Object Detection of Satellite Images Using Multi-Channel Higher-order Local Autocorrelation

TL;DR

This paper introduces a novel feature based on higher-order local autocorrelation for automatic object detection in multispectral satellite images, improving detection performance by capturing spectral and spatial relationships.

Contribution

The paper proposes an extended higher-order local autocorrelation feature that incorporates spectral inter-relationships, enhancing object detection in multispectral satellite imagery.

Findings

Higher detection accuracy than existing methods

Effective exploitation of spectral and spatial information

Demonstrated improved performance in experiments

Abstract

The Earth observation satellites have been monitoring the earth's surface for a long time, and the images taken by the satellites contain large amounts of valuable data. However, it is extremely hard work to manually analyze such huge data. Thus, a method of automatic object detection is needed for satellite images to facilitate efficient data analyses. This paper describes a new image feature extended from higher-order local autocorrelation to the object detection of multispectral satellite images. The feature has been extended to extract spectral inter-relationships in addition to spatial relationships to fully exploit multispectral information. The results of experiments with object detection tasks conducted to evaluate the effectiveness of the proposed feature extension indicate that the feature realized a higher performance compared to existing methods.