Single image super resolution for spatial enhancement of hyperspectral remote sensing imagery

Hyperspectral Imaging (HSI) has emerged as a powerful tool for capturing detailed spectral information across various applications, such as remote sensing, medical imaging, and material identification. However, the limited spatial resolution of acquired HSI data poses a challenge due to hardware and acquisition constraints. Enhancing the spatial resolution of HSI is crucial for improving image processing tasks, such as object detection and classification. This research focuses on utilizing Single Image Super Resolution (SISR) techniques to enhance HSI, addressing four key challenges: the efficiency of 3D Deep Convolutional Neural Networks (3D-DCNNs) in HSI enhancement, minimizing spectral distortions, tackling data scarcity, and improving state-of-the-art performance. The thesis establishes a solid theoretical foundation and conducts an in-depth literature review to identify trends, gaps, and future directions in the field of HSI enhancement. Four chapters present novel research targeting each of the aforementioned challenges. All experiments are performed using publicly available datasets, and the results are evaluated both qualitatively and quantitatively using various commonly used metrics. The findings of this research contribute to the development of a novel 3D-CNN architecture known as 3D Super Resolution CNN 333 (3D-SRCNN333). This architecture demonstrates the capability to enhance HSI with minimal spectral distortions while maintaining acceptable computational cost and training time. Furthermore, a Bayesian-optimized hybrid spectral spatial loss function is devised to improve the spatial quality and minimize spectral distortions, combining the best characteristics of both domains. Addressing the challenge of data scarcity, this thesis conducts a thorough study on Data Augmentation techniques and their impact on the spectral signature of HSI. A new Data Augmentation technique called CutMixBlur is proposed, and various combinations of Data Augmentation techniques are evaluated to address the data scarcity challenge, leading to notable enhancements in performance. Lastly, the 3D-SRCNN333 architecture is extended to the frequency domain and wavelet domain to explore their advantages over the spatial domain. The experiments reveal promising results with the 3D Complex Residual SRCNN (3D-CRSRCNN), surpassing the performance of 3D-SRCNN333. The findings presented in this thesis have been published in reputable conferences and journals, indicating their contribution to the field of HSI enhancement. Overall, this thesis provides valuable insights into the field of HSI-SISR, offering a thorough understanding of the advancements, challenges, and potential applications. The developed algorithms and methodologies contribute to the broader goal of improving the spatial resolution and spectral fidelity of HSI, paving the way for further advancements in scientific research and practical implementations.Hyperspectral Imaging (HSI) has emerged as a powerful tool for capturing detailed spectral information across various applications, such as remote sensing, medical imaging, and material identification. However, the limited spatial resolution of acquired HSI data poses a challenge due to hardware and acquisition constraints. Enhancing the spatial resolution of HSI is crucial for improving image processing tasks, such as object detection and classification. This research focuses on utilizing Single Image Super Resolution (SISR) techniques to enhance HSI, addressing four key challenges: the efficiency of 3D Deep Convolutional Neural Networks (3D-DCNNs) in HSI enhancement, minimizing spectral distortions, tackling data scarcity, and improving state-of-the-art performance. The thesis establishes a solid theoretical foundation and conducts an in-depth literature review to identify trends, gaps, and future directions in the field of HSI enhancement. Four chapters present novel research targeting each of the aforementioned challenges. All experiments are performed using publicly available datasets, and the results are evaluated both qualitatively and quantitatively using various commonly used metrics. The findings of this research contribute to the development of a novel 3D-CNN architecture known as 3D Super Resolution CNN 333 (3D-SRCNN333). This architecture demonstrates the capability to enhance HSI with minimal spectral distortions while maintaining acceptable computational cost and training time. Furthermore, a Bayesian-optimized hybrid spectral spatial loss function is devised to improve the spatial quality and minimize spectral distortions, combining the best characteristics of both domains. Addressing the challenge of data scarcity, this thesis conducts a thorough study on Data Augmentation techniques and their impact on the spectral signature of HSI. A new Data Augmentation technique called CutMixBlur is proposed, and various combinations of Data Augmentation techniques are evaluated to address the data scarcity challenge, leading to notable enhancements in performance. Lastly, the 3D-SRCNN333 architecture is extended to the frequency domain and wavelet domain to explore their advantages over the spatial domain. The experiments reveal promising results with the 3D Complex Residual SRCNN (3D-CRSRCNN), surpassing the performance of 3D-SRCNN333. The findings presented in this thesis have been published in reputable conferences and journals, indicating their contribution to the field of HSI enhancement. Overall, this thesis provides valuable insights into the field of HSI-SISR, offering a thorough understanding of the advancements, challenges, and potential applications. The developed algorithms and methodologies contribute to the broader goal of improving the spatial resolution and spectral fidelity of HSI, paving the way for further advancements in scientific research and practical implementations

