Iterative, Deep, and Unsupervised Synthetic Aperture Sonar Image Segmentation

TL;DR

This paper introduces an iterative unsupervised deep learning approach for segmenting synthetic aperture sonar images, overcoming the lack of labeled data by jointly optimizing feature learning and clustering.

Contribution

It proposes a novel iterative algorithm that alternates between clustering superpixels and updating CNN parameters for improved SAS image segmentation.

Findings

Outperforms current state-of-the-art methods on benchmark datasets.

Effectively learns deep features without labeled training data.

Demonstrates significant improvement in segmentation accuracy.

Abstract

Deep learning has not been routinely employed for semantic segmentation of seabed environment for synthetic aperture sonar (SAS) imagery due to the implicit need of abundant training data such methods necessitate. Abundant training data, specifically pixel-level labels for all images, is usually not available for SAS imagery due to the complex logistics (e.g., diver survey, chase boat, precision position information) needed for obtaining accurate ground-truth. Many hand-crafted feature based algorithms have been proposed to segment SAS in an unsupervised fashion. However, there is still room for improvement as the feature extraction step of these methods is fixed. In this work, we present a new iterative unsupervised algorithm for learning deep features for SAS image segmentation. Our proposed algorithm alternates between clustering superpixels and updating the parameters of a convolutional neural network (CNN) so that the feature extraction for image segmentation can be optimized. We demonstrate the efficacy of our method on a realistic benchmark dataset. Our results show that the performance of our proposed method is considerably better than current state-of-the-art methods in SAS image segmentation.