18.4.3.18 Super Resolution for Remote Sensing Applications

Chapter Contents (Back)
Super Resolution. Restoration. Remote Sensing.
See also Super Resolution for Hyperspectral Data.
See also Image and Sensor Fusion for Cartography and Aerial Images, Satellite Images, Remote Sensing.
See also Super Resolution for Sentinel Sensors.

Scambos, T.A., Kvaran, G., Fahnestock, M.A.,
Improving AVHRR Resolution Through Data Cumulation for Mapping Polar Ice Sheets,
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Tatem, A.J., Lewis, H.G., Atkinson, P.M., Nixon, M.S.,
Super-resolution target identification from remotely sensed images using a Hopfield neural network,
GeoRS(39), No. 4, April 2001, pp. 781-796.
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See also Predicting Missing Field Boundaries to Increase Per-Field Classification Accuracy. BibRef

Wang, Q.M.[Qun-Ming], Shi, W.Z.[Wen-Zhong], Atkinson, P.M.[Peter M.],
Sub-pixel mapping of remote sensing images based on radial basis function interpolation,
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Elsevier DOI 1407
Downscaling BibRef

Wang, Q.M.[Qun-Ming], Atkinson, P.M., Shi, W.Z.[Wen-Zhong],
Indicator Cokriging-Based Subpixel Mapping Without Prior Spatial Structure Information,
GeoRS(53), No. 1, January 2015, pp. 309-323.
IEEE DOI 1410
deconvolution BibRef

Wang, Q.M.[Qun-Ming], Shi, W.Z.[Wen-Zhong], Atkinson, P.M.,
Spatiotemporal Subpixel Mapping of Time-Series Images,
GeoRS(54), No. 9, September 2016, pp. 5397-5411.
IEEE DOI 1609
feature extraction
See also Allocating Classes for Soft-Then-Hard Subpixel Mapping Algorithms in Units of Class. BibRef

Wang, Q.M.[Qun-Ming], Atkinson, P.M., Shi, W.Z.[Wen-Zhong],
Fast Subpixel Mapping Algorithms for Subpixel Resolution Change Detection,
GeoRS(53), No. 4, April 2015, pp. 1692-1706.
IEEE DOI 1502
geophysical image processing
See also Area-to-point regression kriging for pan-sharpening. BibRef

Atkinson, P.M.[Peter M.],
Sub-pixel Target Mapping from Soft-classified, Remotely Sensed Imagery,
PhEngRS(71), No. 7, July 2005, pp. 839-846. A simple and efficient pixel-swapping algorithm for increasing the spatial resolution of land-cover classification from remotely sensed imagery.
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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
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Atkinson, P.M., Pardo-Iguzquiza, E., Chica-Olmo, M.,
Downscaling Cokriging for Super-Resolution Mapping of Continua in Remotely Sensed Images,
GeoRS(46), No. 2, February 2008, pp. 573-580.
IEEE DOI 0801
BibRef

Nguyen, M.Q., Atkinson, P.M., Lewis, H.G.,
Superresolution Mapping Using a Hopfield Neural Network With Fused Images,
GeoRS(44), No. 3, March 2006, pp. 736-749.
IEEE DOI 0604
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Fryer, J.G.[John G.], McIntosh, K.[Kerry],
Enhancement of Image Resolution in Digital Photogrammetry,
PhEngRS(67), No. 6, June 2001, pp. 741-750.
WWW Link. 0108
Rigorously combine multiple low-resolution digital images into a higher resolution composite, apply to photogrammetric DEM generation. BibRef

Galbraith, A.E., Theiler, J., Thome, K.J., Ziolkowski, R.W.,
Resolution Enhancement of Multilook Imagery for the Multispectral Thermal Imager,
GeoRS(43), No. 9, September 2005, pp. 1964-1977.
IEEE DOI 0509
BibRef

Long, D.G., Spencer, M.W., Njoku, E.G.,
Spatial Resolution and Processing Tradeoffs for HYDROS: Application of Reconstruction and Resolution Enhancement Techniques,
GeoRS(43), No. 1, January 2005, pp. 3-12.
IEEE Abstract. 0501
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Akgun, T., Altunbasak, Y., Mersereau, R.M.,
Super-Resolution Reconstruction of Hyperspectral Images,
IP(14), No. 11, November 2005, pp. 1860-1875.
IEEE DOI 0510
BibRef

