 | 0.92 | 0.93 | 0.93 |
Recall | 0.66 | 0.89 | 0.88 | 0.92 | 0.86 | 0.60 | 0.95 | 0.95 | |
change 3 | Precision | 0.00 | 0.98 | 1.00 | 0.16 | 0.18 | 0.86 | 0.70 | 0.84 |
Recall | 0.00 | 0.59 | 0.58 | 0.82 | 0.81 | 0.85 | 0.90 | 0.85 | |
change 4 | Precision | 0.54 | 0.53 | 0.60 | 0.58 | 0.72 | 0.91 | 0.89 | 0.90 |
Recall | 0.97 | 0.98 | 0.98 | 0.71 | 0.70 | 0.77 | 0.89 | 0.95 | |
change 5 | Precision | 0.39 | 0.95 | 0.96 | 0.65 | 0.69 | 0.76 | 0.80 | 0.90 |
Recall | 0.23 | 0.36 | 0.36 | 0.51 | 0.73 | 0.90 | 0.86 | 0.91 |
Time (s) | K-means | RF | SVM | SU | MSU | MSUC | SUJMC | MSUJMC | |
---|---|---|---|---|---|---|---|---|---|
simulation dataset | v = 0.001 | 7.46 | 4.37 | 5.68 | 8.06 | 12.75 | 20.95 | 37.12 | 41.98 |
v = 0.003 | 8.47 | 5.16 | 6.23 | 8.84 | 13.64 | 22.15 | 38.96 | 43.78 | |
v = 0.005 | 9.85 | 6.53 | 7.42 | 9.86 | 14.83 | 23.95 | 40.31 | 45.26 | |
real dataset-1 | 9.83 | 6.94 | 7.85 | 10.21 | 15.36 | 24.66 | 41.08 | 46.24 | |
real dataset-2 | 15.23 | 11.02 | 12.82 | 18.32 | 26.38 | 46.76 | 67.25 | 74.86 |
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Li, H.; Wu, K.; Xu, Y. An Integrated Change Detection Method Based on Spectral Unmixing and the CNN for Hyperspectral Imagery. Remote Sens. 2022, 14, 2523. https://doi.org/10.3390/rs14112523
Li H, Wu K, Xu Y. An Integrated Change Detection Method Based on Spectral Unmixing and the CNN for Hyperspectral Imagery. Remote Sensing. 2022; 14(11):2523. https://doi.org/10.3390/rs14112523
Chicago/Turabian StyleLi, Haishan, Ke Wu, and Ying Xu. 2022. "An Integrated Change Detection Method Based on Spectral Unmixing and the CNN for Hyperspectral Imagery" Remote Sensing 14, no. 11: 2523. https://doi.org/10.3390/rs14112523
APA StyleLi, H., Wu, K., & Xu, Y. (2022). An Integrated Change Detection Method Based on Spectral Unmixing and the CNN for Hyperspectral Imagery. Remote Sensing, 14(11), 2523. https://doi.org/10.3390/rs14112523