Caracterización fisicoquímica de cuerpos de aguas superficiales por medio de Sensores Remotos: Caso de estudio Presa J. A. Alzate, México

Tesis de maestríaEl monitoreo de la calidad del agua a través de los sensores remotos permite la estimación de Parámetros de Calidad del Agua (PCA) como los sólidos suspendidos totales (SST), nitrógeno total (NT), la demanda química de oxígeno (DQO), el fósforo total (PT) entre otros. La estimación de estos PCA se realiza generalmente a través de procesos de laboratorio, los cuales pueden requerir una cantidad considerable de tiempo y costos. El análisis del agua mediante sensores remotos considera la correlación entre los datos de calidad del agua y la reflectancia de los cuerpos de agua superficiales. Este estudio propone el uso de las bandas de la imagen Landsat 8 OLI para estimar cuatro PCA y validarlos mediante muestras de campos. La ventaja de estimar los PCA con sensores remotos radica en la precisión de sus resultados, en menores tiempo y costos a diferencias de los métodos tradicionales. Para obtener las funciones de PCA basadas en la reflectancia del agua, se propusieron regresiones multivariadas lineales, exponenciales y polinomiales. Dicho estudio es aplicado a La Presa J. A. Alzate. México como caso de estudio debido a las concentraciones de contaminantes transportados por el agua proveniente de la Zona Metropolitana de Toluca (ZMT). El análisis consideró 14 muestras de campo, 7 de las cuales se recolectaron antes de la temporada de lluvias (19/05/2018) y 7 después de la misma (16/10/2018). La metodología de este estudio se divide en tres fases: pre-procesamiento (límite del área de estudio, calibración y corrección atmosférica), procesamiento (tamaño de la muestra, regresión múltiple y validación) y post-procesamiento (interpolación). En el pre-procesamiento el modelo MODTRAN 4 fue utilizado para la corrección atmosférica del área de estudio para identificar el modelo de atmósfera ad hoc a esta zona y, por lo tanto, obtener la reflectancia que más se ajuste a la realidad de la superficie analizada. El procesamiento requirió de muestras de campo en diferentes fechas (temporada antes y después de lluvias) para diseñar los modelos de regresión múltiple, además de lo anterior también se analizaron supuestos de validación para los valores de entrada y la evaluación del modelo se basó en el ̅2, p-value, validación cruzada, estadístico F, estadístico t y coeficiente de eficiencia de Nash-Sutcliffe (E). Los resultados obtenidos en el presente estudio indican que el NT y la DQO pueden ser estimados de manera confiable con el modelo de regresión exponencial múltiple, para los SST y PT por medio de la regresión polinómica múltiple. El modelo que presenta la menor capacidad explicativa de los datos corresponde al modelo lineal para los SST ya que obtiene un ̅2=0.6125, a pesar de las transformaciones a la variable dependiente con la finalidad de linealizarlo. El post-procesamiento arrojó que la zona norte presenta altos contenidos de polutos, los cuales inclusive sobrepasan los límites permisibles de las normas mexicanas para la DQO, PT y SST. Únicamente el NT se encuentra dentro de los límites permisibles para aguas de uso de riego agrícola tanto para la temporada antes de lluvias, como para después de lluvias. A nivel mensual el comportamiento de la DQO, NT y PT tienden a presentar altas concentraciones en los meses antes de secas. Para los SST a nivel mensual presenta disparidad en las concentraciones de este PCA.CONACy