Dual-Channel Convolutional Neural Network with Attention Pooling and Sand Cat Swarm Optimization for Incremental Land-Cover Classification in Satellite Image Patches

Incremental Land-Cover Classification in satellite image patches concerns the categorization of different types of land cover in satellite images. Land cover mapping from satellite imagery has applications in environmental observation, planning and resource management, and disaster response. Current land cover change classification paradigms involve heavily sampled and trained state vector data models, which do not have provisions for dynamic updates consequent upon variation in the cover density sizes, seasonal changes, incremental growth of urban sprawl, deforestation, and other natural calamities. Since new satellite images become available often, retraining your models from the very beginning is both time-consuming and computationally expensive. To tackle these issues, we propose a Dual-Channel Convolutional Neural Network with Attention Pooling and Sand Cat Swarm Optimization (DualCCNNet-AP+SCSO) for incremental classification of land cover in satellite imagery patches. This methodology utilizes satellite image patches from the So2Sat image patch dataset, where images are initially preprocessed with Iterative Self-Guided Image Filtering (ISGIF). After preprocessing, the Spike-Driven Transformer (SDT) method extracts key features, which are then classified through the Dual-Channel Convolutional Neural Network with Attention Pooling (DualCCNNet-AP), fine-tuned by Sand Cat Swarm Optimization (SCSO) for accurate incremental land-cover classification. The DualCCNNet-AP+SCSO method is implemented in Python. The effectiveness of the proposed method was assessed on the So2Sat image patch dataset, where it achieved an impressive 99.9% accuracy and 99.5% recall, outperforming current techniques. Based on these conclusions about classification accuracy, it demonstrated that the proposed approach enables the effective classification of the change, especially in the smallest class of the land cover in the satellite image patches. The results of the proposed method were better, in terms of performance, incrementally increasing the classification of land-cover from satellite image patches. The outcomes are higher accuracy, fewer iterations to converge with the result, and better feature extraction in the presence of noise than classical methods of the classification tasks relating to land-cover mapping.