|
|
|
|
|
|
A Rapid Method for Stripe Chromatic Aberration Correction in
Landsat Images |
YAN Xing-guang, LI Jing*, YAN Xiao-xiao, MA Tian-yue, SU Yi-ting, SHAO Jia-hao, ZHANG Rui |
College of Geoscience and Surveying Engineering, China University of Mining and Technology-Beijing, Beijing 100083, China
|
|
|
Abstract Landsat satellite images have become the most widely used data source in large-scale ecological monitoring studies worldwide. In remote sensing application studies of large and medium scale areas, due to seasonal, lighting and climatic conditions and different satellite re-entry cycles and sensors, patchy effects and chromatic unevenness may exist after stitching the mosaic of multi-scene remote sensing images. With the rapid development of remote sensing cloud computing technology, exploring a fast and efficient method to repair Landsat chromatic stripes based on cloud platform is important. In this paper, we propose a histogram image homogenization method based on a random forest algorithm implemented on the Google Earth Engine (GEE) cloud platform, which homogenizes the Landsat Top of Atmosphere (TOA) and Surface Reflectance (SR) of Shanxi Province from 1986 to 2020 (Landsat 5 TM/7 ETM+/8 OLI) normalized vegetation index (NDVI) images after inversion were used as the study data, and MOD13Q1 (250 m resolution), MOD13A1 (500 m resolution) and MOD13A2 (1 km resolution) MODIS datasets were used as the validation data after 2000. The NDVI images of Shanxi Province from 1986 to 2020 before and after image restoration were compared separately, and the results of the study showed that (1) 20 years of the 35-year image analysis had strip color difference problems, and in 1994, for example, the restored Landsat TOA and Landsat SR images compared with those before restoration, the mean NDVI values of the restored areas increased by 32.6% and 29.03% respectively, and the profile analysis showed that the fit increased by 0.162 3 and 0.118 0 respectively; (2) The results of the trend analysis of the 1986—2020 one-dimensional linear regression showed that the fit of the restored images was high and the fluctuation of the year-by-year images was smaller after the long time series analysis. Among them, the slopes of the restored Landsat TOA and SR images decreased by 0.006 2 and 0.006 7, and theR2 improved by 0.024 8 and 0.008 4 respectively; (3) Pearson correlation analysis of Landsat and MODIS images found that the correlation coefficients of the restored Landsat SR and TOA images improved by an average of 0.049 and 0.061 (p<0.05), where the correlation coefficients of restored Landsat SR and TOA images and MOD13Q1, MOD13A1, and MOD13A2 images increased by 0.050, 0.047, 0.049, 0.066, 0.060, and 0.059, respectively; (4) 2000—2020 Landsat and MODIS image time series analysis results show that the overall trend of the restored Landsat images is more similar to MODIS images, and the fit of the restored Landsat TOA and SR images is improved by 0.058 6 and 0.031 9, respectively. The proposed GEE cloud platform-based stochastic The proposed fast image restoration method based on the GEE cloud platform random forest algorithm achieves the accurate evaluation of NDVI inversion results of long time series remote sensing images, and the application of this method can quickly and efficiently solve the chromatic patch and banding effects caused by image mosaic.
|
Received: 2022-05-26
Accepted: 2022-10-04
|
|
Corresponding Authors:
LI Jing
E-mail: lijing@cumtb.edu.cn
|
|
[1] Liu P. Frontiers in Environmental Science, 2015, 3: 45.
[2] Li J, Zipper C E, Donovan P F, et al. Environmental Monitoring and Assessment, 2015, 187(9): 557.
[3] LI Jing, DENG Xiao-juan, YANG Zhen, et al(李 晶, 邓晓娟, 杨 震). Spectroscopy and Spectral Analysis((光谱学与光谱分析), 2019, 39(12): 3788.
[4] LI De-ren, WANG Mi, PAN Jun(李德仁, 王 密, 潘 俊). Geomatics and Information Science of Wuhan University[武汉大学学报(信息科学版)], 2006, 31: 753.
[5] CUI Hao, ZHANG Li, AI Hai-bin, et al(崔 浩, 张 力, 艾海滨, 等). Acta Geodaetica et Cartographica Sinica(测绘学报), 2017, 46(12): 1986.
[6] Lillesand Thomas M, Kiefer Ralph W, Chipman J. Remote Sensing and Image Interpretation 7th ed. American: John Wiley & Sons, Inc, 2015, 289.
[7] LI Shuo, WANG Hui, WANG Li-yong, et al(李 烁, 王 慧, 王利勇). National Remote Sensing Bulletin(遥感学报), 2018, 22(3): 450.
[8] Jiang B, Woodell G A, Jobson D J. Journal of Real-Time Image Processing, 2015, 10(2): 239.
[9] WANG Mi, PAN Jun(王 密, 潘 俊). Remote Sensing for Land and Resources(国土资源遥感), 2006, 18(4): 10.
[10] WANG Wen-tao, WEN De-bao(王文滔, 闻德保). Jiangsu Science & Technology Information(江苏科技信息), 2017, (6): 51.
[11] Liu J, Wang X, Chen M, et al. Remote Sensing, 2014, 6(2): 1102.
[12] Richter R. International Journal of Remote Sensing, 1990, 11(1): 159.
[13] Gorelick N, Hancher M, Dixon M, et al. Remote Sensing of Environment, 2017, 202: 18.
[14] Kumar Lalit, Mutanga Onisimo. Remote Sensing, 2018, 10(10): 1509.
