Frequency-spatial cues based sea-surface salient target detection from UAV image

This paper proposes an algorithm for salient target detection from Unmanned Aerial Vehicles (UAV) sea surface image using frequency and spatial cues. The algorithm is consisted of three parts: background suppression in the frequency domain, adaptive smoothing of the background suppressed image and salient target detection via adaptive thresholding, region growth and cluster. The sea surface background in UAV image is modeled as non-salient components which correspond to the spikes of the amplitude spectrum in the frequency domain. The background suppression is achieved by removing the spikes using a low pass Gaussian kernel of proper scale. In order to eliminate the negative effects brought by the complex textures, a Gaussian blur kernel is introduced to process the background suppressed image and its scale is determined by the entropy of the background suppressed image. The salient target is detected using adaptive thresholding, region growth and cluster performed on the blurred background suppressed image. Experiments on a large number of images indicate that the algorithm proposed in this paper can detected the sea surface salient target accurately and efficiently.