Scientific- Research Quarterly of Geographical Data (SEPEHR)

Current Issue

By Issue

By Author

By Subject

Author Index

Keyword Index

About Journal

Aims and Scope

Editorial Board

Publication Ethics

Indexing and Abstracting

Related Links

FAQ

Peer Review Process

News

Journal Forms

Terms and conditions of submitting an article

Paper Preparation Guide

Automatic extraction of Pseudo-Invariant Features (PIFs) for digital change detection

Document Type : Research Paper

Author

Associate Professor of college of environment, natural resource engineering, Combat Desertification

Abstract

Extended Abstract
Introduction
The process of identifyingthe differences in the status of an object or phenomenon by observing it at different times is called the change detection. In remote sensing change detection, the quantity of a phenomenon is examined from multi-temporal images, and is usually done with the help of multispectral sensors. In remote sensing change detection studies, the type and manner of performing atmospheric correction is one of the most important questions of researchers. In most cases, due to lack of sufficient information or experience and knowledge, absolute radiometric correction is not possible and researchers need to use relative radiometric correction and image-based methods. One of the best image-basedmethods is the radiometric normalization using pseudo-invariant features (PIFs). However, the proper way to select these homogeneous regions remains an important challenge.Therefore, in this research, a very simple method is proposed based on the definition of pseudo-invariant features that automatically identifies these areas and uses a regression process for automatic normalization.
 Materials and Methods
The proposed method in the research is based on the radiometric normalization using pseudo-invariant features. Therefore, it was necessary to identify these areas at first, however, the aim was the automatic extraction of PIFs. According to the definition of pseudo-invariant features, a few basic conditions are needed to define a PIF, therefore, here we have tried to simplify these conditions in order to fall into an automated process:
1- Removing water bodies: The study area has a major part of the Persian Gulf coast and water body, which is affected by the tidal wave and under flood conditions; it is affected by the suspended particles of the rivers. Hence, the first step was to remove the water bodies from the images. To mask water from the images, one of the conditions was the pixel value in the NIR band should be less thanthe pixel value in the blue or green band; and another condition was the pixel value in the NIR band should be less the average minus 1 standard deviation of the entire image.
2- Removing the areas with vegetation: Generally, in regions with vegetation, the reflectance of the NIR band is higher than RED, therefore, a simple criterion for masking the vegetation is the use of this condition. However, given that the images used in this research are raw and unprocessed, a statistically average was used in this condition. First, the water mask was applied to the images and then, the average of difference of the NIR band and the RED band in the remaining area was obtained. Finally, those areas were selected as vegetation in the whole image,in whichthe difference between these two bands was higher than the calculated average.
3- Flatness criterion: The flatness of the area is the simplest criterion for identifying the pseudo-invariant features (PIFs) and is accessible by a digital elevation model with only a slope threshold however, due to the flatness of the study area, this criterion was ignored in this study.
4- Identifying areas with little or no change over time: In this study, in order to evaluate the effect of radiometric correction in the remote sensing change detection, image algebra change detectionmethodwas used. In this method, spectral image enhancement is done by the use of commonly used spectral vegetation indices. Among the spectral vegetation indices based on the unsupervised classification function, and the measures of the dispersion about the mean of a distribution such as the coefficient of variation, the NDVI index showed a better performance. Accordingly, the NDVI index, which proved to be effective in similar studies, was used further in the analysis. In this index, the NIR band and RED bands are used. Therefore, to identify the unchanged areas, unchanged regions in the NIR and RED bands used in this spectral index were identified and combined. For this purpose, water and vegetation masks were first applied to the multispectral image. Then, the OLI image was stretched to 8 bit to match the ETM + image. In the next step, the difference between the two NIR bands for these two sensors was obtained and the mean value and the standard deviation were calculated. Finally, in order to have the least error, an area was taken into consideration as unchanged area, in which the following relation was present:. The same analysis was done on the red band (). These two criteria were combined together to obtain the unchanged areas by the AND Boolean logic method.
Each one of this four conditions is easy to manually apply to the data with the least processing experience, but in this study, these conditions were automatically generated by the Spatial Model Editor of ERDAS IMAGINE.
Radiometric normalization was performed by identifying the pseudo-invariant features (PIFs). In order to validate the accuracy of the proposed method, absolute radiometric correction using ATCOR, FLAASH and ATMOSC methods, and relative radiometric correction using both empirical line calibration method and dark object subtraction method and automatic radiometric correction using QAC and AAIC methods were applied on the data.The output of all atmospheric correction methods and the proposed method was applied in image algebra change detection in the form of a difference and with a threshold of twice the standard deviation from the mean to be checked by 219 points.
 Results and Discussion
The results of validation along with the qualitative studies derived from the histogram comparison proved the proper functioning of the proposed method (Kappa greater than 0.8), and investigating with the help of cross tables indicated that the performance of the proposed method is very similar to that of the empirical line calibration method (More than 76%).
 Conclusions
It should be noted that, some unique features in the present research proposal, including simplicity, automation, negligible systematic error, the possibility of using in a biomarker for degradation warning system, the independence on the type of sensor used, differentiate it from other radiometric correction methods, hence, our suggestion to the researchers interested in the remote sensing change detection of the natural ecosystems is to use the findings of this research.

