Habitat mapping is an important and challenging task that helps in monitoring, managing, and preserving ecosystems. It becomes more challenging when marine habitats are mapped, as it is difficult to get quality images in an underwater environment. Moreover, achieving good location accuracy in underwater environments is an additional issue. Sonar imagery has good quality but is hard to be analyzed. Therefore, camera imagery is used for research purposes. Our research targets marine habitats - more specifically, coral reef marine habitats. Recognition of coral reef in underwater images poses a significant difficulty due to the nature of the data. Many species of coral reef have similar characteristics, i.e. higher inter-class similarity and lower intra-class similarity. Spatial borders between coral reef classes are hard to separate, as they tend to appear together in groups. For these reasons, the classification of coral reef species requires aid from marine biologists. This research work presents a technique for accurate coral reef classification using deep convolutional neural networks. The proposed approach has been validated on Moorea Labeled Corals (MLC), an imbalanced dataset, which is a subset of Moorea Coral Reef Long Term Ecological Research (MCR LTER) packaged for computer vision research. A custom valid patch dataset is extracted using the annotation files provided with the dataset. Two image enhancement algorithms and data-driven feature extraction techniques are employed using several pre-trained deep convolutional neural networks as feature extractors. Local-SPP technique is combined with feature extractors and followed by 2-layers multi-layer perceptron (MLP) classifier to achieve high classification rates.

栖息地制图是一项重要且具有挑战性的任务，有助于监视，管理和保护生态系统。当绘制海洋栖息地时，这变得更具挑战性，因为在水下环境中很难获得高质量的图像。此外，在水下环境中实现良好的定位精度是另一个问题。声纳图像质量较好，但难以分析。因此，相机图像用于研究目的。我们的研究针对海洋生境-更具体地说，是珊瑚礁海洋生境。由于数据的性质，在水下图像中识别珊瑚礁构成了很大的困难。许多种类的珊瑚礁具有相似的特征，即较高的类间相似度和较低的类内相似度。珊瑚礁类别之间的空间边界很难分开，因为它们倾向于一起出现。由于这些原因，对珊瑚礁物种的分类需要海洋生物学家的帮助。这项研究工作提出了一种使用深度卷积神经网络对珊瑚礁进行准确分类的技术。该方法已在Moorea标记珊瑚（MLC）（一种不平衡数据集）上得到验证，该数据集是为计算机视觉研究而打包的Moorea珊瑚礁长期生态研究（MCR LTER）的子集。使用数据集随附的注释文件提取自定义有效补丁数据集。使用几种预训练的深度卷积神经网络作为特征提取器，使用了两种图像增强算法和数据驱动的特征提取技术。