Merino, M.T.[Maria Teresa], Nunez, J.[Jorge],
Super-Resolution of Remotely Sensed Images With Variable-Pixel Linear Reconstruction,
GeoRS(45), No. 5, May 2007, pp. 1446-1457.
IEEE DOI 0704
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

Zhang, H.Y.[Hong-Yan], Yang, Z.Y.[Ze-Yu], Zhang, L.P.[Liang-Pei], Shen, H.F.[Huan-Feng],
Super-Resolution Reconstruction for Multi-Angle Remote Sensing Images Considering Resolution Differences,
RS(6), No. 1, 2014, pp. 637-657.
DOI Link 1402
BibRef

Haris, M., Watanabe, T., Fan, L., Widyanto, M.R., Nobuhara, H.,
Superresolution for UAV Images via Adaptive Multiple Sparse Representation and Its Application to 3-D Reconstruction,
GeoRS(55), No. 7, July 2017, pp. 4047-4058.
IEEE DOI 1706
Dictionaries, Image edge detection, Image resolution, Imaging, Monitoring, Training, Unmanned aerial vehicles, 3-D images, aerial image, agriculture, monitoring, phenotyping, sparse representation, superresolution (SR), unmanned, aerial, vehicle, (UAV) BibRef

Zhang, T.[Ting], Du, Y.[Yi], Lu, F.F.[Fang-Fang],
Super-Resolution Reconstruction of Remote Sensing Images Using Multiple-Point Statistics and Isometric Mapping,
RS(9), No. 7, 2017, pp. xx-yy.
DOI Link 1708
BibRef

Chang, Y.P.[Yun-Peng], Luo, B.[Bin],
Bidirectional Convolutional LSTM Neural Network for Remote Sensing Image Super-Resolution,
RS(11), No. 20, 2019, pp. xx-yy.
DOI Link 1910
BibRef

Dong, X.Y.[Xiao-Yu], Xi, Z.H.[Zhi-Hong], Sun, X.[Xu], Gao, L.R.[Lian-Ru],
Transferred Multi-Perception Attention Networks for Remote Sensing Image Super-Resolution,
RS(11), No. 23, 2019, pp. xx-yy.
DOI Link 1912
BibRef

Dong, X.Y.[Xiao-Yu], Wang, L.G.[Long-Guang], Sun, X.[Xu], Jia, X.P.[Xiu-Ping], Gao, L.R.[Lian-Ru], Zhang, B.[Bing],
Remote Sensing Image Super-Resolution Using Second-Order Multi-Scale Networks,
GeoRS(59), No. 4, April 2021, pp. 3473-3485.
IEEE DOI 2104
Remote sensing, Image reconstruction, Spatial resolution, Convolution, Feature extraction, Task analysis, Feature reuse, super-resolution (SR) BibRef

Dong, X.Y.[Xiao-Yu], Sun, X.[Xu], Jia, X.P.[Xiu-Ping], Xi, Z.H.[Zhi-Hong], Gao, L.R.[Lian-Ru], Zhang, B.[Bing],
Remote Sensing Image Super-Resolution Using Novel Dense-Sampling Networks,
GeoRS(59), No. 2, February 2021, pp. 1618-1633.
IEEE DOI 2101
Image reconstruction, Remote sensing, Feature extraction, Spatial resolution, Convolutional neural networks, wide activation
See also Multi-Resolution Weed Classification via Convolutional Neural Network and Superpixel Based Local Binary Pattern Using Remote Sensing Images. BibRef

Chopade, P.B.[Pravin B.], Patil, P.M.[Pradeep M.],
Multiframe image superresolution based on cepstral analysis,
SIViP(13), No. 1, February 2019, pp. 199-207.
WWW Link. 1901
BibRef

Li, X.W.[Xiao-Wei], Li, L.[Lei], Wang, Q.H.[Qiong-Hua],
Wavelet-based iterative perfect reconstruction in computational integral imaging,
JOSA-A(35), No. 7, July 2018, pp. 1212-1220.
DOI Link 1912
Image processing, Image reconstruction techniques, Computational imaging, Image processing, Image quality, Signal processing BibRef

Gong, Y.F.[Yuan-Fu], Liao, P.[Puyun], Zhang, X.D.[Xiao-Dong], Zhang, L.[Lifei], Chen, G.Z.[Guan-Zhou], Zhu, K.[Kun], Tan, X.L.[Xiao-Liang], Lv, Z.Y.[Zhi-Yong],
Enlighten-GAN for Super Resolution Reconstruction in Mid-Resolution Remote Sensing Images,
RS(13), No. 6, 2021, pp. xx-yy.
DOI Link 2104
BibRef

