# Histogram Layers for Synthetic Aperture Sonar Imagery

arXiv: https://arxiv.org/abs/2209.03878 · HTML: https://mlsanity.com/paper/2209.03878/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 30 references that are in the map.
- First posted on arXiv 8 Sep 2022.

## Builds on (most cited first, 12 of 12)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness](https://mlsanity.com/paper/1811.12231/) (2018)
- [Learning Statistical Texture for Semantic Segmentation](https://mlsanity.com/paper/2103.04133/) (2021)
- [From BoW to CNN: Two Decades of Texture Representation for Texture Classification](https://mlsanity.com/paper/1801.10324/) (2018)
- [Histogram Layers for Texture Analysis](https://mlsanity.com/paper/2001.00215/) (2020)
- [Learnable Histogram: Statistical Context Features for Deep Neural Networks](https://mlsanity.com/paper/1804.09398/) (2018)
- [Possibilistic Fuzzy Local Information C-Means for Sonar Image Segmentation](https://mlsanity.com/paper/1709.10180/) (2017)
- [Differentiable Histogram with Hard-Binning](https://mlsanity.com/paper/2012.06311/) (2020)
- [Comparison of Possibilistic Fuzzy Local Information C-Means and Possibilistic K-Nearest Neighbors for Synthetic Aperture Sonar Image Segmentation](https://mlsanity.com/paper/1904.01014/) (2019)
- [Explainable Systematic Analysis for Synthetic Aperture Sonar Imagery](https://mlsanity.com/paper/2101.03134/) (2021)
- [Image-to-Height Domain Translation for Synthetic Aperture Sonar](https://mlsanity.com/paper/2112.06307/) (2021)
- [Iterative, Deep Synthetic Aperture Sonar Image Segmentation](https://mlsanity.com/paper/2203.15082/) (2022)

## Built on by (most cited first, 2 of 2)

- [Histogram Layers for Neural Engineered Features](https://mlsanity.com/paper/2403.17176/) (2024)
- [Affine-Transformation-Invariant Image Classification by Differentiable Arithmetic Distribution Module](https://mlsanity.com/paper/2309.00752/) (2023)

## Abstract (arXiv)

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.