K-sharp: A segmented regression approach for image sharpening and normalization

[1]  Marcel M. El Hajj,et al.  The radiometric accuracy of the 8-band multi-spectral surface reflectance from the planet SuperDove constellation , 2022, Int. J. Appl. Earth Obs. Geoinformation.

[2]  A. Mohammadzadeh,et al.  Tensor-based keypoint detection and switching regression model for relative radiometric normalization of bitemporal multispectral images , 2022, International Journal of Remote Sensing.

[3]  M. Mccabe,et al.  Revisiting the Spatial Scale Effects on Remotely Sensed Evaporation , 2021, 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS.

[4]  Roberta E. Martin,et al.  NASA's surface biology and geology designated observable: A perspective on surface imaging algorithms , 2021 .

[5]  Philip A. Townsend,et al.  LOESS radiometric correction for contiguous scenes (LORACCS): Improving the consistency of radiometry in high-resolution satellite image mosaics , 2021, Int. J. Appl. Earth Obs. Geoinformation.

[6]  Yufang Jin,et al.  Deep Learning for Feature-Level Data Fusion: Higher Resolution Reconstruction of Historical Landsat Archive , 2021, Remote. Sens..

[7]  Weisheng Li,et al.  DMNet: A Network Architecture Using Dilated Convolution and Multiscale Mechanisms for Spatiotemporal Fusion of Remote Sensing Images , 2020, IEEE Sensors Journal.

[8]  Philippe Lejeune,et al.  PlanetScope Radiometric Normalization and Sentinel-2 Super-Resolution (2.5 m): A Straightforward Spectral-Spatial Fusion of Multi-Satellite Multi-Sensor Images Using Residual Convolutional Neural Networks , 2020, Remote. Sens..

[9]  Yones Khaledian,et al.  Selecting appropriate machine learning methods for digital soil mapping , 2020, Applied Mathematical Modelling.

[10]  Le Sun,et al.  Spatiotemporal Fusion of Satellite Images via Very Deep Convolutional Networks , 2019, Remote. Sens..

[11]  Nicholas Leach,et al.  Normalization method for multi-sensor high spatial and temporal resolution satellite imagery with radiometric inconsistencies , 2019, Comput. Electron. Agric..

[12]  Peifeng Ma,et al.  Sentinel-2A Image Fusion Using a Machine Learning Approach , 2019, IEEE Transactions on Geoscience and Remote Sensing.

[13]  David Frantz,et al.  FORCE - Landsat + Sentinel-2 Analysis Ready Data and Beyond , 2019, Remote. Sens..

[14]  P. Strobl,et al.  Benefits of the free and open Landsat data policy , 2019, Remote Sensing of Environment.

[15]  Radoslaw Guzinski,et al.  Evaluating the feasibility of using Sentinel-2 and Sentinel-3 satellites for high-resolution evapotranspiration estimations , 2019, Remote Sensing of Environment.

[16]  C. Justice,et al.  The Harmonized Landsat and Sentinel-2 surface reflectance data set , 2018, Remote Sensing of Environment.

[17]  Katharina Fricke,et al.  Thermal sharpening of Landsat-8 TIRS surface temperatures for inland water bodies based on different VNIR land cover classifications , 2018, Remote Sensing.

[18]  Claudia Notarnicola,et al.  High Spatio- Temporal Resolution Land Surface Temperature Mission - a Copernicus Candidate Mission in Support of Agricultural Monitoring , 2018, IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium.

[19]  Gérard Dedieu,et al.  The Indian-French Trishna Mission: Earth Observation in the Thermal Infrared with High Spatio-Temporal Resolution , 2018, IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium.

[20]  M. Krzywinski,et al.  The curse(s) of dimensionality , 2018, Nature Methods.

[21]  R. Houborg,et al.  A Cubesat enabled Spatio-Temporal Enhancement Method (CESTEM) utilizing Planet, Landsat and MODIS data , 2018 .

[22]  Xiaolin Zhu,et al.  Spatiotemporal Fusion of Multisource Remote Sensing Data: Literature Survey, Taxonomy, Principles, Applications, and Future Directions , 2018, Remote. Sens..

[23]  Y. Blau,et al.  The Perception-Distortion Tradeoff , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[24]  Jiancheng Shi,et al.  The Future of Earth Observation in Hydrology. , 2017, Hydrology and earth system sciences.

[25]  R. Houborg,et al.  Impacts of dust aerosol and adjacency effects on the accuracy of Landsat 8 and RapidEye surface reflectances , 2017 .

