Histogram Layers for Synthetic Aperture Sonar Imagery
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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.
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.
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12 of 30 references.
References · 12
- 2014Adam: A Method for Stochastic Optimizationcited by 37,240
- 2018ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustnesscited by 1,432
- 2021Learning Statistical Texture for Semantic Segmentationcited by 51
- 2018From BoW to CNN: Two Decades of Texture Representation for Texture Classificationcited by 40
- 2020Histogram Layers for Texture Analysiscited by 19
- 2018Learnable Histogram: Statistical Context Features for Deep Neural Networkscited by 9
- 2017Possibilistic Fuzzy Local Information C-Means for Sonar Image Segmentationcited by 5
- 2020Differentiable Histogram with Hard-Binningcited by 5
- 2019Comparison of Possibilistic Fuzzy Local Information C-Means and Possibilistic K-Nearest Neighbors for Synthetic Aperture Sonar Image Segmentationcited by 3
- 2021Explainable Systematic Analysis for Synthetic Aperture Sonar Imagerycited by 3
- 2021Image-to-Height Domain Translation for Synthetic Aperture Sonarcited by 3
- 2022Iterative, Deep Synthetic Aperture Sonar Image Segmentationcited by 2
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Citing papers per year: 2023: 1; 2024: 1.
2 papers.