Reef Cover, a coral reef classification for global habitat mapping from remote sensing

Coral reef management and conservation stand to benefit from improved high-resolution global mapping. Yet classifications underpinning large-scale reef mapping to date are typically poorly defined, not shared or region-specific, limiting end-users’ ability to interpret outputs. Here we present Reef Cover, a coral reef geomorphic zone classification, developed to support both producers and end-users of global-scale coral reef habitat maps, in a transparent and version-based framework. Scalable classes were created by focusing on attributes that can be observed remotely, but whose membership rules also reflect deep knowledge of reef form and functioning. Bridging the divide between earth observation data and geo-ecological knowledge of reefs, Reef Cover maximises the trade-off between applicability at global scales, and relevance and accuracy at local scales. Two case studies demonstrate application of the Reef Cover classification scheme and its scientific and conservation benefits: 1) detailed mapping of the Cairns Management Region of the Great Barrier Reef to support management and 2) mapping of the Caroline and Mariana Island chains in the Pacific for conservation purposes.

Coral Reef Classification using Improved WLD Feature Extraction with Convolution Neural Network Classification

Background: Coral reefs are one of the most oldest and dynamic ecosystems of the world. Manual annotation of coral reef is not possible due to lacking consistency and objectivity in human labeling. Objective: Manual annotation consumes an enormous number of hours to annotate every coral image and video frames as well as human resource. An emblematic survey states that more than 400 person hours are required to annotate 1000 images. Incidentally, some coral species have different shapes, sizes and colors, most of the corals seem indistinguishable to the human eye. In order to avoid the contradictory classifications, an expert system that can automatically annotate the corals is essential to improve the accuracy of classification. Method: The proposed improved WLD extract texture features from six combinations of color channels like (1) R Channel, (2) G Channel (3) B Channel (4) RG Channel (5) GB Channel and (6) BR Channel of an image in a holistic way while preserving their relations. The extracted features are analyzed, and classified using CNN Classifier. Results: Experiments are carried out with EILAT, RSMAS, EILAT 2 and MLC2008 datasets and the proposed improved WLD based coral reef classification is found to be appropriate. From the accuracy point of view, the improved WLD demonstrate higher accuracy compared to other state-of-the-art techniques. Conclusion: This paper analyzes the role of Improved WLD for feature extraction to classify coral reefs. For this purpose EILAT, RSMAS, EILAT 2 and MLC2008 datasets have been used. It is observed that the proposed IWLD based classifier gives promising results for coral reef classification.