Open Access. Powered by Scholars. Published by Universities.®

Engineering Commons

Open Access. Powered by Scholars. Published by Universities.®

Electrical and Computer Engineering

PDF

Electronic Thesis and Dissertation Repository

2020

Remote sensing

Articles 1 - 2 of 2

Full-Text Articles in Engineering

Radiometric Correction Of Multispectral Cameras Using Photosensor Irradiance For Agronomy Applications, Nicholas S. Mitchell Dec 2020

Radiometric Correction Of Multispectral Cameras Using Photosensor Irradiance For Agronomy Applications, Nicholas S. Mitchell

Electronic Thesis and Dissertation Repository

Cloud coverage has a significant impact on the reflectance maps generated by multispectral cameras mounted on UAV's in small farm settings. The current approach is to calibrate the camera once before every flight and use the radiometric calibration map for the entire flight. In this work we have designed and built a downwelling and upwelling photosensor to work in sync with our custom multispectral camera. A dual reflectance panel implementation for the time dependent radiometric calibration of the multispectral camera is completed. The solar spectral irradiance curve is approximated using the current measurements from the downwelling photosensor and the ground …


Hyperspectral Image Classification For Remote Sensing, Hadis Madani Mar 2020

Hyperspectral Image Classification For Remote Sensing, Hadis Madani

Electronic Thesis and Dissertation Repository

This thesis is focused on deep learning-based, pixel-wise classification of hyperspectral images (HSI) in remote sensing. Although presence of many spectral bands in an HSI provides a valuable source of features, dimensionality reduction is often performed in the pre-processing step to reduce the correlation between bands. Most of the deep learning-based classification algorithms use unsupervised dimensionality reduction methods such as principal component analysis (PCA).

However, in this thesis in order to take advantage of class discriminatory information in the dimensionality reduction step as well as power of deep neural network we propose a new method that combines a supervised dimensionality …