Accurate Recognition of Deep-Sea Small-Size Polymetallic Nodules Based on Multi-source Data and Deep Learning Model

Solid mineral resources form the essential material foundation for the sustainable development of human society. The international seabed hosts vast and potentially valuable mineral deposits, among which deep-sea polymetallic nodules represent a key marine resource. Focusing on a specific area in the Western Pacific, this study aims to identify and classify small-scale polymetallic nodules in the deep-sea environment. We employ super-resolution reconstruction methods to enhance the resolution of deep-sea hydroacoustic images. Subsequently, a super-pixel segmentation approach is applied to construct a sample enhancement model for deep-sea objects, enabling in-depth extraction of multi-dimensional heterogeneous features from seabed targets and facilitating the effective development of training samples. Constrained by geological seabed samples, an accurate recognition model for seabed polymetallic nodules is established, achieving intelligent mineral classification based on multi-source data such as bathymetry and backscatter. Ultimately, by leveraging the generalization capability of the model, the recognition and classification of untrained samples can be accomplished, thereby promoting the application of the proposed algorithm in large-scale deep-sea mineral exploration.