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Logistic Regression A Self Learning Text

https://www.geo.uzh.ch/microsite/rsl-documents/research/publications/peer-reviewed-articles/2014/RSE158_APEX_Schaepman_2015-1746441218/RSE158_APEX_Schaepman_2015.pdf

Advanced radiometry measurements and Earth science applications with the Airborne Prism Experiment (APEX)

https://scholarworks.boisestate.edu/cgi/viewcontent.cgi?article=1579&context=geo_facpubs

NASA's surface biology and geology designated observable: A perspective on surface imaging algorithms

Active multi-kernel domain adaptation for hyperspectral image classification

Remote sensing of long-term vegetation monitoring relies on the analysis of multisensor and multitemporal time-series measurements. Cross-sensor calibration is therefore important to prevent artifacts in the temporal signal due to inherent differences in sensors configurations. Variations in spectral response functions (SRFs) are among the major causes of differences in multisensor reflectances and products. In this paper, we report on the SRF comparability of the upcoming Sentinel-2 Multispectral Instrument (MSI) sensor with a number of operational sensors National Oceanic and Atmospheric Administration (NOAA)/ Advanced Very High Resolution Radiometer (AVHRR9), Landsat 7 Enhanced Thematic Mapper Plus (ETM+), Satellite Pour l'Observation de la Terre VEGETATION1 (VGT1), Moderate Resolution Imaging Spectroradiometer (MODIS), and Medium Resolution Imaging Spectrometer (MERIS) relevant for vegetation monitoring. SRF cross-sensor calibration methods for the conversion of red and near infrared (NIR) reflectances and Normalized Difference Vegetation Index (NDVI) values of operational sensors in reference to the Sentinel-2 MSI sensor were evaluated. Calibration data sets obtained using the soil-leaf-canopy radiative transfer model; a state-of-the-art airborne imaging spectrometer Airborne Prism Experiment (APEX); and univariate and multivariate regression models were considered for SRF cross-sensor calibration. For AVHRR9 and VGT1, reflectances in the red spectral region differed more than 30% from Sentinel-2 reflectances. These differences translated in NDVI deviations of up to 10%. The developed SRF cross-sensor calibration method reduced the differences by factors up to 6, 3, and 7 for red, NIR, and NDVI values, respectively. All but AVHRR9 have been found to be cross-calibrated to within 5% differences for reflectances and NDVI values. The present work is considered as part of a broader harmonization effort aimed at preparing for the integration of Sentinel-2 MSI data with existing historical data records and product time series.

/pdf/experimental-evaluation-of-sentinel-2-spectral-response-w12ohcz41m.pdf

Experimental Evaluation of Sentinel-2 Spectral Response Functions for NDVI Time-Series Continuity

Hyperspectral radiance spectra are the sensor's response, through its spectral response functions (SRFs), to the at-sensor radiance field. As the SRFs vary with sensors, hyperspectral radiance data need to be transformed for cross-calibration with another sensor or data simulation of a future sensor. In fact, the hyperspectral radiance data are composed of average radiance in the sensor's passbands and bear a spectral smoothing effect. Current data transformation methods do not fully consider this spectral response effect in hyperspectral data, and it restricts the accuracy of the transformation and, hence, the accuracy of the cross-calibration and data simulation. The spectral response effect is modeled as a correlation-sampling process instead of convolution sampling to fit all the hyperspectral, multispectral, and panchromatic sensors. An iterative method is proposed to get a superresolution spectrum from the hyperspectral radiance spectrum according to the response model. Data of another sensor are simulated by responding to the superresolution spectrum. Validation experiments are performed using MODTRAN4-simulated spectrum, coincident Earth Observing-1 Hyperion and Advanced Land Imager (ALI) radiance data, and nearly coincident Hyperion and Landsat-7 Enhanced Thematic Mapper Plus data. For transformations from the MODTRAN4-simulated spectrum to the hyperspectral spectrum of different spectral resolutions, the proposed transformation method achieves the least root mean square relative error (RMSRE) compared with the linear interpolation, the cubic spline interpolation, and the deconvolution-sampling method. For transformation from the Hyperion data to the ALI multispectral and panchromatic data, the RMSRE is less than 9% and 4%, respectively.

