This research proposes an innovative method for correcting banding errors in satellite images based on Generative Adversarial Networks (GAN). Small satellites are frequently launched into space to obtain images that can be used in scientific or military research, commercial activities, and urban planning, among other applications. However, its small cameras are more susceptible to radiometric, geometric errors, and other distortions caused by atmospheric interference. The proposed method was compared to the conventional correction technique using experimental data, showing the similar performance (92.64% and 90.05% accuracy, respectively). These experimental results suggest that generative models utilizing Artificial Intelligence (AI) techniques, specifically Deep Learning, are getting closer to achieving automatic correction close to conventional methods. Advantages of the GAN models include automating the task of correcting banding in satellite images, reducing the required time, and facilitating the processing without requiring prior technical knowledge in handling Geographic Information Systems (GIS). Potentially, this technique could represent a valuable tool for satellite image processing, improving the accuracy of the results and making the process more efficient. The research is particularly relevant to the field of remote sensing and can have practical applications in various industries.

This paper presents the results of the integration, commissioning, and in-orbit calibration of the ARGUS 2000 SWIR wavelength spectrometer onboard the Colombian FACSAT-2 satellite, Colombia’s first satellite used for the measurement of greenhouse gases (GHG). The satellite has been in orbit for approximately one year, gathering spectral signatures in order to characterize the data and perform calibration. The calibration represents a certain behavior following a second-order adjustment. For data analysis, retrieval algorithms have been developed to obtain synthetic spectra using the Genspect 1.2 software code. These synthetic spectra were obtained from the spectroscopic data associated with atmospheric gases (H₂O, O₂, CO, and CO₂), which are stored in the HITRAN database. Attempting to achieve a more accurate simulation of the experimental spectrum, certain instrumental parameters have been incorporated into the synthetic spectrum, including the resolution of the spectrometer, the field of view (FOV) angle of observation, the limited quantum efficiency of detection, and the slit function. As a result, six slit functions were tested, with the Gaussian and the diffraction functions proving to be the most effective. Finally, a profile of Total Carbon Column Observing Network (TCCON) concentrations was used for comparison with a spectral signature acquired by FACSAT-2, resulting in a close match between the synthetic spectrum and the measured spectrum.

El nanosatélite FACSAT-2 “Chiribiquete”, desarrollado conjuntamente por la Fuerza Aeroespacial Colombiana (FAC) y la empresa danesa GomSpace, tiene como objetivo proporcionar datos críticos sobre el territorio colombiano para aplicaciones de observación terrestre, en particular, en la cuantificación y evaluación de impactos ambientales. Este estudio detalla el proceso de selección tecnológica para las cargas útiles que se integrarán en el bus del FACSAT-2. El documento presenta los requisitos de la misión, las limitaciones y la evaluación del mercado, siguiendo los diez pasos propuestos por Larson, bajo un modelo de vigilancia tecnológica. Los requisitos técnicos guiaron la identificación de proveedores globales de sensores espaciales, la evaluación del historial espacial de los instrumentos y la evaluación de su éxito en misiones anteriores. Como resultado, se determinó que el bus de seis unidades albergaría la cámara MultiScape CIS 100, que ofrece una resolución de 4,7 metros por píxel y un rango espectral de 450 nm a 900 nm, y el espectrómetro de infrarrojo de onda corta Argus 2000, que cubre un rango de 1000 nm a 1700 nm. Esta combinación proporciona una capacidad significativa para estudios multitemporales en agricultura, uso del suelo e impacto ambiental. Esto posiciona a Colombia como el primer país sudamericano en lanzar y operar una misión de nanosatélite específicamente diseñada para el análisis de gases de efecto invernadero. Esta misión contribuye a las políticas ambientales actuales y a los planes de desarrollo espacial.

En un entorno caracterizado por amenazas híbridas y multidominio, esta inves-tigación analiza la inteligencia artificial explicable (XAI) como un habilitador estratégico en la toma de decisiones de la Fuerza Aeroespacial Colombiana (FAC). Para ello, se emplea un enfoque mixto que combinó una revisión documental con una encuesta estructurada a oficiales del alto mando de la FAC. Los resultados muestran una fuerte voluntad institucional para incorporar la XAI. Sin embargo, persisten limitaciones técnicas, doctrinales y culturales relacionadas con la estandarización de los datos, la capacitación del personal y la falta de marcos regulatorios específicos. Se concluye que la XAI es un habilitador estratégico capaz de garantizar la transparencia, la trazabilidad y la legitimidad de las decisiones militares. Para su implementación efectiva, se requiere un enfoque integral que articule las políticas éticas, la interoperabilidad de los datos y el fortalecimiento de las capacidades institucionales. De este modo, Colombia podría posicionarse como un referente regional en innovación y defensa.

Satellite images have diverse applications across scientific, commercial, and other domains. As a result, institutions are increasingly deploying Earth observation satellites to cater to their specific needs. This is the case of the Colombian Aerospace Force, which launched its nanosatellite, the FACSAT-1, to contribute to developing the space sector in Colombia. However, in some cases, captured images may need more quality and resolution for their intended purposes. Numerous image processing tools have been developed to enhance and optimize satellite imagery to address this challenge. Deep learning techniques, particularly Generative Adversarial Networks, have recently shown significant advancements in image processing. This paper investigates the widespread application of Generative Networks for satellite and aerial imagery, specifically focusing on super-resolution tasks. Super-resolution involves increasing the resolution of satellite images by up to four times their original size. The study presents the implementation and training of four different generative models and evaluates their performance using qualitative and quantitative measures. Two metrics, namely the maximum signal-to-noise ratio and the structural similarity index, are employed for comparative analysis. By assessing the output of each generative model, this research aims to determine their efficacy in enhancing satellite imagery resolution.