Histogram Layers for Synthetic Aperture Sonar Imagery
This work addresses texture analysis in SAS imagery for applications like target recognition and environmental segmentation, but it is incremental as it adapts an existing method to a new domain.
The paper tackled the problem of deep learning models not capturing certain textural information in synthetic aperture sonar (SAS) imagery by applying histogram layers, resulting in improved performance on synthetic and real-world datasets.
Synthetic aperture sonar (SAS) imagery is crucial for several applications, including target recognition and environmental segmentation. Deep learning models have led to much success in SAS analysis; however, the features extracted by these approaches may not be suitable for capturing certain textural information. To address this problem, we present a novel application of histogram layers on SAS imagery. The addition of histogram layer(s) within the deep learning models improved performance by incorporating statistical texture information on both synthetic and real-world datasets.