Intelligent assessment of mine native vegetation restoration using optical and SAR imagery with a deep attention convolutional network

High-intensity mining activities in mining areas cause severe disturbances to native vegetation. Native vegetation plays an irreplaceable role in maintaining biodiversity, controlling soil erosion and regulating regional microclimates, and its restoration status is directly related to the environmentally sustainable development of ecologically sensitive areas. At present, accurate assessment of native vegetation in complex mining scenarios still faces challenges. This study constructs an intelligent evaluation framework integrating multi-source remote sensing and deep learning, and proposes the Multi-source Remote Sensing Fusion Deep Attention Convolutional Network (MSR-DACN). Based on optical remote sensing images and synthetic aperture radar (SAR) data, this network achieves accurate identification and coverage estimation for ground objects with complex backgrounds and multi-scales. To improve the ability to acquire multi-scale ground object features and efficiently fuse multi-source information from optical and SAR images, a deep attention mechanism is added to MSR-DACN. The results show that the suggested model’s overall accuracy of 0.881 is 5.51% greater than that of the best multi-context attention network. In the meantime, the F1-score rises by 7.41% and the recall rate rises by 8.39%. The coverage estimation results based on this model show that from 2015 to 2024, the high-coverage areas in the Qilian Mountains mining area expanded significantly, reflecting the positive promotion effect of the restoration and governance measures implemented in recent years on ecological reconstruction. The interpretable analysis shows that the fusion feature has the highest contribution value, the optical feature has a strong advantage in vegetation spectral expression, and the SAR feature plays an auxiliary role in surface structure and scattering information supplement. This study improves the intelligent analysis capability of remote sensing data in complex ecological scenarios and can provide strong technical support for mining area ecological restoration monitoring and policy formulation.