Keywords

References
1.   Adler-Golden, S., Berk, A., Bernstein, L., Richtsmeier, S., Acharya, P., Matthew, M., Anderson, G., Allred, C., Jeong, L., Chetwynd, J., 1998. FLAASH, a MODTRAN4 atmospheric correction package for hyperspectral data retrievals and simulations, Proc. 7th Ann. JPL Airborne Earth Science Workshop. JPL Publication Pasadena, CA, pp. 9-14.
2.   Aosier, B., Kaneko, M., Takada, M., Saitoh, K., Katoh, K., 2005. Evaluate The Accuracy of The Atmosphere Correction (ATCOR Software Method) of The ASTER Data Using Ground Radiometric Measurement Data, ISPRS, pp. 358-362.
3.   Baldridge, A.M., Hook, S.J., Grove, C.I., Rivera, G., 2009. The ASTER spectral library version 2.0. Remote Sensing of Environment 113, 711-715.
4.   Barati, B., Jahani, A., Zebardast, L., Rayegani, B., 2017. Integration assessment of the protected areas using landscape ecological approach (Case Study: Kolah Ghazy National Park and Wildlife Refuge). Town And Country Planning, 153-168.
5.   Barati GHahfarokhi, S., KHajeddin, S., Rayegani, B., 2009. Investigation of LandUse Changes in Qale Shahrokh Basin Using Remote Sensing (1975-2002). JWSS-Isfahan University of Technology 13, 349-365.
6.   Barati, S., Rayegani, B., Saati, M., Sharifi, A., Nasri, M., 2011. Comparison the accuracies of different spectral indices for estimation of vegetation cover fraction in sparse vegetated areas. The Egyptian Journal of Remote Sensing and Space Science 14, 49-56.
7.   Bernstein, L.S., Jin, X., Gregor, B., Adler-Golden, S.M., 2012. Quick atmospheric correction code: algorithm description and recent upgrades. Optical engineering 51, 111719.
8.   Campbell, J.B., Wynne, R.H., 2011. Introduction to remote sensing. Guilford Press, New York.
9.   Carney, J., Gillespie, T.W., Rosomoff, R., 2014. Assessing forest change in a priority West African mangrove ecosystem: 1986–2010. Geoforum 53, 126-135.
10. Chavez, P.S., 1996. Image-based atmospheric corrections-revisited and improved. Photogrammetric engineering and remote sensing 62, 1025-1035.
11. Chen, X., Vierling, L., Deering, D., 2005. A simple and effective radiometric correction method to improve landscape change detection across sensors and across time. Remote Sensing of Environment 98, 63-79.
12. Congalton, R.G., Green, K., 2008a. Assessing the accuracy of remotely sensed data: principles and practices. CRC press.
13. Congalton, R.G., Green, K., 2008b. Assessing the Accuracy of Remotely Sensed Data: Principles and Practices, Second Edition. CRC Press.
14. Coppin, P., Jonckheere, I., Nackaerts, K., Muys, B., Lambin, E., 2004. Review ArticleDigital change detection methods in ecosystem monitoring: a review. International journal of remote sensing 25, 1565-1596.
15. Coppin, P.R., Bauer, M.E., 1996. Digital change detection in forest ecosystems with remote sensing imagery. Remote sensing reviews 13, 207-234.
16. de Carvalho, O.A., Guimarães, R.F., Silva, N.C., Gillespie, A.R., Gomes, R.A.T., Silva, C.R., de Carvalho, A.P.F., 2013. Radiometric normalization of temporal images combining automatic detection of pseudo-invariant features from the distance and similarity spectral measures, density scatterplot analysis, and robust regression. Remote Sensing 5, 2763-2794.
17. Eastman, J., 2012. IDRISI Selva Tutorial.
18. Eastman, J., 2015a. TerrSet Tutorial. Clark Labs, Clark University: Worcester, MA, United States.
19. Eastman, J.R., 2015b. TerrSet manual. Accessed in TerrSet version 18, 1-390.
