Recent advances in machine learning and computer vision have enabled increased automation in benthic habitat mapping through airborne and satellite remote sensing. Here, we applied deep learning and neural network architectures in NASA NeMO-Net, a novel neural multimodal observation and training network for global habitat mapping of shallow benthic tropical marine systems. These ecosystems, particularly coral reefs, are undergoing rapid changes as a result of increasing ocean temperatures, acidification, and pollution, among other stressors. Remote sensing from air and space has been the primary method in which changes are assessed within these important, often remote, ecosystems at a global scale. However, such global datasets often suffer from large spectral variances due to the time of observation, atmospheric effects, water column properties, and heterogeneous instruments and calibrations. To address these challenges, we developed an object-based fully convolutional network (FCN) to improve upon the spatial-spectral classification problem inherent in multimodal datasets. We showed that with training upon augmented data in conjunction with classical methods, such as K-nearest neighbors, we were able to achieve better overall classification and segmentation results. This suggests FCNs are able to effectively identify the relative applicable spectral and spatial spaces within an image, whereas pixel-based classical methods excel at classification within those identified spaces. Our spectrally invariant results, based on minimally preprocessed WorldView-2 and Planet satellite imagery, show a total accuracy of approximately 85% and 80%, respectively, over nine classes when trained and tested upon a chain of Fijian islands imaged under highly variable day-to-day spectral inputs.

中文翻译：

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NASA NeMO-Net 的卷积神经网络：使用光谱异构遥感图像绘制海洋栖息地图

机器学习和计算机视觉的最新进展通过机载和卫星遥感提高了底栖栖息地测绘的自动化程度。在这里，我们在 NASA NeMO-Net 中应用了深度学习和神经网络架构，这是一种用于浅层底栖热带海洋系统全球栖息地测绘的新型神经多模态观测和训练网络。由于海洋温度升高、酸化和污染以及其他压力因素，这些生态系统，尤其是珊瑚礁正在发生快速变化。空气和空间遥感一直是在全球范围内评估这些重要的、通常是偏远的生态系统变化的主要方法。然而，由于观测时间、大气效应、水柱特性、以及异构仪器和校准。为了应对这些挑战，我们开发了一个基于对象的全卷积网络 (FCN) 来改进多模态数据集中固有的空间光谱分类问题。我们表明，通过结合经典方法（例如 K 最近邻）对增强数据进行训练，我们能够获得更好的整体分类和分割结果。这表明 FCN 能够有效地识别图像中相对适用的光谱和空间空间，而基于像素的经典方法在这些识别空间内的分类方面表现出色。我们基于最少预处理的 WorldView-2 和 Planet 卫星图像的光谱不变结果显示总准确度分别约为 85% 和 80%，