ANALYTICAL CLASSIFICATION PERFORMANCE ANALYSIS OF MACHINE-LEARNING-BASED SHIP DETECTION FROM OPTICAL SATELLITE IMAGERY

We investigate the performance of machine learning (ML) binary classification models in terms of error probabilities to detect targets (specifically, ships) from optical satellite imagery. The ML approach uses a Data-Driven Decision Function (D3F), learned during training, as a decision statistic. Inspired by the Large Deviations Analysis (LDA), we observe that, under suitable conditions, the detection error probabilities decrease as the number of pixels occupied by the target(s) in the image increases. Coherent with the LDA, the D3F follows a Gaussian distribution, conditioned to parameters like the background. We propose a methodology to set a desired false alarm rate and estimate the correct decision probability, beneficial for various remote sensing applications, including maritime surveillance.