Object Detection of Satellite Images Using Multi-Channel Higher-order Local Autocorrelation
This addresses the problem of manually analyzing large satellite image datasets for researchers or analysts, though it appears incremental as it builds on an existing feature.
The paper tackled automatic object detection in multispectral satellite images by extending higher-order local autocorrelation features to include spectral inter-relationships, resulting in higher performance compared to existing methods.
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.