Delineation of cultivated land parcels based on deep convolutional networks and geographical thematic scene division of remotely sensed images

2022 ◽  
Vol 192 ◽  
pp. 106611
Author(s):  
Lu Xu ◽  
Dongping Ming ◽  
Tongyao Du ◽  
Yangyang Chen ◽  
Dehui Dong ◽  
...  
Keyword(s):  
Remotely Sensed ◽  
Cultivated Land ◽  
Convolutional Networks ◽  
Remotely Sensed Images

scholarly journals Deep Residual Autoencoder with Multiscaling for Semantic Segmentation of Land-Use Images

2019 ◽  
Vol 11 (18) ◽  
pp. 2142 ◽  
Author(s):  
Lianfa Li
Keyword(s):  
Land Use ◽  
Deep Learning ◽  
Semantic Segmentation ◽  
Remotely Sensed ◽  
Convolutional Network ◽  
Convolutional Networks ◽  
Residual Learning ◽  
Fully Convolutional Networks ◽  
Remotely Sensed Images ◽  
Real World Datasets

Semantic segmentation is a fundamental means of extracting information from remotely sensed images at the pixel level. Deep learning has enabled considerable improvements in efficiency and accuracy of semantic segmentation of general images. Typical models range from benchmarks such as fully convolutional networks, U-Net, Micro-Net, and dilated residual networks to the more recently developed DeepLab 3+. However, many of these models were originally developed for segmentation of general or medical images and videos, and are not directly relevant to remotely sensed images. The studies of deep learning for semantic segmentation of remotely sensed images are limited. This paper presents a novel flexible autoencoder-based architecture of deep learning that makes extensive use of residual learning and multiscaling for robust semantic segmentation of remotely sensed land-use images. In this architecture, a deep residual autoencoder is generalized to a fully convolutional network in which residual connections are implemented within and between all encoding and decoding layers. Compared with the concatenated shortcuts in U-Net, these residual connections reduce the number of trainable parameters and improve the learning efficiency by enabling extensive backpropagation of errors. In addition, resizing or atrous spatial pyramid pooling (ASPP) can be leveraged to capture multiscale information from the input images to enhance the robustness to scale variations. The residual learning and multiscaling strategies improve the trained model’s generalizability, as demonstrated in the semantic segmentation of land-use types in two real-world datasets of remotely sensed images. Compared with U-Net, the proposed method improves the Jaccard index (JI) or the mean intersection over union (MIoU) by 4-11% in the training phase and by 3-9% in the validation and testing phases. With its flexible deep learning architecture, the proposed approach can be easily applied for and transferred to semantic segmentation of land-use variables and other surface variables of remotely sensed images.

A Siamese convolutional neural network with high–low level feature fusion for change detection in remotely sensed images

2021 ◽  
Vol 12 (4) ◽  
pp. 387-396
Author(s):  
Hao Zhou ◽  
Mi Zhang ◽  
Xiangyun Hu ◽  
Kun Li ◽  
Jing Sun
Keyword(s):  
Neural Network ◽  
Change Detection ◽  
Convolutional Neural Network ◽  
Feature Fusion ◽  
Remotely Sensed ◽  
Low Level ◽  
Remotely Sensed Images

scholarly journals MACU-Net for Semantic Segmentation of Fine-Resolution Remotely Sensed Images

2021 ◽  
pp. 1-5
Author(s):  
Rui Li ◽  
Chenxi Duan ◽  
Shunyi Zheng ◽  
Ce Zhang ◽  
Peter M. Atkinson
Keyword(s):  
Semantic Segmentation ◽  
Remotely Sensed ◽  
Remotely Sensed Images ◽  
Fine Resolution

scholarly journals Super-resolution target identification from remotely sensed images using a Hopfield neural network

2001 ◽  
Vol 39 (4) ◽  
pp. 781-796 ◽  
Author(s):  
A.J. Tatem ◽  
H.G. Lewis ◽  
P.M. Atkinson ◽  
M.S. Nixon
Keyword(s):  
Neural Network ◽  
Target Identification ◽  
Super Resolution ◽  
Hopfield Neural Network ◽  
Remotely Sensed ◽  
Remotely Sensed Images

scholarly journals A Model-Based Approach to Multiresolution Fusion in Remotely Sensed Images

2006 ◽  
Vol 44 (9) ◽  
pp. 2549-2562 ◽  
Author(s):  
M.V. Joshi ◽  
L. Bruzzone ◽  
S. Chaudhuri
Keyword(s):  
Remotely Sensed ◽  
Model Based ◽  
Remotely Sensed Images

Cloud removal for remotely sensed images by similar pixel replacement guided with a spatio-temporal MRF model

2014 ◽  
Vol 92 ◽  
pp. 54-68 ◽  
Author(s):  
Qing Cheng ◽  
Huanfeng Shen ◽  
Liangpei Zhang ◽  
Qiangqiang Yuan ◽  
Chao Zeng
Keyword(s):  
Remotely Sensed ◽  
Remotely Sensed Images ◽  
Spatio Temporal ◽  
Cloud Removal

Performance evaluation of state-of-the-art CNN architectures for the on-board processing of remotely sensed images

2021 ◽  
Author(s):  
Romen Neris ◽  
Rael Guerra ◽  
Sebastian Lopez ◽  
Roberto Sarmiento
Keyword(s):  
Performance Evaluation ◽  
State Of The Art ◽  
Remotely Sensed ◽  
Remotely Sensed Images

A novel approach for predicting the tc center of remotely sensed images using pso based density matrix

2021 ◽  
Author(s):  
S. Mohammad Malik Mubeen ◽  
M. Shanmuga Priya ◽  
M. Vijayaraj
Keyword(s):  
Density Matrix ◽  
Remotely Sensed ◽  
Novel Approach ◽  
Remotely Sensed Images
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