Land cover classification in fused multisensor multispectral satellite imagery

The increase in number of deployed satellite constellations and the improvement in sensing capabilities have led to large volumes of data with a wide range of temporal and spatial coverage. The data analysis capability, however, has been lagging, and has historically focused on single-sensor individual images. We present results from an ongoing effort to develop satellite imagery analysis tools that aggregate information across multiple sensors and bands, and at multiple scales. We focus on field and landmark separation around Clinton, Iowa, and show land cover classification results that combine fused imagery from Planet Labs and Landsat 8. Classification performance is assessed using Cropland Data Layer images generated by USDA. Our method combines spectral, spatial, and temporal information to improve the accuracy of practical land cover classification.