Satellite-based resource classification is a critical task in remote
sensing, enabling efficient resource management and monitoring for
environmental and developmental applications. Traditional
classification techniques often struggle to balance accuracy and
computational efficiency due to the complexity and high dimensionality
of satellite imagery data. The proposed approach integrates the Hybrid
Curvelet Transform (HCT) with the K-Nearest Neighbor (KNN)
algorithm to address these challenges effectively. The Hybrid Curvelet
Transform is utilized to enhance image feature extraction by capturing
both multiscale and multidirectional information, enabling better edge
preservation and noise reduction. Subsequently, the K-Nearest
Neighbor algorithm is employed for classification due to its simplicity
and effectiveness in handling non-linear data patterns. The
methodology was tested on a satellite image dataset comprising 1000
samples, categorized into five resource classes: water bodies,
vegetation, urban areas, barren lands, and snow cover. The proposed
approach achieved an accuracy of 96.8%, outperforming traditional
methods such as Principal Component Analysis (PCA) and Support
Vector Machines (SVM), which achieved 88.5% and 92.3% accuracy,
respectively. Additionally, the hybrid approach demonstrated a
classification precision of 95.4%, recall of 96.2%, and F1-score of
96.1%. The computational time for classification was reduced by 15%,
indicating the approach's efficiency in processing large satellite
datasets.
Priusha Narwariya, Susheel kumar Tiwari
Madhyanchal Professional University, India
Satellite Imagery, Curvelet Transform, K-Nearest Neighbor, Resource Classification, Remote Sensing
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| Published By :
ICTACT
Published In :
ICTACT Journal on Image and Video Processing
( Volume: 15 , Issue: 3 , Pages: 3469 - 3475 )
Date of Publication :
February 2025
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