Study on Urban Heat Island Intensity Level Identification Based on an Improved Restricted Boltzmann Machine
Abstract
:1. Introduction
2. Research Area and Data
2.1. Research Area
2.2. Data
3. Research Methods
3.1. Technical Route
3.2. Land Surface Temperature Inversion
3.3. K-RBM Clustering
- (1)
- Initialize K-RBMs, and initialize the rate of error change (EC = 15) of the sum of the reconstruction errors of the eigenvectors of all the pixels in the sample. Initialize all elements in the class chart Y to 0, Y = {ym,n|1 ≤ m ≤ M, 1 ≤ n ≤ N}; ym,n indicates the class of any pixel (m, n) in the sample. The number of the subscript k in the 3 × 3 neighborhood of (m, n) in the initialized graph Y is N3m,n(k) = 0.
- (2)
- Input the feature vectors of all the pixels (A) into the K-RBMs.The feature vectors of all the pixels (m, n) are input into the K-RBMs, and the reconstruction error () of the K-RBMs for Fm,n is calculated. Based on the principle of minimum error, the extracted feature vector (Fm,n) is classified into K groups (Sk).
- (3)
- Correct the error of the preliminary classification results. In this step, the mean value (Pk) of the eigenvector (Fm,n ) in Sk is calculated; then, the distance () between Fm,n and the cluster center is calculated.
- (4)
- Train the K-RBMs by the feature vector (Fm,n) in Gk. If the algorithm converges, the training is terminated, otherwise perform step 2.
3.4. Improved Restricted Boltzmann Machine
- (1)
- is normalized for each row of matrix (HJ×L), using to denote the value of row j and column 1 in the matrix after row normalization. The formula is expressed as follows:
- (2)
- l2 is normalized for the columns of the normalized matrix, using to denote the value of row j and column 1 in the matrix after row normalization. The formula is expressed as follows:
3.5. Construction of the Urban Heat Island Intensity Level Identification Algorithm Based on the Improved RBM
- (1)
- A small amount of sample S1 is extracted from the image that has been subjected to the land surface temperature inversion and it is manually marked. The number of artificial markers in a small sample of S1 of each type of clustering center is calculated. At the same time, the unmarked samples (S2) are clustered based on the K-means algorithm.
- (2)
- The Euclidean distance between the two types of cluster centers is compared with that of S1 and S2. Based on the minimum error criterion, the set of assignments with higher confidence, achieved via the K-RBM algorithm, is selected and combined with S1 samples to form the most valuable samples.
- (3)
- For pre-training to improve the RBM, the unsupervised learning of the RBM is improved by using the full sample (S). The next step is carried out when the error between the modified training samples of the RBM and the reconstructed data is quite small. At the same time, the relevant parameters obtained from the pre-training improvement RBM are used in the next step to improve the RBM; otherwise, continue to perform pre-training operations.
- (4)
- The most valuable sample obtained in step 2 is entered into the improved RBM-softmax urban heat island intensity level identification model, training the model parameters by the steepest descent method until convergence.
- (5)
- The data are input into the trained RBM-softmax urban heat intensity level identifier; then, the identification result is obtained, and the classification accuracy is calculated.
4. Results Analysis
4.1. Spatial Distribution of Surface Urban Heat Island
4.2. Identify the Results
4.3. Comparison of Identification Results
5. Discussion
6. Conclusions
- (1)
- The largest heat island area is that of the heat island level in Wuhan. Regarding the hottest areas that can be seen, most of the “heat” comes from the industrial building area; secondary heat sources are mainly distributed in dense, commercial areas and residential areas. Heavy industry production companies, steel mills and other enterprises in production projects produce a lot of heat. From the heat island spatial distribution map, the distribution of water and green space can also be clearly seen; the impact of the distribution of water and green space on urban surface light temperature is very obvious. Around the several large lakes and urban parks of the main city of Wuhan, a green island center of Wuhan is formed.
- (2)
- For the RBM identification algorithm, the total accuracy was 93.31%, the kappa coefficient was 0.8861, and the test time was 0.72 s. The algorithm had a good classification effect, and the computational efficiency was higher than that of the K-means clustering algorithm. Compared with K-means and the genetic K-means, the improved RBM identification algorithm is more obvious in terms of the division of the temperature level within the city, and the boundary of the category is obvious. The clustering effect of the algorithm can determine the spatial distribution of the heat island effect within the city, and this method is more practical than others.