[26]  Martha C. Anderson,et al.  The future of evapotranspiration: Global requirements for ecosystem functioning, carbon and climate feedbacks, agricultural management, and water resources , 2017 .

[27]  Rebecca L. Whetton,et al.  Machine learning based prediction of soil total nitrogen, organic carbon and moisture content by using VIS-NIR spectroscopy , 2016 .

[28]  Matthew F. McCabe,et al.  High-Resolution NDVI from Planet's Constellation of Earth Observing Nano-Satellites: A New Data Source for Precision Agriculture , 2016, Remote. Sens..

[29]  M. Vohland,et al.  Downscaling land surface temperatures at regional scales with random forest regression , 2016 .

[30]  Bartosz Krawczyk,et al.  Learning from imbalanced data: open challenges and future directions , 2016, Progress in Artificial Intelligence.

[31]  Mariana Belgiu,et al.  Random forest in remote sensing: A review of applications and future directions , 2016 .

[32]  Matthew F. McCabe,et al.  ECOSTRESS: NASA's Next Generation Mission to Measure Evapotranspiration From the International Space Station , 2015, Water Resources Research.

[33]  M. Krzywinski,et al.  Multiple linear regression , 2015, Nature Methods.

[34]  A. S. Belward,et al.  Who launched what, when and why; trends in global land-cover observation capacity from civilian earth observation satellites , 2015 .

[35]  A. Gitelson,et al.  Joint leaf chlorophyll content and leaf area index retrieval from Landsat data using a regularized model inversion system (REGFLEC) , 2015 .

[36]  Kenton Lee,et al.  The Spectral Response of the Landsat-8 Operational Land Imager , 2014, Remote. Sens..

[37]  C. K. Michael Tse,et al.  Data Clustering with Cluster Size Constraints Using a Modified K-Means Algorithm , 2014, 2014 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery.

[38]  N. Coops,et al.  Satellites: Make Earth observations open access , 2014, Nature.

[39]  Pasi Fränti,et al.  Balanced K-Means for Clustering , 2014, S+SSPR.

[40]  Xiaoyu Song,et al.  Exploring the Best Hyperspectral Features for LAI Estimation Using Partial Least Squares Regression , 2014, Remote. Sens..

[41]  Aaron C. Courville,et al.  Generative adversarial networks , 2014, Commun. ACM.

[42]  Matthew F. McCabe,et al.  Effects of spatial aggregation on the multi-scale estimation of evapotranspiration , 2013 .

[43]  Onisimo Mutanga,et al.  Predicting Thaumastocoris peregrinus damage using narrow band normalized indices and hyperspectral indices using field spectra resampled to the Hyperion sensor , 2013, Int. J. Appl. Earth Obs. Geoinformation.

[44]  Feng Gao,et al.  A Data Mining Approach for Sharpening Thermal Satellite Imagery over Land , 2012, Remote. Sens..

[45]  David Krejci,et al.  A survey and assessment of the capabilities of Cubesats for Earth observation , 2012 .

[46]  Jungho Im,et al.  Support vector machines in remote sensing: A review , 2011 .

[47]  Henry R. Hertzfeld,et al.  Cubesats: Cost-effective science and technology platforms for emerging and developing nations , 2011 .

[48]  N. Pettorelli,et al.  The Normalized Difference Vegetation Index (NDVI): unforeseen successes in animal ecology , 2011 .

[49]  Joanne C. White,et al.  Generation of dense time series synthetic Landsat data through data blending with MODIS using a spatial and temporal adaptive reflectance fusion model. , 2009 .

[50]  Alan C. Bovik,et al.  Mean squared error: Love it or leave it? A new look at Signal Fidelity Measures , 2009, IEEE Signal Processing Magazine.

[51]  James Theiler,et al.  Linear mixing in thermal infrared temperature retrieval , 2008 .

[52]  D. Roy,et al.  Multi-temporal MODIS-Landsat data fusion for relative radiometric normalization, gap filling, and prediction of Landsat data , 2008 .

[53]  Wenzhong Shi,et al.  Remote Sensing Image Fusion Using Multiscale Mapped LS-SVM , 2008, IEEE Transactions on Geoscience and Remote Sensing.

[54]  Bunkei Matsushita,et al.  Sensitivity of the Enhanced Vegetation Index (EVI) and Normalized Difference Vegetation Index (NDVI) to Topographic Effects: A Case Study in High-Density Cypress Forest , 2007, Sensors.

[55]  R. O’Brien,et al.  A Caution Regarding Rules of Thumb for Variance Inflation Factors , 2007 .

