Signal-Centric Remote Sensing via Alternative Preprocessing and Acoustic Processing for ML-Driven Applications

Logan Luna; Sirio Jansen-Sánchez; Ilteris Demirkiran; Leo Ghelarducci

cs.LGSep 17, 2026

Signal-Centric Remote Sensing via Alternative Preprocessing and Acoustic Processing for ML-Driven Applications

Authors: Logan Luna, Sirio Jansen-Sánchez, Ilteris Demirkiran, Leo Ghelarducci

Organizations: Electrical Engineering and Computer Science Embry-Riddle Aeronautical University Daytona Beach, USA

Abstract

The dominant method of processing sonar data is using image-based representations, requiring the preprocessing of image data on autonomous systems. We propose an alternative data processing method for remote sensing applications via the use of data in Comma-Seperated Value format. Experimentation on our alternative approach shows a reduction of processing time by 91.18%, an improvement in accurate object detection by Machine Learning, and an increase in SNR (Signal-to-noise ratio), PSNR (Peak signal-to-noise ratio), and other evaluation metrics.

Figures & tables

Panel of Figure 1 — Fig. 1 : Comparison of different views of objects used in the dataset.

Timestamp	Angle (°)	Scan Data
1728153122.072070224	4	255 255 255 161 155 138 60 52 70

TABLE I : Sample Sonar Data Before and After Preprocessing

Timestamp	Angle (°)	Scan Data
1728153122.072070224	4	255 255 255 161 155 138 60 52 70

TABLE II : Sample Sonar Data Before and After Preprocessing

Panel of Figure 6 — Fig. 4 : Visualization of Median Filtering Process.

Panel of Figure 7 — Fig. 5: Background Subtraction Process

Panel of Figure 8 — Fig. 6: Display of K-Means Clustering on Sample Data

Panel of Figure 9 — Fig. 7: Comparison of different views of objects used in the dataset.

Method	Filtering Time (s)	Total Processing Time (s)
CSV Approach	0.0031	0.0031
CV Image Processing	0.003798	0.037419

TABLE III: Processing Time Comparison between Proposed CSV Approach and CV Image Processing

Panel of Figure 11 — Fig. 8: Comparison of Differences between Processed and Original Sonar Scans

Fig. 9: Experimental Sensor Fusion System on MATE ROV Submersible

Remote Sensing

Remote sensing uses satellite, aerial, and ground-based observations to measure and map Earth and other planetary surfaces.

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Jun 9, 2026J/K move · Enter open · S save

Deep learning for echo sounder data

Ketil Malde

Underwater Acoustics

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There is no doubt that over the last decade, techniques from the field of machine learning have revolutionized how we process and interpret data, especially images and text. For underwater observations acoustics is a primary source of information, and naturally, deep learning methods have been applied to echograms and other acoustics data, but so far with rather modest results. Here, we argue that due to intrinsic properties of acoustic data, substantial advances will likely require research into deep learning methods beyond mere recycling of models and techniques from image processing. Currently, the potential for breakthroughs in method development is hindered by the lack of standard data formats and organization, and even more by the lack of readily available, high quality data sets with established performance goals. To advance the field, these shortcomings should be remedied

Abstract

Remote Sensing

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