CODE-MM: Convex Deep Mangrove Mapping Algorithm Based on Optical Satellite Images

Mangrove mapping (MM) is a critical satellite remote sensing technology since mangrove forests have a large capacity for carbon storage among the blue carbon ecosystems. However, we surprisingly found that benchmark MM methods are all index-based ones, completely ignoring the spatially neighboring information on the one hand and quite sensitive to the threshold setting on the other hand. Deep learning has been proven to be an effective solution for incorporating the desired spatial information, but the induced big data collection of MM is difficult and time-consuming, especially for ground-truth labeling; this would be the reason why benchmark methods are all index-based ones. To solve the dilemma, we introduce convex analysis into deep learning, thereby achieving small-data learning. The proposed algorithm is hence termed convex deep MM (CODE-MM), mainly developed for the Sentinel-2 satellite, which is the mainstream satellite for the MM mission, as it involves those key green/infrared bands for characterizing mangrove multispectral signatures. We also generalize our CODE-MM to test the hyperspectral satellite data, which should be the trend for various classification missions in the future due to its strong material identifiability. Simply speaking, CODE-MM first infers a rough mangrove signature for designing a Siamese deep regularizer, which is then plugged into a convex criterion customized for the mapping task. We implement the convex criterion by deriving closed-form solutions for all the algorithmic steps, ensuring computational efficiency. Extensive experiments demonstrate that CODE-MM is insensitive to the threshold setting and yields state-of-the-art performance in accurate mangrove forest mapping.