Zhang, D.Y.[Dong-Yang], Shao, J.[Jie], Li, X.Y.[Xin-Yao], Shen, H.T.[Heng Tao],
Remote Sensing Image Super-Resolution via Mixed High-Order Attention Network,
GeoRS(59), No. 6, June 2021, pp. 5183-5196.
IEEE DOI 2106
Remote sensing, Feature extraction, Image resolution, Image restoration, Image reconstruction, Task analysis, Satellites, satellite image BibRef

Peng, Y.[Yali], Wang, X.N.[Xu-Ning], Zhang, J.W.[Jun-Wei], Liu, S.G.[Shi-Gang],
Pre-training of gated convolution neural network for remote sensing image super-resolution,
IET-IPR(15), No. 5, 2021, pp. 1179-1188.
DOI Link 2106
BibRef

Zhao, M.H.[Ming-Hua], Ning, J.W.[Jia-Wei], Hu, J.[Jing], Li, T.T.[Ting-Ting],
Hyperspectral Image Super-Resolution under the Guidance of Deep Gradient Information,
RS(13), No. 12, 2021, pp. xx-yy.
DOI Link 2106
BibRef

Huan, H.[Hai], Li, P.C.[Peng-Cheng], Zou, N.[Nan], Wang, C.[Chao], Xie, Y.Q.[Ya-Qin], Xie, Y.[Yong], Xu, D.D.[Dong-Dong],
End-to-End Super-Resolution for Remote-Sensing Images Using an Improved Multi-Scale Residual Network,
RS(13), No. 4, 2021, pp. xx-yy.
DOI Link 2103
BibRef

Huang, B.[Bo], He, B.[Boyong], Wu, L.[Liaoni], Guo, Z.M.[Zhi-Ming],
Deep Residual Dual-Attention Network for Super-Resolution Reconstruction of Remote Sensing Images,
RS(13), No. 14, 2021, pp. xx-yy.
DOI Link 2107
BibRef

Tao, Y.T.[Yi-Ting], Xu, M.Z.[Miao-Zhong], Zhong, Y.F.[Yan-Fei], Cheng, Y.F.[Yu-Feng],
GAN-Assisted Two-Stream Neural Network for High-Resolution Remote Sensing Image Classification,
RS(9), No. 12, 2017, pp. xx-yy.
DOI Link 1802
BibRef

Hu, F.[Fan], Xia, G.S.[Gui-Song], Hu, J.W.[Jing-Wen], Zhang, L.P.[Liang-Pei],
Transferring Deep Convolutional Neural Networks for the Scene Classification of High-Resolution Remote Sensing Imagery,
RS(7), No. 11, 2015, pp. 14680.
DOI Link 1512
BibRef

Tao, Y.T.[Yi-Ting], Xu, M.Z.[Miao-Zhong], Zhang, F.[Fan], Du, B.[Bo], Zhang, L.P.[Liang-Pei],
Unsupervised-Restricted Deconvolutional Neural Network for Very High Resolution Remote-Sensing Image Classification,
GeoRS(55), No. 12, December 2017, pp. 6805-6823.
IEEE DOI 1712
Use small number of labeled pixels. Data models, Deconvolution, Feature extraction, Image resolution, Remote sensing, Satellites, Training, very high resolution (VHR) image per-pixel classification BibRef

Xu, R.D.[Ru-Dong], Tao, Y.T.[Yi-Ting], Lu, Z.Y.[Zhong-Yuan], Zhong, Y.F.[Yan-Fei],
Attention-Mechanism-Containing Neural Networks for High-Resolution Remote Sensing Image Classification,
RS(10), No. 10, 2018, pp. xx-yy.
DOI Link 1811
BibRef

Ma, Y.C.[Yun-Chuan], Lv, P.Y.[Peng-Yuan], Liu, H.[Hao], Sun, X.H.[Xue-Hong], Zhong, Y.F.[Yan-Fei],
Remote Sensing Image Super-Resolution Based on Dense Channel Attention Network,
RS(13), No. 15, 2021, pp. xx-yy.
DOI Link 2108
BibRef