20. El-Askary, H.M., Sarkar, S., Kafatos, M., El-Ghazawi, T.A., 2003. A multisensor approach to dust storm monitoring over the Nile Delta. IEEE Transactions on Geoscience and Remote Sensing 41, 2386-2391.
21. Estoque, R.C., Murayama, Y., 2015. Classification and change detection of built-up lands from Landsat-7 ETM+ and Landsat-8 OLI/TIRS imageries: A comparative assessment of various spectral indices. Ecological indicators 56, 205-217.
22. Felde, G., Anderson, G., Cooley, T., Matthew, M., Berk, A., Lee, J., 2003. Analysis of Hyperion data with the FLAASH atmospheric correction algorithm, Geoscience and Remote Sensing Symposium, 2003. IGARSS’03. Proceedings. 2003 IEEE International. IEEE, pp. 90-92.
23. Fichera, C.R., Modica, G., Pollino, M., 2012. Land Cover classification and change-detection analysis using multi-temporal remote sensed imagery and landscape metrics. European Journal of Remote Sensing 45, 1-18.
24. Gao, J., 2009. Digital Analysis of Remotely Sensed Imagery. McGraw-Hill Education.
25. Giri, C., Pengra, B., Zhu, Z., Singh, A., Tieszen, L.L., 2007. Monitoring mangrove forest dynamics of the Sundarbans in Bangladesh and India using multi-temporal satellite data from 1973 to 2000. Estuarine, Coastal and Shelf Science 73, 91-100.
26. Giri, C., Zhu, Z., Tieszen, L., Singh, A., Gillette, S., Kelmelis, J., 2008. Mangrove forest distributions and dynamics (1975–2005) of the tsunami affected region of Asia. Journal of Biogeography 35, 519-528.
27. Giri, C.P., 2016. Remote Sensing of Land Use and Land Cover: Principles and Applications. CRC Press.
28. Gregorich, E.G., Turchenek, L.W., Carter, M.R., Angers, D.A., 2001. Soil and Environmental Science Dictionary. CRC Press.
29. Hermosilla, T., Wulder, M.A., White, J.C., Coops, N.C., Hobart, G.W., 2015. An integrated Landsat time series protocol for change detection and generation of annual gap-free surface reflectance composites. Remote Sensing of Environment 158, 220-234.
30. Huguenin, R., Bouchard, M., Penney, C., Conlon, E., Waddington, G., 2013. Applied Analysis Image Calibrator (AAIC): Automatic Retrieval of Ground Reflectance from Spectral Imagery.
31. Ilsever, M., Unsalan, C., 2012. Two-dimensional change detection methods : remote sensing applications.
32. Jahari, M., Khairunniza-Bejo, S., Shariff, A.R.M., Shafri, H.Z.M., 2011. Change detection studies in Matang mangrove forest area, Perak. Pertanika J. Sci. Technol 19, 307-327.
33. Janzen, D.T., Fredeen, A.L., Wheate, R.D., 2006. Radiometric correction techniques and accuracy assessment for Landsat TM data inremote forested regions. Canadian Journal of Remote Sensing 32, 330-340.
34. Jensen, J.R., 2005. Introductory digital image processing : a remote sensing perspective, 3rd ed. Prentice Hall, Upper Saddle River, N.J.
35. Jensen, J.R., 2016. Introductory digital image processing : a remote sensing perspective. Pearson Education, Inc., Glenview, IL.
36. Jianya, G., Haigang, S., Guorui, M., Qiming, Z., 2008. A review of multi-temporal remote sensing data change detection algorithms. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 37, 757-762.
37. Johansen, K., Arroyo, L.A., Phinn, S., Witte, C., 2010. Comparison of geo-object based and pixel-based change detection of riparian environments using high spatial resolution multi-spectral imagery. Photogrammetric Engineering & Remote Sensing 76, 123-136.
38. Kalacska, M., Sanchez-Azofeifa, G.A., 2008. Hyperspectral Remote Sensing of Tropical and Sub-Tropical Forests. CRC Press.
