Algorithms and Data Structures for Automated Change Detection and Classification of Sidescan Sonar Imagery

During Mine Warfare (MIW) operations, MIW analysts perform change detection by visually comparing historical sidescan sonar imagery (SSI) collected by a sidescan sonar with recently collected SSI in an attempt to identify objects (which might be explosive mines) placed at sea since the last time the area was surveyed. This dissertation presents a data structure and three algorithms, developed by the author, that are part of an automated change detection and classification (ACDC) system. MIW analysts at the Naval Oceanographic Office, to reduce the amount of time to perform change detection, are currently using ACDC. The dissertation introductory chapter gives background information on change detection, ACDC, and describes how SSI is produced from raw sonar data. Chapter 2 presents the author\u27s Geospatial Bitmap (GB) data structure, which is capable of storing information geographically and is utilized by the three algorithms. This chapter shows that a GB data structure used in a polygon-smoothing algorithm ran between 1.3 – 48.4x faster than a sparse matrix data structure. Chapter 3 describes the GB clustering algorithm, which is the author\u27s repeatable, order-independent method for clustering. Results from tests performed in this chapter show that the time to cluster a set of points is not affected by the distribution or the order of the points. In Chapter 4, the author presents his real-time computer-aided detection (CAD) algorithm that automatically detects mine-like objects on the seafloor in SSI. The author ran his GB-based CAD algorithm on real SSI data, and results of these tests indicate that his real-time CAD algorithm performs comparably to or better than other non-real-time CAD algorithms. The author presents his computer-aided search (CAS) algorithm in Chapter 5. CAS helps MIW analysts locate mine-like features that are geospatially close to previously detected features. A comparison between the CAS and a great circle distance algorithm shows that the CAS performs geospatial searching 1.75x faster on large data sets. Finally, the concluding chapter of this dissertation gives important details on how the completed ACDC system will function, and discusses the author\u27s future research to develop additional algorithms and data structures for ACDC

Hierarchical MRF Modeling For Sonar Picture Segmentation

This paper deals with sonar image segmentation based on a hierarchical markovian modelization. This model takes into account both the phenomenon of speckle noise through Rayleigh's law, and notions of geometry corresponding to geometric object shadows. We adopt an 8-connexity neighbourhood in order to dicriminate geometric and no-geometric shadows. The Markov Random Field (MRF) are well adapted for this kind of segmentation where a priori knowledge about the shapes we are searching is available. We propose some new results on real sonar pictures obtained with a multigrid algorithm. The hierarchical modelization allow to successfully improve the sonar image segmentation as it will be shown. 1. SHADOW DETECTION IN SONAR IMAGERY Because of its high-resolution performance a mine hunting sonar allows all kinds of objects located on the seabottom to be vizualized. Their detection, then their classification (as rocks, wrecks, geometric man-made objects, . . . ), are based on the study of th..