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RSS FeedsRemote Sensing, Vol. 11, Pages 2380: DE-Net: Deep Encoding Network for Building Extraction from High-Resolution Remote Sensing Imagery (Remote Sensing)

 
 

14 october 2019 16:00:56

 
Remote Sensing, Vol. 11, Pages 2380: DE-Net: Deep Encoding Network for Building Extraction from High-Resolution Remote Sensing Imagery (Remote Sensing)
 


Deep convolutional neural networks have promoted significant progress in building extraction from high-resolution remote sensing imagery. Although most of such work focuses on modifying existing image segmentation networks in computer vision, we propose a new network in this paper, Deep Encoding Network (DE-Net), that is designed for the very problem based on many lately introduced techniques in image segmentation. Four modules are used to construct DE-Net: the inceptionstyle downsampling modules combining a striding convolution layer and a max-pooling layer, the encoding modules comprising six linear residual blocks with a scaled exponential linear unit (SELU) activation function, the compressing modules reducing the feature channels, and a densely upsampling module that enables the network to encode spatial information inside feature maps. Thus, DE-Net achieves stateoftheart performance on the WHU Building Dataset in recall, F1-Score, and intersection over union (IoU) metrics without pretraining. It also outperformed several segmentation networks in our self-built Suzhou Satellite Building Dataset. The experimental results validate the effectiveness of DE-Net on building extraction from aerial imagery and satellite imagery. It also suggests that given enough training data, designing and training a network from scratch may excel fine-tuning models pre-trained on datasets unrelated to building extraction.


 
193 viewsCategory: Geology, Physics
 
Remote Sensing, Vol. 11, Pages 2374: Improved Spatial-Spectral Superpixel Hyperspectral Unmixing (Remote Sensing)
Remote Sensing, Vol. 11, Pages 2379: A Filter for SAR Image Despeckling Using Pre-Trained Convolutional Neural Network Model (Remote Sensing)
 
 
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