  Autonomous repair of deep-sea coral reefs is a recent proposed idea to
support the oceans ecosystem in which is vital for commercial fishing, tourism
and other species. This idea can be operated through using many small
autonomous underwater vehicles (AUVs) and swarm intelligence techniques to
locate and replace chunks of coral which have been broken off, thus enabling
re-growth and maintaining the habitat. The aim of this project is developing
machine vision algorithms to enable an underwater robot to locate a coral reef
and a chunk of coral on the seabed and prompt the robot to pick it up. Although
there is no literature on this particular problem, related work on fish
counting may give some insight into the problem. The technical challenges are
principally due to the potential lack of clarity of the water and platform
stabilization as well as spurious artifacts (rocks, fish, and crabs). We
present an efficient sparse classification for coral species using supervised
deep learning method called Convolutional Neural Networks (CNNs). We compute
Weber Local Descriptor (WLD), Phase Congruency (PC), and Zero Component
Analysis (ZCA) Whitening to extract shape and texture feature descriptors,
which are employed to be supplementary channels (feature-based maps) besides
basic spatial color channels (spatial-based maps) of coral input image, we also
experiment state-of-art preprocessing underwater algorithms for image
enhancement and color normalization and color conversion adjustment. Our
proposed coral classification method is developed under MATLAB platform, and
evaluated by two different coral datasets (University of California San Diego's
Moorea Labeled Corals, and Heriot-Watt University's Atlantic Deep Sea).