[56]  E. Vermote,et al.  Validation of a vector version of the 6S radiative transfer code for atmospheric correction of satellite data. Part I: path radiance. , 2006, Applied optics.

[57]  Pierre Geurts,et al.  Extremely randomized trees , 2006, Machine Learning.

[58]  In-Beum Lee,et al.  A novel multivariate regression approach based on kernel partial least squares with orthogonal signal correction , 2005 .

[59]  Qingquan Li,et al.  A comparative analysis of image fusion methods , 2005, IEEE Transactions on Geoscience and Remote Sensing.

[60]  John R. Miller,et al.  Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: Modeling and validation in the context of precision agriculture , 2004 .

[61]  Eero P. Simoncelli,et al.  Image quality assessment: from error visibility to structural similarity , 2004, IEEE Transactions on Image Processing.

[62]  J. Schjoerring,et al.  Reflectance measurement of canopy biomass and nitrogen status in wheat crops using normalized difference vegetation indices and partial least squares regression , 2003 .

[63]  Eric R. Ziegel,et al.  The Elements of Statistical Learning , 2003, Technometrics.

[64]  Elaine B. Martin,et al.  Model selection for partial least squares regression , 2002 .

[65]  A. Bovik,et al.  A universal image quality index , 2002, IEEE Signal Processing Letters.

[66]  Abderrazak Bannari,et al.  Transformed difference vegetation index (TDVI) for vegetation cover mapping , 2002, IEEE International Geoscience and Remote Sensing Symposium.

[67]  S. Wold,et al.  PLS-regression: a basic tool of chemometrics , 2001 .

[68]  L. Breiman.  Random Forests , 2001, Encyclopedia of Machine Learning and Data Mining.

[69]  S. Liang.  Narrowband to broadband conversions of land surface albedo I Algorithms , 2001 .

[70]  Ayhan Demiriz,et al.  Constrained K-Means Clustering , 2000 .

[71]  Tin Kam Ho,et al.  The Random Subspace Method for Constructing Decision Forests , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[72]  S. K. McFeeters.  The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features , 1996 .

[73]  J. Ross Quinlan,et al.  Improved Use of Continuous Attributes in C4.5 , 1996, J. Artif. Intell. Res..

[74]  G. Rondeaux,et al.  Optimization of soil-adjusted vegetation indices , 1996 .

[75]  Ranga B. Myneni,et al.  The interpretation of spectral vegetation indexes , 1995, IEEE Transactions on Geoscience and Remote Sensing.

[76]  Christopher B. Field,et al.  Reflectance indices associated with physiological changes in nitrogen- and water-limited sunflower leaves☆ , 1994 .

[77]  A. Huete,et al.  A Modified Soil Adjusted Vegetation Index , 1994 .

[78]  R. Cramer.  Partial Least Squares (PLS): Its strengths and limitations , 1993 .

[79]  Charlotte H. Mason,et al.  Collinearity, Power, and Interpretation of Multiple Regression Analysis , 1991 .

[80]  S. Wold,et al.  Nonlinear PLS modeling , 1989 .

[81]  G. Stewart.  Collinearity and Least Squares Regression , 1987 .

[82]  S. P. Lloyd,et al.  Least squares quantization in PCM , 1982, IEEE Trans. Inf. Theory.

[83]  Turgay Celik,et al.  Distortion Robust Relative Radiometric Normalization of Multitemporal and Multisensor Remote Sensing Images Using Image Features , 2022, IEEE Transactions on Geoscience and Remote Sensing.

[84]  Trevor Hastie,et al.  An Introduction to Statistical Learning , 2013, Springer Texts in Statistics.

[85]  Alessandro Golkar,et al.  CubeSat evolution: Analyzing CubeSat capabilities for conducting science missions , 2017 .

[86]  Rainer Sandau,et al.  Status and trends of small satellite missions for Earth observation , 2010 .

[87]  Prashanth H.S.,et al.  Image Scaling Comparison Using Universal Image Quality Index , 2009, 2009 International Conference on Advances in Computing, Control, and Telecommunication Technologies.

[88]  Nitesh V. Chawla,et al.  SMOTE: Synthetic Minority Over-sampling Technique , 2002, J. Artif. Intell. Res..

[89]  Xiaojun Yang,et al.  Relative Radiometric Normalization Performance for Change Detection from Multi-Date Satellite Images , 2000 .

[90]  M. Stone.  Continuum regression: Cross-validated sequentially constructed prediction embracing ordinary least s , 1990 .