Algae-Mamba: A Spatially Variable Mamba for Algae Extraction From Remote Sensing Images
1939-1404
Abstract
To maintain marine ecosystem health, effective algae monitoring is essential. Traditional threshold-based methods and standard machine learning techniques often fall short in accurately and automatically distinguishing algae types. This study presents Algae-Mamba, an advanced network for algae extraction that builds upon the visual state-space (VSS) model. The Algae-Mamba unified VSS model and the Kolmogorov–Arnold network proposed the Kolmogorov–Arnold visual state space (KVSS) model. KVSS block combines VSS for comprehensive global feature extraction with a small-kernel convolution module to capture local spatial and channel-specific information, supporting multiscale data processing and improving model generalization. The KVSS represents high-dimensional features using orthogonal polynomial combinations through Gram polynomials and leverages an attention mechanism to index interactions between target algae and their features, enabling the model to learn distinct characteristics of sargassum and ulva effectively and enhance extraction precision. To address the common misclassification between sargassum and ulva under limited spectral data, Algae-Mamba incorporates the normalized difference water index (NDWI) to enhance semantic richness. Furthermore, the model addresses class imbalances by employing a hybrid cross-entropy and Lovász-Softmax loss function, ensuring balanced and robust training. Unlike other methods that depend on extensive spectral information, Algae-Mamba achieves precise differentiation of sargassum and ulva with just 4-band spectral imagery, offering a powerful tool for monitoring marine ecological security. Testing on the GF-1 algae dataset demonstrates that Algae-Mamba surpasses other deep learning approaches in accurately extracting sargassum and ulva.