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
Capturing Small Objects and Edges Information for Cross-Sensor and Cross-Region Land Cover Semantic Segmentation in Arid Areas
TL;DR: Zhang et al. as mentioned in this paper proposed FPN_PSA_DLV3+ network, which improved atrous spatial pyramid pooling module extracts the multiscale features, especially small-scale feature details; feature pyramid network (FPN) module realizes better integration of detailed information and semantic information; and the spatial context information at both global and local levels is enhanced by introducing polarized self-attention (PSA) module.
Journal ArticleDOI
Semantic segmentation for remote sensing images based on an AD-HRNet model
TL;DR: Zhang et al. as mentioned in this paper proposed a new model by combining HRNet with attention mechanisms and dilated convolution, denoted as AD-HRNet for the semantic segmentation of remote sensing images.
Proceedings ArticleDOI
A Joint Semantic Segmentation Loss Function for Imbalanced Datasets
TL;DR: In this article , a loss function that combines weighted focal loss with Jaccard loss has been developed for remote sensing image analysis, which has been used to train U-Net and DeepLabV3+ semantic segmentation models on the recently introduced Land-cover.ai dataset.
Journal ArticleDOI
BiTSRS: A Bi-Decoder Transformer Segmentor for High-Spatial-Resolution Remote Sensing Images
TL;DR: In this paper , a novel transformer-based model named the bi-decoder transformer segmentor for remote sensing (BiTSRS) is proposed, aiming at alleviating the problem of flexible feature decoding, through a bidecoder design for semantic segmentation of RS images.
Journal ArticleDOI
Change Detection of Open-Pit Mine Based on Siamese Multiscale Network
TL;DR: Wang 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.
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.