Transformation From Hyperspectral Radiance Data to Data of Other Sensors Based on Spectral Superresolution

Quantitative uncertainty analysis is generally taken as an indispensable step in the calibration of a remote sensor. A full uncertainty propagation chain has not been established to set up the metrological traceability for surface reflectance inversed from remotely sensed images. As a step toward this goal, we proposed an uncertainty analysis method for the two typical semi-empirical topographic correction models, i.e., C and Minnaert, according to the ‘Guide to the Expression of Uncertainty in Measurement (GUM)’. We studied the data link and analyzed the uncertainty propagation chain from the digital elevation model (DEM) and at-sensor radiance data to the topographic corrected radiance. We obtained spectral uncertainty characteristics of the topographic corrected radiance as well as its uncertainty components associated with all of the input quantities by using a set of Earth Observation-1 (EO-1) Hyperion data acquired over a rugged soil surface partly covered with snow. Firstly, the relative uncertainty of cover types with lower radiance values was larger for both C and Minnaert corrections. Secondly, the trend of at-sensor radiance contributed to a spectral feature, where the uncertainty of the topographic corrected radiance was poor in bands below 1400 nm. Thirdly, the uncertainty components associated with at-sensor radiance, slope, and aspect dominated the total combined uncertainty of corrected radiance. It was meaningful to reduce the uncertainties of at-sensor radiance, slope, and aspect for reducing the uncertainty of corrected radiance and improving the data quality. We also gave some suggestions to reduce the uncertainty of slope and aspect data.

https://www.zora.uzh.ch/id/eprint/190499/1/remotesensing-12-00705.pdf

Uncertainty analysis for topographic correction of hyperspectral remote sensing images

Sensor simulators can be used in forecasting the imaging quality of a new hyperspectral imaging spectrometer, and generating simulated data for the development and validation of the data processing algorithms This paper presents a novel digital sensor simulator for the pushbroom Offner hyperspectral imaging spectrometer, which is widely used in the hyperspectral remote sensing Based on the imaging process, the sensor simulator consists of a spatial response module, a spectral response module, and a radiometric response module In order to enhance the simulation accuracy, spatial interpolation-resampling, which is implemented before the spatial degradation, is developed to compromise the direction error and the extra aliasing effect Instead of using the spectral response function (SRF), the dispersive imaging characteristics of the Offner convex grating optical system is accurately modeled by its configuration parameters The non-uniformity characteristics, such as keystone and smile effects, are simulated in the corresponding modules In this work, the spatial, spectral and radiometric calibration processes are simulated to provide the parameters of modulation transfer function (MTF), SRF and radiometric calibration parameters of the sensor simulator Some uncertainty factors (the stability, band width of the monochromator for the spectral calibration, and the integrating sphere uncertainty for the radiometric calibration) are considered in the simulation of the calibration process With the calibration parameters, several experiments were designed to validate the spatial, spectral and radiometric response of the sensor simulator, respectively The experiment results indicate that the sensor simulator is valid

https://www.mdpi.com/1424-8220/14/12/23822/pdf

A Digital Sensor Simulator of the Pushbroom Offner Hyperspectral Imaging Spectrometer

A novel logistic multi-class supervised classification model based on multi-fractal spectrum parameters is proposed to avoid the error that is caused by the difference between the real data distribution and the hypothetic Gaussian distribution and avoid the computational burden working in the logistic regression classification directly for hyperspectral data. The multi-fractal spectra and parameters are calculated firstly with training samples along the spectral dimension of hyperspectral data. Secondly, the logistic regression model is employed in our work because the logistic regression classification model is a distribution-free nonlinear model which is based on the conditional probability without the Gaussian distribution assumption of the random variables, and the obtained multi-fractal parameters are applied to establish the multi-class logistic regression classification model. Finally, the Newton–Raphson method is applied to estimate the model parameters via the maximum likelihood algorithm. The cl...

A novel logistic multi-class supervised classification model based on multi-fractal spectrum parameters for hyperspectral data

The acousto-optic tunable filter (AOTF) with wide wavelength range and high spectral resolution has long crystal and two transducers. A longer crystal length leads to a bigger chromatic focal shift and the double-transducer arrangement induces angular mutation in diffracted beam, which increase difficulty in longitudinal and lateral chromatic aberration correction respectively. In this study, the two chromatic aberrations are analyzed quantitatively based on an AOTF optical model and a novel catadioptric dual-path configuration is proposed to correct both the chromatic aberrations. The test results exhibit effectiveness of the optical configuration for this type of AOTF-based imaging spectrometer.

Guorui Jia

Papers

Transformation From Hyperspectral Radiance Data to Data of Other Sensors Based on Spectral Superresolution

Uncertainty analysis for topographic correction of hyperspectral remote sensing images

A Digital Sensor Simulator of the Pushbroom Offner Hyperspectral Imaging Spectrometer

A novel logistic multi-class supervised classification model based on multi-fractal spectrum parameters for hyperspectral data

Chromatic aberrations correction for imaging spectrometer based on acousto-optic tunable filter with two transducers