Detection of seagrass scars using sparse coding and morphological filter

The proximity of seagrass meadows to centers of human activity makes them vulnerable to a variety of habitat degrading insults. Physical scarring has long been recognized as an important but difficult-to-quantify source of habitat fragmentation and seagrass loss. We present a pixel-based algorithm to detect seafloor propeller seagrass scars in shallow water that promises to automate the detection and measurement of scars across the submarine landscape. 1 We applied the algorithm to multispectral and panchromatic images captured at the Deckle Beach, Florida using the WorldView-2 commercial satellite. The algorithm involves four steps using spectral and spatial information from radiometrically calibrated multispectral and panchromatic images. First, we fused multispectral and panchromatic images using a principal component analysis (PCA)-based pan-sharpening method to obtain multispectral pan-sharpened bands. In the second step, we enhanced the image contrast of the pan-sharpened bands for better scar detection. In the third step, we classified the contrast enhanced image pixels into scar and non-scar categories based on a sparse coding algorithm that produced an initial scar map in which false positive scar pixels were also present. In the fourth step, we applied post-processing techniques including a morphological filter and local orientation to reduce false positives. Our results show that the proposed method may be implemented on a regular basis to monitor changes in habitat characteristics of coastal waters.