Yang, X.[Xin], Xie, T.X.[Tang-Xin], Guo, Y.Q.[Ying-Qing], Zhou, D.[Dake],
Remote sensing image super-resolution based on convolutional blind denoising adaptive dense connection,
IET-IPR(15), No. 11, 2021, pp. 2508-2520.
DOI Link 2108
BibRef

Zhang, L.Z.[Li-Ze], Lu, W.[Wen], Huang, Y.F.[Yuan-Fei], Sun, X.P.[Xiao-Peng], Zhang, H.Y.[Hong-Yi],
Unpaired Remote Sensing Image Super-Resolution with Multi-Stage Aggregation Networks,
RS(13), No. 16, 2021, pp. xx-yy.
DOI Link 2109
BibRef

Choi, Y.[Yeonju], Han, S.[Sanghyuck], Kim, Y.[Yongwoo],
A No-Reference CNN-Based Super-Resolution Method for KOMPSAT-3 Using Adaptive Image Quality Modification,
RS(13), No. 16, 2021, pp. xx-yy.
DOI Link 2109
BibRef

He, Z.[Zhi], He, D.[Dan],
A Unified Network for Arbitrary Scale Super-Resolution of Video Satellite Images,
GeoRS(59), No. 10, October 2021, pp. 8812-8825.
IEEE DOI 2109
Satellites, Convolution, Feature extraction, Spatial resolution, Image reconstruction, Image edge detection, Training, video satellite BibRef


Nguyen, N.L.[Ngoc Long], Anger, J.[Jérémy], Davy, A.[Axel], Arias, P.[Pablo], Facciolo, G.[Gabriele],
Self-supervised multi-image super-resolution for push-frame satellite images,
EarthVision21(1121-1131)
IEEE DOI 2109
Training, Satellites, Planets, Superresolution, Neural networks, Computer architecture, Optical imaging BibRef

Li, Y.H.[Yin-Hao], Iwamoto, Y.[Yutaro], Lin, L.F.[Lan-Fen], Chen, Y.W.[Yen-Wei],
Parallel-connected Residual Channel Attention Network for Remote Sensing Image Super-resolution,
MLCSA20(18-30).
Springer DOI 2103
BibRef

Shin, C., Kim, S., Kim, Y.,
From Planetscope To Worldview: Micro-Satellite Image Super-Resolution With Optimal Transport Distance,
ICIP20(898-902)
IEEE DOI 2011
Degradation, Remote sensing, Satellites, Histograms, Image resolution, Training, Generators, Micro-satellite image, degradation learning BibRef

Zhu, X., Talebi, H., Shi, X., Yang, F., Milanfar, P.,
Super-Resolving Commercial Satellite Imagery Using Realistic Training Data,
ICIP20(498-502)
IEEE DOI 2011
Satellites, Training data, Data models, Kernel, Spatial resolution, Degradation, Remote sensing, satellite imagery, super-resolution BibRef

Nair, P., Unni, V.S., Chaudhury, K.N.,
Hyperspectral Image Fusion Using Fast High-Dimensional Denoising,
ICIP19(3123-3127)
IEEE DOI 1910
hyperspectral image fusion, plug-and-play, regularization, high-dimensional denoiser BibRef

Bosch, M., Gifford, C.M., Rodriguez, P.A.,
Super-Resolution for Overhead Imagery Using DenseNets and Adversarial Learning,
WACV18(1414-1422)
IEEE DOI 1806
convolution, feedforward neural nets, image resolution, learning (artificial intelligence), stereo image processing, Training BibRef

Hu, W.G.[Wen-Guang], Hu, T.B.[Ting-Bo], Wu, T.[Tao], Zhang, B.[Bo], Liu, Q.[Qixu],
Sea-surface image super-resolution based on sparse representation,
IASP11(102-107).
IEEE DOI 1112
BibRef

Zomet, A.[Assaf], Peleg, S.[Shmuel],
Multi-sensor super-resolution,
WACV02(27-31).
IEEE DOI 0303
BibRef
Earlier:
Efficient Super-resolution and Applications to Mosaics,
ICPR00(Vol I: 579-583).
IEEE DOI 0009
BibRef
Earlier:
Applying Super-Resolution to Panoramic Mosaics,
WACV98(286-287).
IEEE DOI 9809
BibRef

Chapter on Motion Analysis -- Low-Level, Image Level Analysis, Mosaic Generation, Super Resolution, Shape from Motion continues in
Super Resolution for Sentinel Sensors .


Last update:Nov 1, 2021 at 09:26:50