22.2.2.1 Land Cover, Land Use, Super-Resolution Techniques

Chapter Contents (Back)
Super Resolution. Not high resolution input data, apply super-resolution techniques to mapping.
See also Subpixel Target, Subpixel Land Use, Tiny Objects.
See also Super Resolution for Remote Sensing Applications.
See also Super Resolution or Superresolution, General.
See also Land Cover, Land Use, Very High Resolution, High Spatial Resolution.

Tatem, A.J., Lewis, H.G., Atkinson, P.M., Nixon, M.S.,
Super-resolution land cover pattern prediction using a Hopfield neural network,
RSE(79), No. 1, January 2002, pp. 1-14.
HTML Version. 0201
BibRef

Boucher, A.[Alexandre], Kyriakidis, P.C.[Phaedon C.],
Integrating Fine Scale Information in Super-resolution Land-cover Mapping,
PhEngRS(73), No. 8, August 2007, pp. 913-922.
WWW Link. 0709
Accounting for additional fine spatial resolution information can lead to super-resolution maps with more realistic spatial patterns. BibRef

Boucher, A.[Alexandre], Kyriakidis, P.C.[Phaedon C.], Cronkite-Ratcliff, C.,
Geostatistical Solutions for Super-Resolution Land Cover Mapping,
GeoRS(46), No. 1, January 2008, pp. 272-283.
IEEE DOI 0712
BibRef

Hong, D.F.[Dan-Feng], Yokoya, N.[Naoto], Ge, N.[Nan], Chanussot, J.[Jocelyn], Zhu, X.X.[Xiao Xiang],
Learnable manifold alignment (LeMA): A semi-supervised cross-modality learning framework for land cover and land use classification,
PandRS(147), 2019, pp. 193-205.
Elsevier DOI 1901
Cross-modality, Graph learning, Hyperspectral, Manifold alignment, Multispectral, Remote sensing, Semi-supervised learning BibRef

Dong, X.Y.[Xiao-Yu], Yokoya, N.[Naoto], Wang, L.G.[Long-Guang], Uezato, T.[Tatsumi],
Learning Mutual Modulation for Self-supervised Cross-Modal Super-Resolution,
ECCV22(XIX:1-18).
Springer DOI 2211
BibRef

Nogueira, K.[Keiller], Dalla Mura, M., Chanussot, J.[Jocelyn], Schwartz, W.R., dos Santos, J.A.[Jefersson A.],
Learning to Semantically Segment High-Resolution Remote Sensing Images,
ICPR16(3566-3571)
IEEE DOI 1705
Context, Feature extraction, Image segmentation, Machine learning, Remote sensing, Semantics, Visualization, Deep Learning, Feature Learning, High-resolution Images, Land-cover Mapping, Pixel-wise Classification, Remote Sensing, Semantic, Segmentation BibRef

Luo, B.[Bin], Chanussot, J.[Jocelyn],
Geometrical features for the classification of very high resolution multispectral remote-sensing images,
ICIP10(1045-1048).
IEEE DOI 1009
BibRef

Yang, X.[Xuan], Li, S.S.[Shan-Shan], Chen, Z.C.[Zheng-Chao], Chanussot, J.[Jocelyn], Jia, X.P.[Xiu-Ping], Zhang, B.[Bing], Li, B.P.[Bai-Peng], Chen, P.[Pan],
An attention-fused network for semantic segmentation of very-high-resolution remote sensing imagery,
PandRS(177), 2021, pp. 238-262.
Elsevier DOI 2106
Semantic segmentation, Deep learning, Very-high-resolution imagery, Attention-fused network, ISPRS, Convolutional neural network BibRef

Zhang, Y.H.[Yi-Hang], Atkinson, P.M.[Peter M.], Li, X.D.[Xiao-Dong], Ling, F.[Feng], Wang, Q.M.[Qun-Ming], Du, Y.[Yun],
Learning-Based Spatial-Temporal Superresolution Mapping of Forest Cover With MODIS Images,
GeoRS(55), No. 1, January 2017, pp. 600-614.
IEEE DOI 1701
vegetation mapping BibRef

Ling, F.[Feng], Zhang, Y.H.[Yi-Hang], Foody, G.M.[Giles M.], Li, X.D.[Xiao-Dong], Zhang, X.H.[Xiu-Hua], Fang, S.M.[Shi-Ming], Li, W.B.[Wen-Bo], Du, Y.[Yun],
Learning-Based Superresolution Land Cover Mapping,
GeoRS(54), No. 7, July 2016, pp. 3794-3810.
IEEE DOI 1606
Algorithm design and analysis BibRef