39. Knight, J.F., Pelletier, K.C., Rampi, L.P., 2017. Change Detection. The International Encyclopedia of Geography.
40. Koch, M., Mather, P., 2013. Computer processing of remotely-sensed images : an introduction. Wiley, Hoboken, N.J.
41. Lee, T.-M., Yeh, H.-C., 2009. Applying remote sensing techniques to monitor shifting wetland vegetation: A case study of Danshui River estuary mangrove communities, Taiwan. Ecological engineering 35, 487-496.
42. Li, Z., Chen, J., Baltsavias, E., 2008a. Advances in Photogrammetry, Remote Sensing and Spatial Information Sciences: 2008 ISPRS Congress Book. CRC Press.
43. Li, Z., Chen, J., Baltsavias, E., 2008b. Advances in photogrammetry, remote sensing and spatial information sciences: 2008 ISPRS congress book. CRC Press.
44. Liu, K., Li, X., Shi, X., Wang, S., 2008. Monitoring mangrove forest changesusing remote sensing and GIS data with decision-tree learning. Wetlands 28, 336-346.
45. Long, X., Li, N., Tie, X., Cao, J., Zhao, S., Huang, R., Zhao, M., Li, G., Feng, T., 2016. Urban dust in the Guanzhong Basin of China, part I: A regional distribution ofdust sources retrieved using satellite data. Science of The Total Environment 541, 1603-1613.
46. Lu, D., Li, G., Moran, E., 2014. Current situation and needs of change detection techniques. International Journal of Image and Data Fusion 5, 13-38.
47. Lu, D., Mausel, P., Brondizio, E., Moran, E., 2004. Change detection techniques. International journal of remote sensing 25, 2365-2401.
48. Lyon, J.G., Yuan, D., Lunetta, R.S., Elvidge, C.D., 1998. A change detection experiment using vegetation indices. Photogrammetricengineering and remote sensing 64, 143-150.
49. Mahiny, A.S., Turner, B.J., 2007. A comparison of four common atmospheric correction methods. Photogrammetric Engineering & Remote Sensing 73, 361-368.
50. Nazeer, M., Nichol, J.E., Yung, Y.-K., 2014. Evaluation ofatmospheric correction models and Landsat surface reflectance product in an urban coastal environment. International journal of remote sensing 35, 6271-6291.
51. Nguyen, H.-H., McAlpine, C., Pullar, D., Johansen, K., Duke, N.C., 2013. The relationship of spatial–temporal changes in fringe mangrove extent and adjacent land-use: Case study of Kien Giang coast, Vietnam. Ocean & coastal management 76, 12-22.
52. Owojori, A., Xie, H., 2005. Landsat image-based LULC changes of San Antonio, Texas using advanced atmospheric correction and object-oriented image analysis approaches, 5th International symposium on remote sensing of urban areas, Tempe, AZ.
53. Paolini, L., Grings, F., Sobrino, J.A., Jiménez Muñoz, J.C., Karszenbaum, H., 2006. Radiometric correction effects in Landsat multi‐date/multi‐sensor change detection studies. International Journal of Remote Sensing 27, 685-704.
54. Pettorelli, N., 2013. The Normalized Difference Vegetation Index. OUP Oxford.
55. Pflug, B., Main-Knorn, M., 2014. Validation of atmospheric correction algorithm ATCOR, Remote Sensing of Clouds and the Atmosphere XIX; and Optics in Atmospheric Propagation and Adaptive Systems XVII. International Society for Optics and Photonics, p. 92420W.
56. Pham, T.D., Yoshino, K., Mangrove Mapping and Change DetectionUsing Multi-temporal Landsat imagery in Hai Phong city, Vietnam.
57. Pham, T.D., Yoshino, K., 2015. Mangrove Mapping and Change Detection Using Multi-temporal Landsat imagery in Hai Phong city, Vietnam, International Symposium on Cartography in Internet and Ubiquitous Environments.
