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Building extraction is an essential component of study in the science ofremote sensing, and applications for building extraction heavily rely onsemantic segmentation of high-resolution remote sensing imagery. Semanticinformation extraction gap constraints in the present deep learning basedapproaches, however can result in inadequate segmentation outcomes. To addressthis issue and extract buildings with high accuracy, various efficientNetbackbone based U-Net++ has been proposed in this study. The designed network,based on U-Net, can improve the sensitivity of the model by deep supervision,voluminous redesigned skip-connections and hence reducing the influence ofirrelevant feature areas in the background. Various effecientNet backbone basedencoders have been employed when training the network to enhance the capacityof the model to extract more relevant feature. According on the experimentalfindings, the suggested model significantly outperforms previous cutting-edgeapproaches. Among the 5 efficientNet variation Unet++ based on efficientb4achieved the best result by scoring mean accuracy of 92.23%, mean iou of88.32%, and mean precision of 93.2% on publicly available Massachusettsbuilding dataset and thus showing the promises of the model for automaticbuilding extraction from high resolution satellite images.

Performance Analysis of Various EfficientNet Based U-Net++ Architecture for Automatic Building Extraction from High Resolution Satellite Images