Li, X.D.[Xiao-Dong], Ling, F.[Feng], Foody, G.M.[Giles M.], Ge, Y.[Yong], Zhang, Y.H.[Yi-Hang], Wang, L.H.[Li-Hui], Shi, L.F.[Ling-Fei], Li, X.Y.[Xin-Yan], Du, Y.[Yun],
Spatial-Temporal Super-Resolution Land Cover Mapping With a Local Spatial-Temporal Dependence Model,
GeoRS(57), No. 7, July 2019, pp. 4951-4966.
IEEE DOI 1907
Spatial resolution, Remote sensing, Adaptation models, Graphical models, Distribution functions, Forestry, Image series, temporal dependence
See also Impervious Surface Change Mapping with an Uncertainty-Based Spatial-Temporal Consistency Model: A Case Study in Wuhan City Using Landsat Time-Series Datasets from 1987 to 2016. BibRef

Chen, Y.H.[Yue-Hong], Zhou, Y.[Ya'nan], Ge, Y.[Yong], An, R.[Ru], Chen, Y.[Yu],
Enhancing Land Cover Mapping through Integration of Pixel-Based and Object-Based Classifications from Remotely Sensed Imagery,
RS(10), No. 1, 2018, pp. xx-yy.
DOI Link 1802

See also Super-Resolution Mapping of Impervious Surfaces from Remotely Sensed Imagery with Points-of-Interest. BibRef

Chen, Y.H.[Yue-Hong], Ge, Y.[Yong], Heuvelink, G.B.M.[Gerard B.M.], An, R.[Ru], Chen, Y.[Yu],
Object-Based Superresolution Land-Cover Mapping From Remotely Sensed Imagery,
GeoRS(56), No. 1, January 2018, pp. 328-340.
IEEE DOI 1801
geophysical image processing, image classification, land cover, terrain mapping, advanced object-based classification, superresolution mapping (SRM) BibRef

Jia, Y.I.[Yuan-In], Ge, Y.[Yong], Chen, Y.H.[Yue-Hong], Li, S.P.[San-Ping], Heuvelink, G.B.M.[Gerard B.M.], Ling, F.[Feng],
Super-Resolution Land Cover Mapping Based on the Convolutional Neural Network,
RS(11), No. 15, 2019, pp. xx-yy.
DOI Link 1908
BibRef

Li, X.D.[Xiao-Dong], Ling, F.[Feng], Du, Y.[Yun], Feng, Q.[Qi], Zhang, Y.H.[Yi-Hang],
A spatial-temporal Hopfield neural network approach for super-resolution land cover mapping with multi-temporal different resolution remotely sensed images,
PandRS(93), No. 1, 2014, pp. 76-87.
Elsevier DOI 1407
Land cover BibRef

Li, X.D.[Xiao-Dong], Ling, F.[Feng], Foody, G.M., Du, Y.[Yun],
A Superresolution Land-Cover Change Detection Method Using Remotely Sensed Images With Different Spatial Resolutions,
GeoRS(54), No. 7, July 2016, pp. 3822-3841.
IEEE DOI 1606
Earth BibRef

Ling, F., Foody, G.M., Ge, Y., Li, X.D.[Xiao-Dong], Du, Y.,
An Iterative Interpolation Deconvolution Algorithm for Superresolution Land Cover Mapping,
GeoRS(54), No. 12, December 2016, pp. 7210-7222.
IEEE DOI 1612
geophysical image processing BibRef

Ling, F.[Feng], Foody, G.M.[Giles M.], Li, X.D.[Xiao-Dong], Zhang, Y.H.[Yi-Hang], Du, Y.[Yun],
Assessing a Temporal Change Strategy for Sub-Pixel Land Cover Change Mapping from Multi-Scale Remote Sensing Imagery,
RS(8), No. 8, 2016, pp. 642.
DOI Link 1609
BibRef

Ling, F.[Feng], Du, Y.[Yun], Li, X.D.[Xiao-Dong], Zhang, Y.H.[Yi-Hang], Xiao, F.[Fei], Fang, S.M.[Shi-Ming], Li, W.B.[Wen-Bo],
Superresolution Land Cover Mapping With Multiscale Information by Fusing Local Smoothness Prior and Downscaled Coarse Fractions,
GeoRS(52), No. 9, Sept 2014, pp. 5677-5692.
IEEE DOI 1407
land cover BibRef


Yu, Q., Liu, W., Li, J.,
Spatial Resolution Enhancement of Land Cover Mapping Using Deep Convolutional Nets,
ISPRS20(B1:85-89).
DOI Link 2012
BibRef

Chapter on Remote Sensing General Issue, Land Use, Land Cover continues in
Object Based Land Cover, Parcels, Region Based Land Cover, Land Use Analysis .


Last update:Jan 30, 2024 at 20:33:16