Unsupervised change detection of water, soil and vegetation covers using multi-sensor remote sensing images based on Tasseled Cap transformation

Ghorbani, Atiyeh; Sadeghi, Vahid

doi:10.22059/ije.2021.328606.1536

Unsupervised change detection of water, soil and vegetation covers using multi-sensor remote sensing images based on Tasseled Cap transformation

Document Type : Research Article

Authors

Atiyeh Ghorbani ¹
Vahid Sadeghi ²

¹ MSc of Remote sensing engineering, Department of Geomatics Engineering, Faculty of Civil Engineering, University of Tabriz, Iran

² Assistant Professor, Department of Geomatics Engineering, Faculty of Civil Engineering, University of Tabriz, Iran

10.22059/ije.2021.328606.1536

Abstract

Over the last two centuries, natural hazards and widespread human activities have led to significant changes in water, soil, and vegetation covers. Although, multi-temporal remote sensing (RS) images provide continuous monitoring of changes in land surface, one of the most important challenges is applying multi-sensor images to detect land cover changes in unsupervised flow. This study aims to provide a method for changes detection in water, soil, and vegetation covers within multi-sensor RS images. In this regard, new biophysical parameters have been defined for the Sentinel2B sensor, as well as a new unsupervised method for binary and multiple changes detection has been developed. Landsat8 OLI and Sentinel2B images of the southwestern shore of the Urmia Lake were used to evaluate this method. In the proposed method; first, the generalizability of the Tasseled Cap (TC) transformation was investigated and a new TC transformation for the Sentinel2B sensor was estimated. After TC, the images were transferred from multispectral feature space to biophysical feature space, and a binary changes map was generated using the proposed multivariate iterative trimming method. Then, via FCM clustering, the changed samples were separated into a certain number of clusters determined by the WSJI criterion which is one of the innovations of the proposed method. Overall accuracy, missed error, and false alarm of the proposed approach are %92.06, %9.62, and %6.27, respectively. The proposed method in this paper can be used as an unsupervised, accurate, and reliable technique for changes detection in water, soil, and vegetation covers.

Keywords

References

[1]. Moghimi A, Ebadi H, Sadeghi V. Review of Change Detection Methods from Multitempolar Satellite Images by Pixel-Based and Object-Based Approach. Geospatial Engineering Journal. 2016; 7 (2): 99-110. [Persian]

[2]. Rokni K, Ahmd A, Selamat A, Hazini S. Water Feature Extraction and Change Detection Using Multitemporal Landsat Imagery. Remote sensing. 2014; 6(5): 4173-4189.

[3]. Huang X, Zhang L, Zhu T. Building change detection from multitemporal high-resolution remotely sensed images based on a morphological building index. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2013; 7(1): 105-115.

[4]. Cifuentes P, Malpica J.A, González-Matesanz F.J. Change Detection with SPOT-5 and FORMOSAT-2 Imageries. International Symposium on Visual Computing. 2008; 1186-1195.

[5]. Deng J.S, Wang K, Li J, Deng Y.H. Urban Land Use Change Detection Using Multisensor Satellite Images. Soil Science Society of China. 2009; 19(1): 96-103.

[6]. Correa Y.T.S, Bovolo F, Bruzzone L. Change detection in Very High Resolution multisensor optical images. Image and Signal Processing for Remote Sensing XX. October 2014.

[7]. Liu G, Delon J, Gousseau Y, Tupin F. Unsupervised change detection between multi-sensor high resolution satellite images. 24th European Signal Processing Conference. Budapest, Hungary. 2016.

[8]. Ali Baig M.H, Zhang L, Shuai T, Tong Q. Derivation of a tasseled cap transformation based on Landsat 8 at-satellite reflectance. Remote Sensing Letters. 2014; 5(5): 423-431.

[9]. Nedkov R. Orthogonal transformation of segmented images from the satellite Sentinel-2. Comptes rendus de l’Academie bulgare des Sciences. 2017; 70(5): 687-692.

[10]. Huang C, Wylie B, Yang L, Homer C, Zylstra G. Derivation of a tasseled cap transformation based on Landsat 7 at-satellite reflectance. International journal of remote sensing. 2002; 23(8): 1741-1748.

[11]. Kauth R.J, Thomas G.S. The Tasseled Cap - A Graphic Description of the Spectral-Temporal Development of Agricultural Crops as Seen by LANDSAT. LARS Symposia. 1976.

[12]. Rahman, S, Mesev V. Change Vector Analysis, Tasseled Cap, and NDVI-NDMI for Measuring Land Use/Cover Changes Caused by a Sudden Short-Term Severe Drought: 2011 Texas Event. Remote sensing. 2019; 11(19): 1-21.

[13]. Thakkar A, Desai V, Patel A, Potdar M. An effective hybrid classification approach using tasseled cap transformation (TCT) for improving classification of land use/land cover (LU/LC) in semi-arid region: a case study of Morva-Hadaf watershed, Gujarat, India. Arabian journal of Geoscience. 2016; 9: 180.

[14]. Yarbrough L.D, Easson, G, Kuszmaul J.S. Using At-Sensor Radiance and Reflectance Tasseled Cap Transforms Applied to Change Detection for the ASTER Sensor. Aster. 2005; 2:141-145.

[15]. Minu S, Shetty A. A Comparative Study of Image Change Detection Algorithms in MATLAB. Aquatic Procedia. 2015; 4: 1366-1373.

[16]. Jensen J.R. Introductory Digital Image Processing: A Remote Sensing Perspective 4th ed. Pearson Series in Geographic Information Science; 2015.

[17]. Sadeghi V. Development of fuzzy logic thresholding technique for automatic changes detection in satellite imagery. PhD thesis. KNT University of Technology. 1395. [Persian]

[18]. Ghorbani A, Sadeghi V, Ranjbari A. Change detection in optical multi-sensor remote sensing images using biophysical transformations correction. 12th National Congress of Civil Engineering. 2020. [Persian]

[19]. Teng S.P, Chen Y.K, Cheng K.S, Lo H.C. Hypothesis-test-based landcover change detection using multi-temporal satellite images–A comparative study. Advances in Space Research, 2008; 41(11): 1744-1754.

[20]. Thill M. The Relationship between the Mahalanobis Distance and the Chi-Squared Distribution. https://markusthill.github.io/mahalanbis-chi-squared. 2017.

[21]. Ghosh A, Mishra N.S, Ghosh S. Fuzzy clustering algorithms for unsupervised change detection in remote sensing images. Information Sciences. 2011; 181(4): 699-715.

[22]. Desclée ‌ B, Bogaert P, Defourny P. Forest change detection by statistical object-based method. Remote Sensing of Environment. 2006; 102 (1): 1-11.

[23]. Li H, Zhang S, Ding X, Zhang C, Dale P. Performance evaluation of cluster validity indices (CVIs) on multi/hyperspectral remote sensing datasets. Remote Sensing. 2016; 8(4): 2-22.

[24]. Ghosh A, Mishra N.S, Ghosh S. Fuzzy clustering algorithms for unsupervised change detection in remote sensing images. Information Sciences, 2011; 181(4): 699-715.

[25]. Patra S, Ghosh S, Ghosh A. Histogram thresholding for unsupervised change detection of remote sensing images. International Journal of Remote Sensing. 2011; 32(21): 6071–6089.