Abstract. Extracting building footprints utilizing deep learning-based (DL-based) methods for high-resolution remote sensing images is one of the current research interest areas. However, the extraction results suffer from blurred edges, rounded corners and detail loss in general. Hence, this article presents a detail-oriented deep learning network named eU-Net (enhanced U-Net). The method adopted in this study, imagery send into the pre-module, which consists of the Canny edge detector, Principal Component Analysis (PCA) and the inter-band ratio operations, before feeding them into the network. Then, process skips connections used in the network to reduce the loss of details during edge and corner detection. The encoding and decoding modules, in this network, are redesigned to expand the perceptual field with shortcut connections and stacked layers. Finally, a Dropout module is added in the bottom layer of the network to avoid the over-fitting problem. The experimental results indicate that the methods used in this study outperform other commonly used and state-of-the-art methods of FCN-8s, U-net, DeepLabv3 and Fast SCNN.
Classical lossless compression algorithm highly relies on artificially designed encoding and quantification strategies for general purposes. With the rapid development of deep learning, data-driven methods based on the neural network can learn features and show better performance on specific data domains. We propose an efficient deep lossless compression algorithm, which uses arithmetic coding to quantify the network output. This scheme compares the training effects of Bi-directional Long Short-Term Memory (Bi-LSTM) and Transformers on minute-level power data that are not sparse in the time-frequency domain. The model can automatically extract features and adapt to the quantification of the probability distribution. The results of minute-level power data show that the average compression ratio (CR) is 4.06, which has a higher compression ratio than the classical entropy coding method.
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