[15] LI Jing, YAN Xing-guang, YAN Xiao-xiao, et al(李 晶, 闫星光, 闫萧萧, 等). China Coal Society(煤炭学报), 2021, 46(5): 1439.
[16] Helmer E H, Ruefenacht B. Photogrammetric Engineering & Remote Sensing, 2005, 71(9): 1079.
[17] Chen Soong-Der, Ramli A R. IEEE Transactions on Consumer Electronics, 2003, 49(4): 1310.
[18] Breiman L. Machine Learning, 2001, 45:5.
|
[1] |
YANG Qun1, 2, LING Qi-han1, WEI Yong1, NING Qiang1, 2, KONG Fa-ming1, ZHOU Yi-fan1, 2, ZHANG Hai-lin1, WANG Jie1, 2*. Non-Destructive Monitoring Model of Functional Nitrogen Content in
Citrus Leaves Based on Visible-Near Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3396-3403. |
[2] |
DONG Jian-jiang1, TIAN Ye1, ZHANG Jian-xing2, LUAN Zhen-dong2*, DU Zeng-feng2*. Research on the Classification Method of Benthic Fauna Based on
Hyperspectral Data and Random Forest Algorithm[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3015-3022. |
[3] |
HUANG Hua1, LIU Ya2, KUERBANGULI·Dulikun1, ZENG Fan-lin1, MAYIRAN·Maimaiti1, AWAGULI·Maimaiti1, MAIDINUERHAN·Aizezi1, GUO Jun-xian3*. Ensemble Learning Model Incorporating Fractional Differential and
PIMP-RF Algorithm to Predict Soluble Solids Content of Apples
During Maturing Period[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3059-3066. |
[4] |
LIU Fei1, TAN Jia-jin1*, XIE Gu-ai2, SU Jun3, YE Jian-ren1. Early Diagnosis of Pine Wilt Disease Based on Hyperspectral Data and Needle Resistivity[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3280-3285. |
[5] |
LI Quan-lun1, CHEN Zheng-guang1*, JIAO Feng2. Prediction of Oil Content in Oil Shale by Near-Infrared Spectroscopy Based on Stacking Ensemble Learning[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(04): 1030-1036. |
[6] |
LIU Xin-yu1, SHAO Wen-wu2*, ZHOU Shi-rui3. Spectral Pattern Recognition of Cardiac Tissue in Electric Shock Death and Post-Mortem Electric Shock[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(04): 1126-1133. |
[7] |
YANG Cheng-en1, SU Ling2, FENG Wei-zhi1, ZHOU Jian-yu1, WU Hai-wei1*, YUAN Yue-ming1, WANG Qi2*. Identification of Pleurotus Ostreatus From Different Producing Areas Based on Mid-Infrared Spectroscopy and Machine Learning[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(02): 577-582. |
[8] |
ZHANG Hai-yang, ZHANG Yao*, TIAN Ze-zhong, WU Jiang-mei, LI Min-zan, LIU Kai-di. Extraction of Planting Structure of Winter Wheat Using GBDT and Google Earth Engine[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(02): 597-607. |
[9] |
YAN Wen-hao1, YANG Xiao-ying1, GENG Xin1, WANG Le-shan1, LÜ Liang1, TIAN Ye1*, LI Ying1, LIN Hong2. Rapid Identification of Fish Products Using Handheld Laser Induced Breakdown Spectroscopy Combined With Random Forest[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(12): 3714-3718. |
[10] |
GONG Sheng1, ZHU Ya-ning2, ZENG Chen-juan3, MA Xiu-ying3, PENG Cheng1, GUO Li1*. Near-Infrared Spectroscopy Combined With Random Forest Algorithm: A Fast and Effective Strategy for Origin Traceability of Fuzi[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(12): 3823-3829. |
[11] |
GAO Feng1, 2, 3, JIANG Qun-ou1, 2, 3*, XIN Zhi-ming4, XIAO Hui-jie1, 2, LÜ Ke-xin1, QIAO Zhi1. Extraction Method of Oasis Shelterbelt Systems Based on Remote-Sensing Images ——A Case Study of Dengkou County[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(12): 3896-3905. |
[12] |
GAO Rong-hua1, 2, FENG Lu1, 2*, ZHANG Yue3, YUAN Ji-dong3, WU Hua-rui1, 2, GU Jing-qiu1, 2. Early Detection of Tomato Gray Mold Disease With Multi-Dimensional Random Forest Based on Hyperspectral Image[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(10): 3226-3234. |
[13] |
LI Zi-zhao, BI Shou-dong, CUI Yu-huan, HAO Shuang*. Comparison of Forest Stock Volume Inversion Methods Coupled With Multiple Features——A Case Study of Forest in Yarlung Zangbo River Basin[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(10): 3263-3268. |
[14] |
YANG Cheng-en1, WU Hai-wei1*, YANG Yu2, SU Ling2, YUAN Yue-ming1, LIU Hao1, ZHANG Ai-wu3, SONG Zi-yang3. A Model for the Identification of Counterfeited and Adulterated Sika Deer Antler Cap Powder Based on Mid-Infrared Spectroscopy and Support
Vector Machines[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(08): 2359-2365. |
[15] |
WANG Cai-ling1, WANG Bo2, JI Tong3, XU Jun4, JU Feng5, WANG Hong-wei6*. Simulated Estimation of Nitrite Content in Water Based on
Transmission Spectrum[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(07): 2181-2186. |
|
|
|
|