58. Rayegani, B., 2016. Monitoring Hormozgan Mangrove forest changes in the past three decades and prioritizing of degraded ecosystems in order to carry out restoration projects. College of Environment, Department of Environment, p. 280.
59. Rayegani, B., Barati, S., Goshtasb, H., Sarkheil, H., Ramezani, J., 2019. An effective approach to selecting the appropriate pan-sharpening method in digital change detection of natural ecosystems. Ecological Informatics 53, 100984.
60. Rayegani, B., Zehtabian, G., Azarnivand, H., Alavipanah, S.K., Khajeddin, S.J., 2015. LADA method Performance evaluation on soil degradation assessment in the East of Esfahan.
61. Raygani, B., kheirandish, Z., Kermani, F., Miyab, M.M., Torabinia, A., 2017. Identification Of Active Dust Sources Using Remote Sensing Data And Air Flow Simulation (Case Study: Alborz Province). Desert Management 4, 15-26.
62. Richter, R., Schläpfer, D., 2016. ATCOR-2/3 User Guide, Version 9.0.2, March 2016.
63. Rokni, K., Ahmad, A., Selamat, A., Hazini, S., 2014. Water feature extraction and change detection using multitemporal Landsat imagery. Remote Sensing 6, 4173-4189.
64. Sahu, K.C., 2007. Textbook of Remote Sensing and Geographical Information Systems. Atlantic Publishers & Distributors (P) Limited.
65. Son, N.-T., Chen, C.-F., Chang, N.-B., Chen, C.-R., Chang, L.-Y., Thanh, B.-X., 2015. Mangrove mapping and change detection in Ca Mau Peninsula, Vietnam, using Landsat data and object-based image analysis. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 8, 503-510.
66. Song, C., Woodcock, C.E., Seto, K.C., Lenney, M.P., Macomber, S.A., 2001. Classification and change detection using Landsat TM data: when and how to correct atmospheric effects? Remote sensing of Environment 75, 230-244.
67. Thenkabail, P.S., Lyon, J.G., 2016. Hyperspectral Remote Sensing of Vegetation. CRC Press.
68. Vanonckelen, S., Lhermitte, S., Van Rompaey, A., 2015. The effect of atmospheric and topographic correction on pixel-based image composites: Improved forest cover detection in mountain environments. International Journal of Applied Earth Observation and Geoinformation 35, 320-328.
69. Wang, C., Myint, S.W., 2015. A simplified empirical line method of radiometric calibration for small unmanned aircraft systems-basedremote sensing. IEEE Journal of selected topics in applied earth observations and remote sensing 8, 1876-1885.
70. Wang, G., Weng, Q., 2013. Remote Sensing of Natural Resources. CRC Press.
71. Weisberg, S., 2013. Applied Linear Regression. Wiley.
72. Yan, X., 2009. Linear Regression Analysis: Theory and Computing. World Scientific Publishing Company Pte Limited.
73. Yang, X., Lo, C., 2000. Relative radiometric normalization performance for change detection from multi-date satellite images. Photogrammetric Engineering and Remote Sensing 66, 967-980.
74. Yuan, J., Niu, Z., 2008. Evaluation of atmospheric correction using FLAASH, Earth Observation and Remote Sensing Applications, 2008. EORSA 2008. International Workshop on. IEEE, pp. 1-6.
75.          Zhu, Z., Woodcock, C.E., 2014. Continuous change detection and classification of land cover using all available Landsat data. Remote sensing of Environment 144, 152-171.
Scientific- Research Quarterly of Geographical Data (SEPEHR)
Volume 28, Issue 112 - Serial Number 112
March 2020
Pages 151-166
Files
Share
How to cite
Statistics