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
Evaluating green cover and open spaces in informal settlements of Mumbai using deep learning
Ayush Dabra,V. Dhilip Kumar +1 more
Journal ArticleDOI
Evaluation of Semantic Segmentation Methods for Land Use with Spectral Imaging Using Sentinel-2 and PNOA Imagery
TL;DR: In this paper, state-of-the-art semantic segmentation networks such as UNet and DeepLabV3+ are evaluated, consisting of two datasets based on aircraft and satellite imagery are generated as a new state of the art to test land use classification.
Journal ArticleDOI
Real-Time Tracking of Object Melting Based on Enhanced DeepLab v3+ Network
Tian-Yu Jiang,Feng-lan Ju,Ya-xun Dai,Jie Li,Yi-fan Li,Yun-jie Bai,Z.Q. Cui,Zhenghong Xu,Zun-Qian Zhang +8 more
TL;DR: Improve the expression of the loss function for the binary classification model of small object in this paper, combining the advantages of the Dice Loss binary classification segmentation and the Focal Loss balance of positive and negative samples, solving the problem of unbalanced dataset caused by the small proportion of positive samples.
Proceedings ArticleDOI
An Ensemble Model Based on Deep Semantic Segmentation Network and Graph Convolutional Network for Cloud Detection
TL;DR: In this paper , an ensemble model based on deep semantic segmentation network and graph convolutional network for cloud detection on a pixel level classification was introduced to accurately detect the clouds and clearly define boundaries given a remote sensing image.
Journal ArticleDOI
Semantic Labeling of High-Resolution Images Using EfficientUNets and Transformers
H. Almarzouqi,Lyes Saad Saoud +1 more
TL;DR: A new segmentation model that combines convolutional neural networks with transformers is proposed, and it is shown that this mixture of local and global feature extraction techniques provides significant advantages in remote sensing segmentation.
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.