Image analysis in scenes with low contrast in the infrared is difficult in general. At sea, this can be further challenging due to varying levels of sea clutter. Ideally, adaptive algorithms should be able to consider the clutter to determine how sensitive they can be, and to what degree success is possible. This would allow greater control over the balance between Type I and Type II errors, 1 and improve performance. To achieve this, we need measures for sea clutter. This paper is a search for features that seem useful for discriminating between different levels of sea clutter. We investigate spatial features, such as texture energy measures, properties of the Fourier coefficients, and the statistical features of segments in a thresholded image. In addition, inspired by the field of radar, where sea clutter is well studied, we will look at the statistical distribution of the IR sea clutter itself. Results indicate that these features, when paired with classification algorithms, can be useful to discriminate between scenes with various levels of sea clutter. In addition, they might even be suitable as a quantitative measure of the amount of clutter in the scene.
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