- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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Dense Semantic Labeling with Atrous Spatial Pyramid Pooling and Decoder for High-Resolution Remote Sensing Imagery
摘要： Dense semantic labeling is significant in high-resolution remote sensing imagery research and it has been widely used in land-use analysis and environment protection. With the recent success of fully convolutional networks (FCN), various types of network architectures have largely improved performance. Among them, atrous spatial pyramid pooling (ASPP) and encoder-decoder are two successful ones. The former structure is able to extract multi-scale contextual information and multiple effective field-of-view, while the latter structure can recover the spatial information to obtain sharper object boundaries. In this study, we propose a more efficient fully convolutional network by combining the advantages from both structures. Our model utilizes the deep residual network (ResNet) followed by ASPP as the encoder and combines two scales of high-level features with corresponding low-level features as the decoder at the upsampling stage. We further develop a multi-scale loss function to enhance the learning procedure. In the postprocessing, a novel superpixel-based dense conditional random field is employed to refine the predictions. We evaluate the proposed method on the Potsdam and Vaihingen datasets and the experimental results demonstrate that our method performs better than other machine learning or deep learning methods. Compared with the state-of-the-art DeepLab_v3+ our model gains 0.4% and 0.6% improvements in overall accuracy on these two datasets respectively.
关键词： dense semantic labeling,encoder-decoder,superpixel-based DenseCRF,remote sensing imagery,fully convolutional networks,atrous spatial pyramid pooling
更新于2025-09-23 15:23:52
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[IEEE 2018 IEEE 20th International Workshop on Multimedia Signal Processing (MMSP) - Vancouver, BC (2018.8.29-2018.8.31)] 2018 IEEE 20th International Workshop on Multimedia Signal Processing (MMSP) - A Deep Convolutional Network Based Supervised Coarse-to-Fine Algorithm for Optical Flow Measurement
摘要： The measurement of optical flow is an important problem in image processing. There are a number of methods available for optical flow estimation, including traditional variational methods, deep learning based supervised/unsupervised methods. In this work, we propose a deep convolutional network (CNN) based supervised coarse-to-fine approach, which is trained in end-to-end fashion. The proposed method is tested on standard optical flow benchmark datasets including Flying Chairs, MPI Sintel Clean and Final, KITTI. Experimental results show that the proposed framework is able to achieve comparable results to previous approaches with much smaller network architecture.
关键词： spatial-pyramid,deep learning,optical flow
更新于2025-09-23 15:22:29