论文标题
合成光圈声纳图像的直方图层
Histogram Layers for Synthetic Aperture Sonar Imagery
论文作者
论文摘要
合成孔径声纳（SAS）图像对于多种应用至关重要，包括目标识别和环境分割。深度学习模型在SAS分析中取得了很大的成功。但是，这些方法提取的功能可能不适合捕获某些纹理信息。为了解决这个问题，我们提出了直方图层在SAS图像上的新应用。在深度学习模型中添加直方图层，通过在合成和现实世界数据集上合并统计纹理信息，从而提高了性能。
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.
