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
More filters
Journal ArticleDOI
Deep Learning-Based Semantic Segmentation of Urban Features in Satellite Images: A Review and Meta-Analysis
TL;DR: In this paper, a review and meta-analysis of deep learning-based semantic segmentation in urban remote sensing images is presented. But, the focus of this paper is on urban sensing images.
Journal ArticleDOI
RAANet: A Residual ASPP with Attention Framework for Semantic Segmentation of High-Resolution Remote Sensing Images
TL;DR: In this article , an improved deep learning model named RAANet (Residual ASPP with Attention Net) is constructed, which constructed a new residual ASPP by embedding the attention module and residual structure into the ASPP.
Journal ArticleDOI
Vectorized rooftop area data for 90 cities in China
Zhixin Zhang,Zhen Qian,Teng Zhong,Min Chen,Kai Zhang,Yue Yang,Rui Zhu,Fan Zhang,Haoran Zhang,Fangzhuo Zhou,Jianing Yu,Bing Zhong Zhang,Guonian Lü,Jinyue Yan +13 more
TL;DR: In this article , a geospatial artificial intelligence framework is presented to obtain data for rooftops using high-resolution open-access remote sensing imagery, which can be used for data support and decision-making to facilitate sustainable urban development effectively.
Journal ArticleDOI
An evidential classifier based on Dempster-Shafer theory and deep learning
TL;DR: In this article, a new classifier based on Dempster-Shafer (DS) theory and a convolutional neural network (CNN) architecture for set-valued classification is proposed.
Journal ArticleDOI
Land-Use Mapping for High-Spatial Resolution Remote Sensing Image Via Deep Learning: A Review
TL;DR: In this article, the authors provide a thorough review of recent achievements in the field of land-use mapping using deep learning (DL) algorithms, which offer novel opportunities for the development of LUM for HSR-RSIs.
References
More filters
Journal ArticleDOI
A patch-based convolutional neural network for remote sensing image classification.
TL;DR: Considering the spatial relation of a pixel to its neighborhood, a new deep patch-based CNN system tailored for medium-resolution remote sensing data is proposed and it is believed that much more accurate land cover datasets can be produced over large areas.
Proceedings ArticleDOI
Dense Semantic Labeling of Very-High-Resolution Aerial Imagery and LiDAR with Fully-Convolutional Neural Networks and Higher-Order CRFs
TL;DR: This paper proposes a decision-level fusion approach using a probabilistic graphical model for the task of dense semantic labeling of airborne remote sensing imagery and shows that the proposed approach compares favorably to the state-of-the-art baseline methods.
Journal ArticleDOI
Supervised Classification of Multisensor Remotely Sensed Images Using a Deep Learning Framework
TL;DR: A convolutional neural network (CNN)-based method to efficiently combine information from multisensor remotely sensed images for pixel-wise semantic classification and a composite fusion architecture that fuses features throughout the network are presented.
Journal ArticleDOI
A multi-level context-guided classification method with object-based convolutional neural network for land cover classification using very high resolution remote sensing images
TL;DR: A feature-fusing OCNN, including the object contour-preserving mask strategy with the supplement of object deformation coefficient, is developed for accurate object discrimination by learning simultaneously high-level features from independent spectral patterns, geometric characteristics, and object-level contextual information.
Journal ArticleDOI
Recurrent Multiresolution Convolutional Networks for VHR Image Classification
TL;DR: In this paper, a single-stage framework embedding the processing stages in a recurrent multiresolution convolutional network trained in an end-to-end manner is proposed to match the resolution of the panchromatic and multispectral bands.