Underwater images quality improvement techniques for feature extraction based on comparative analysis for species classification

The object recognition under the sea is a difficult task due to the presence of different environmental conditions around oceans. Various techniques have been used to enhance the properties of underwater images. This paper proposed the pre-processing and feature extraction techniques for object recognition under the seawater. The proposed methodology consists of several pre-processing steps for underwater images to make a compatible image for feature extraction purposes, which helps in object recognition. The proposed pre-processing works on the intensity, contrast, and sharpness of the object to improve their visualization quality. We compared the proposed ALE algorithm (Atmospheric-Light-Enhancement Algorithm) with existing image enhancement techniques such as Intensity-based, Histogram based, Contrast-based image enhancement techniques. From the analysis, the ALE algorithm is most effective for underwater images. Also, a comparative analysis has been presented for feature extraction from underwater images using PCA (Principal Component Analysis), SIFT (Scale-Invariant Feature Transform), and SURF (Speeded up Robust Features) to extract the unique feature. The proposed technique SURF shows better results attainment in contrary to other feature extraction techniques. At last, in simulation analysis, we observed that the error rate and feature extraction time taken by SURF is better than PCA as well as SIFT, due to the speed up methodology of SURF during the feature points filtration.