[A method of object detection for remote sensing-imagery based on spectral space transformation]

Area of Science:

• Remote Sensing
• Image Processing
• Signal Analysis

Context:

• Object detection is crucial for remote sensing applications.
• Traditional methods face challenges with remotely sensed imagery characteristics.

Purpose:

• To propose a novel object detection algorithm for remote sensing images.
• To leverage spectral space transformation for improved feature analysis.

Summary:

• The algorithm transforms spatial domain images to the frequency domain using Fourier transformation.
• It decomposes signals into frequency spectrum zones using wedge-shaped samples and overlay analysis.
• Object features are detected using Gabor filters with direction and frequency selectivity.

Impact:

• The proposed algorithm demonstrates superior performance in object detection.
• It exhibits effective detection capabilities, particularly for objects with specific directional features.