Cascaded Deep Learning Model for Accurate Land Use and Land Cover Classification

Remote sensing technologies, such as aerial photography and satellite remote sensing, play a crucial role in assessing and monitoring the changes in land use and land cover (LULC) for large areas. Deep learning methods are recently becoming popular for LULC classification and have maintained promising performance in many applications. However, challenges remain in LULC classification for the complex semantic appearance in high-resolution remote sensing images. In this paper, we proposed a novel framework that cascades two deep learning models i.e., ResNet and SegNet for land use and land cover classification. The advantages of strong semantic feature extraction (ResNet) and efficient boundary delineation (SegNet) capabilities of these two models can be combined to derive highly accurate land use and land cover classification results. Our method performed well on the recently developed multisource Dense-Pixel Annotation Dataset Globe230K.