Multiscale Feature Extraction for Remote Sensing Image Analysis Using Discrete Wavelet Transform

Mohammed Abdulhasan Hussein¹, Rajaa Daami Resen², Ali Nafea Yousif², Oday Ali Hassen^1,3,*,
Ansam A. Abdulhussein²

¹Ministry of Education, Wasit Education Directorate. Iraq

²University of Information Technology and Communications, Baghdad, Iraq

³Computer Department, College of Education for Pure Sciences, Wasit University, Iraq

Emails: mohammedabdalhassan7@gmail.com; rajaa.alnidway@uoitc.edu.iq; alinafea@uoitc.edu.iq; odayali@uowasit.edu.iq; Ansam.Abdulhussein@uoitc.edu.iq

Abstract

Remote sensing image evaluation faces continual challenges in extracting discriminative capabilities from complex; multi-scale landscapes the use of conventional spectral-spatial techniques, which often fail to capture hierarchical structures correctly. This examine proposes a brand-new methodology that leverages the discrete wavelet remodel (DWT) for multi-scale characteristic extraction. It is carried out thru Python and the PyWavelets library to offer an open-source, reproducible solution. The framework decomposes pictures into subscales of path and directional detail throughout multiple scales, extracting statistical and textural descriptors optimized for remote sensing obligations. A complete assessment of 500 multispectral patches (Sentinel-2, Landsat-8, and high-decision sensors) demonstrates advanced overall performance in land cover class, accomplishing an accuracy of 92.4%, outperforming uncooked pixel methods (84.1%), important issue evaluation (PCA) (87.3%), and GLCM-based totally techniques (89.6%). A sensitivity analysis famous that Daubeches wavelet 4 at decomposition level three improves function discriminability, in particular for agricultural textures (91.2% accuracy) and concrete limitations (IoU=0.873), while directional subbands (LH/HL) reduce transition area mistakes by way of 23%. The computational efficiency (184 ms/megapixel) remains possible. These consequences show that DWT is an effective and handy device for improving faraway sensing analysis, with the full code and datasets being made publicly available to promote community adoption and foster innovation.

Keywords: Discrete Wavelet Transform (DWT); Remote Sensing Feature Extraction; Multiscale Image Analysis; PyWavelets Implementation; Land-Cover Classification