Journal ArticleDOI
Incorporating DeepLabv3+ and object-based image analysis for semantic segmentation of very high resolution remote sensing images
TLDR
This study proposes a semantic segmentation method for VHR images by incorporating deep learning semantic segmentsation model (DeepLabv3+) and object-based image analysis (OBIA), wherein DSM is employed to provide geometric information to enhance the interpretation of V HR images.Abstract:
Semantic segmentation of remote sensing images is an important but unsolved problem in the remote sensing society. Advanced image semantic segmentation models, such as DeepLabv3+, have achieved ast...read more
Citations
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Journal ArticleDOI
Research on Ground Object Classification Method of High Resolution Remote-Sensing Images Based on Improved DeeplabV3+
TL;DR: This paper improves the DeeplabV3+ network by enhancing the ability of network to extract image features; it reduces network training costs; and it achieves better semantic segmentation accuracy.
Journal ArticleDOI
Open-Pit Mine Extraction from Very High-Resolution Remote Sensing Images Using OM-DeepLab
Change Detection of Open-Pit Mine Based on Siamese Multiscale Network
TL;DR: Li et al. as discussed by the authors proposed a siamese multiscale change detection network (SMCDNet) with an encoder-decoder structure for automatic change detection of open-pit mines.
Journal ArticleDOI
Minimum Class Confusion based Transfer for Land Cover Segmentation in Rural and Urban Regions
TL;DR: In this article , a semantic segmentation method that allows to make land cover maps by using transfer learning methods is presented, where models trained in low-resolution images with insufficient data for the targeted region or zoom level.
Proceedings ArticleDOI
Transfer Learning for Land Cover Semantic Segmentation
TL;DR: Observations indicate that transfer learning is more advantageous when two datasets share a comparable zoom level and are annotated with identical rules; otherwise, treating the data as unlabeled and employing semi-supervised learn is more effective.
References
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Book ChapterDOI
U-Net: Convolutional Networks for Biomedical Image Segmentation
TL;DR: Neber et al. as discussed by the authors proposed a network and training strategy that relies on the strong use of data augmentation to use the available annotated samples more efficiently, which can be trained end-to-end from very few images and outperforms the prior best method (a sliding-window convolutional network) on the ISBI challenge for segmentation of neuronal structures in electron microscopic stacks.
Journal ArticleDOI
Deep learning
TL;DR: Deep learning is making major advances in solving problems that have resisted the best attempts of the artificial intelligence community for many years, and will have many more successes in the near future because it requires very little engineering by hand and can easily take advantage of increases in the amount of available computation and data.
Book
Deep Learning
TL;DR: Deep learning as mentioned in this paper is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts, and it is used in many applications such as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames.
Proceedings ArticleDOI
Fully convolutional networks for semantic segmentation
TL;DR: The key insight is to build “fully convolutional” networks that take input of arbitrary size and produce correspondingly-sized output with efficient inference and learning.
Proceedings ArticleDOI
Densely Connected Convolutional Networks
TL;DR: DenseNet as mentioned in this paper proposes to connect each layer to every other layer in a feed-forward fashion, which can alleviate the vanishing gradient problem, strengthen feature propagation, encourage feature reuse, and substantially reduce the number of parameters.