Geometric distortion and mixed pixel elimination via TDYWT image enhancement for precise spatial measurement to avoid la
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METHODOLOGIES AND APPLICATION
Geometric distortion and mixed pixel elimination via TDYWT image enhancement for precise spatial measurement to avoid land survey error modeling M. Prabu1 • N. R. Shanker2 • A. Celine Kavida3 • E. Ganesh4
Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract In remote sensing, land cover classification of vegetation and water area from satellite image play a vital role for rural and urban planning and development. Existing algorithms of land cover classification require more sample image datasets for training. For existing algorithms, land cover classification of vegetation and water area is a challenging task because of mixed pixel and geometric distortion over boundary and curvature region. Mixed pixel affects the precise classification and measurement of land cover. Geometric distortion arises due to frame of isotropic and angular selectivity during image acquisition and affects the contour of land cover. In this paper, the proposed transverse dyadic wavelet transform (TDyWT) enhances and classifies vegetation and water area in land cover from LANDSAT image without training datasets. The proposed TDyWT uses Haar wavelet for decomposition and Burt 5 9 7 wavelet for reconstruction. The TDyWT enhances the contour, curvature, and boundary of vegetation and water area in LANDSAT image due to reversible and lifting properties of wavelet. TDyWT removes geometric distortion and spatial scale error of mixed pixel. In traditional land surveying spatial scale error reduction eliminates through total station and error modeling techniques. From the results, the proposed TDyWT algorithm classifies the area of subclass of vegetation and water with the 95% of accuracy with respect to ground truth survey methods. Keywords Remote sensing Land cover classification Geometric distortion Transverse dyadic wavelet transform
1 Introduction Communicated by V. Loia. & M. Prabu [email protected] N. R. Shanker [email protected] A. Celine Kavida [email protected] E. Ganesh [email protected] 1
Department of ECE, Misrimal Navajee Munoth Jain Engineering College, Chennai, Tamil Nadu 600 097, India
2
Department of ECE, Aalim Muhammed Salegh College of Engineering, Chennai, Tamil Nadu 600 054, India
3
Department of Physics, Vel Tech Multi Tech Dr.Rangarajan Dr. Sakunthala Engineering College, Chennai, Tamil Nadu 600 054, India
4
Saveetha Engineering College, Chennai, Tamil Nadu 602105, India
Remote sensing is the science of obtaining information of earth surface from satellites. Satellite sensor observes the reflected radiation from earth surface such as vegetation, water body, and objects and converts to image. Satellite image sensor is classified as passive and active. The passive sensor utilizes the sun radiation as the source, whereas active sensor has self-source radiation such as microwave, LF, and HF for image acquisition. For example, European ERS-2 satellite consists of active sensor such as syntheticaperture radar
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