CART Model for Black-Odorous Water Body Identification

The CART classification model performs self-learning from training data to automatically determine optimal thresholds and classification rules. The model selects spectral features and sets thresholds through iterative training on labeled black-odorous and non-black-odorous water body samples, eliminating the need for manual expert threshold setting while maintaining scientific objectivity and consistency.

The model uses feedback from training data to continuously optimize classification thresholds. During the training phase, the model learns from labeled samples and adjusts its internal parameters and thresholds based on classification performance, creating a self-improving system that adapts to different water body conditions without requiring repeated expert intervention.

The CART classification model merges multiple spectral features (including but not limited to blue band, green band, red band, near-infrared band reflectance values) into a comprehensive classification system. The model evaluates combinations of these features together to distinguish black-odorous water bodies, leveraging the complementary information from each spectral band to improve overall identification accuracy while maintaining a unified analytical framework.

The model transitions from single-feature analysis to multi-dimensional spectral space analysis. By considering multiple spectral bands simultaneously and evaluating their combined effects through the decision tree structure, the model creates additional discriminative dimensions that enable better separation between black-odorous and non-black-odorous water bodies that cannot be distinguished using single features alone.