4 research outputs found
Hyperspectral Empirical Absolute Calibration Model Using Libya 4 Pseudo-Invariant Calibration Site
The objective of this paper is to find an empirical hyperspectral absolute calibration model using Libya 4 pseudo-invariant calibration site (PICS). The approach involves using the Landsat 8 (L8) Operational Land Imager (OLI) as the reference radiometer and using Earth Observing One (EO-1) Hyperion, with a spectral resolution of 10 nm as a hyperspectral source. This model utilizes data from a region of interest (ROI) in an āoptimal regionā of 3% temporal, spatial, and spectral stability within the Libya 4 PICS. It uses an improved, simple, empirical, hyperspectral Bidirectional Reflectance Distribution function (BRDF) model accounting for four angles: solar zenith and azimuth, and view zenith and azimuth angles. This model can perform absolute calibration in 1 nm spectral resolution by predicting TOA reflectance in all existing spectral bands of the sensors. The resultant model was validated with image data acquired from satellite sensors such as Landsat 7, Sentinel 2A, and Sentinel 2B, Terra MODIS, Aqua MODIS, from their launch date to 2020. These satellite sensors differ in terms of the width of their spectral band-pass, overpass time, off-nadir viewing capabilities, spatial resolution, and temporal revisit time, etc. The result demonstrates the efficacy of the proposed model has an accuracy of the order of 3% with a precision of about 3% for the nadir viewing sensors (with view zenith angle up to 5Ā°) used in the study. For the off-nadir viewing satellites with view zenith angle up to 20Ā°, it can have an estimated accuracy of 6% and precision of 4%
Decay2Distill: Leveraging spatial perturbation and regularization for self-supervised image denoising
Unpaired image denoising has achieved promising development over the last few
years. Regardless of the performance, methods tend to heavily rely on
underlying noise properties or any assumption which is not always practical.
Alternatively, if we can ground the problem from a structural perspective
rather than noise statistics, we can achieve a more robust solution. with such
motivation, we propose a self-supervised denoising scheme that is unpaired and
relies on spatial degradation followed by a regularized refinement. Our method
shows considerable improvement over previous methods and exhibited consistent
performance over different data domains
Hyperspectral Empirical Absolute Calibration Model Using Libya 4 Pseudo Invariant Calibration Site
The objective of this paper is to find an empirical hyperspectral absolute calibration model using Libya 4 pseudo invariant calibration site (PICS). The approach involves using the Landsat 8 (L8) Operational Land Imager (OLI) as the reference radiometer and using Earth Observing One (EO-1) Hyperion, with a spectral resolution of 10 nm as a hyperspectral source. This model utilizes data from a region of interest (ROI) in an āoptimal regionā of 3% temporal, spatial, and spectral stability within the Libya 4 PICS. It uses an improved, simple, empirical, hyperspectral Bidirectional Reflectance Distribution function (BRDF) model accounting for four angles: solar zenith and azimuth, and view zenith and azimuth angles. This model can perform absolute calibration in 1 nm spectral resolution by predicting TOA reflectance in all existing spectral bands of the sensors. The resultant model was validated with image data acquired from satellite sensors such as Landsat 7, Sentinel 2A, and Sentinel 2B, Terra MODIS, Aqua MODIS, from their launch date to 2020. These satellite sensors differ in terms of the width of their spectral bandpass, overpass time, off-nadir viewing capabilities, spatial resolution, and temporal revisit time, etc. The result demonstrates the efficacy of the proposed model has an accuracy of the order of 3% with a precision of about 3% for the nadir viewing sensors (with view zenith angle up to 5Ā°) used in the study. For the off-nadir viewing satellites with view zenith angle up to 20Ā°, it can have an estimated accuracy of 6% and precision of 4%
DeepRoadNet: A deep residual based segmentation network for road map detection from remote aerial image
Abstract The extraction of road networks is a critical activity in contemporary transportation networks. Deep neural networks have recently demonstrated excellent performance in the field of road segmentation. However, most of the convolutional neural network (CNN) based architectures could not verify their effectiveness in remote sensing images due to a smaller ratio of the targeted pixels, simple design, and fewer layers. In this study, a practical approach is assessed for road segmentation. The investigation was begun with basic encoderādecoder based segmentation models. Different stateāofātheāart segmentation models like UāNet, VāNet, ResUNet and SegNet were used for road network detection experiments in this research. A robust model named DeepRoadNet, a more complicated alternative, is proposed by utilizing a preātrained EfficientNetB7 architecture in the encoder and residual blocks as the decoder which mostly resembles the UāNet segmentation process. The proposed model has been trained, validated as well as tested using the highāresolution aerial image datasets and yielded good segmentation results with a mean intersection over union (mIoU) of 76%, a mean dice coefficient (mDC) of 73.18%, and an accuracy of 97.64% using Massachusetts road dataset. The proposed DeepRoadNet architecture overcomes the issues of lower mIoU, lower mDC, limited flexibility and interpretability already faced by existing models in the road segmentation field. The code is available at https://github.com/Imteaz1998/DeepRoadNet