- (3)
- Based on the improved RBM model, this paper analyzed the urban heat island and compared it to those obtained with other methods. The focus of the study the spectral characteristics of surface temperature after inversion, which includes texture features for further feature extraction. This method may be enhanced by adding other methods to complete the feature extraction, such as an automatic encoder method. In the process of RBM learning, it is possible to obtain a positive training sample image and a negative training sample image, through the use of a sliding window, to obtain a better distribution rule of the data.
Acknowledgments
Author Contributions
Conflicts of Interest
Nomenclature
Lλ | Radiation intensity received by the TM remote sensor |
DN | Digital number |
ε | Specific emissivity |
T | Radiation light temperature |
A0, A1, A2 | Coefficient of Formula (5) |
τ | Atmospheric transmittance |
ω | Water vapor |
ai, bi | Regression coefficients of TIRS |
E0, E1, E2, Ci, Di | Procedure parameter |
k, K | Classification sequence number, number of classifications, respectively |
m, n | Line sequence number of pixels, column sequence number of pixels, respectively |
M, N | Maximum number of lines and columns in pixels |
i, I | Sequence number of X(l), total number of X(l), respectively |
l, L | Training sample sequence number, total number of training samples, respectively |
j, J | Sequence number of hidden units, total number of hidden units, respectively |
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Acquisition Time | Line | Number | Image Type | Thermal Infrared Band Spatial Resolution |
---|---|---|---|---|
23 July 2016 | 123 | 39 | Band 1 Coastal | 30 m |
Band 2 Blue | 30 m | |||
Band 3 Green | 30 m | |||
Band 4 Red | 30 m | |||
Band 5 NIR (Near infrared) | 30 m | |||
Band 6 SWIR 1 (Short-wave infrared) | 30 m | |||
Band 7 SWIR 2 | 30 m | |||
Band 8 Pan | 15 m | |||
Band 9 Cirrus | 30 m | |||
Band 10 TIRS 1 | 100 m | |||
Band 11 TIRS 2 | 100 m |
Temperature Range/°C | a10 | b10 | a11 | b11 | ||
---|---|---|---|---|---|---|
0–30 | −59.139 | 0.421 | 0.9991 | −63.392 | 0.457 | 0.9991 |
0–40 | −60.919 | 0.428 | 0.9985 | −65.224 | 0.463 | 0.9985 |
10–40 | −62.806 | 0.434 | 0.9992 | −67.173 | 0.47 | 0.9992 |
10–50 | −64.608 | 0.44 | 0.9986 | −69.022 | 0.476 | 0.9986 |
Atmospheric Mode | Atmospheric Transmittance Estimation Equation | r2 |
---|---|---|
The United States in 1976 standard atmosphere | | 0.9982 0.9947 |
Mid-latitude summer | | 0.9986 0.9996 |
Heat Island Level | Area | Area Ratio |
---|---|---|
Green island | 21,341 | 16.55% |
Sub-green island | 37,753 | 29.28% |
Heat island | 50,525 | 39.19% |
Strong heat island | 19,304 | 14.97% |
Identification Model | K-Means Clustering | Genetic K-Means Clustering | Improved RBM Identifier |
---|---|---|---|
Total Accuracy | 73.39% | 91.22% | 93.31% |
Kappa | 0.6725 | 0.8735 | 0.8861 |
Testing time | 2.87 | 0.91 | 0.72 |
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Zhang, Y.; Jiang, P.; Zhang, H.; Cheng, P. Study on Urban Heat Island Intensity Level Identification Based on an Improved Restricted Boltzmann Machine. Int. J. Environ. Res. Public Health 2018, 15, 186. https://doi.org/10.3390/ijerph15020186
Zhang Y, Jiang P, Zhang H, Cheng P. Study on Urban Heat Island Intensity Level Identification Based on an Improved Restricted Boltzmann Machine. International Journal of Environmental Research and Public Health. 2018; 15(2):186. https://doi.org/10.3390/ijerph15020186
Chicago/Turabian StyleZhang, Yang, Ping Jiang, Hongyan Zhang, and Peng Cheng. 2018. "Study on Urban Heat Island Intensity Level Identification Based on an Improved Restricted Boltzmann Machine" International Journal of Environmental Research and Public Health 15, no. 2: 186. https://doi.org/10.3390/ijerph15020186