This paper presents a methodology to detect the coastal erosion and progradation effects in the ‘Atrato River’ delta, located in the Gulf of Urabá in Colombia, using SAR (Synthetic Aperture Radar) images. Erosion is the physical–mechanical loss of the soil that affects its functions and ecosystem services while producing a reduction in its productive capacity. Progradation is the deposition of layers in the basinward direction while moving coastward. Other studies have investigated these two phenomena using optical images, encountering difficulties due to the persistent presence of clouds in this region. In order to avoid the cloud effects, in this study, we used 16 Sentinel 1 SAR images with two different polarizations between 2016 and 2023. First, each image was rescaled from 0 to 255, then the image was despeckled by a deep learning (DL) model. Afterwards, a single RGB image was composed with the filtered polarizations. Next, a classifier with 99% accuracy based on Otsu’s method was used to determine whether each pixel was water or not. Then, the classified image was registered to a reference one using Oriented FAST and Rotated BRIEF (ORB) descriptor. Finally, a multitemporal analysis was performed by comparing every image to the previous one to identify the studied phenomena, calculating areas. Also, all images were integrated to obtain a heatmap that showed the overall changes across eight years (2016–2023) in a single image. The multitemporal analysis performed found that the newly created mouth is the most active area for these processes, coinciding with other studies. In addition, a comparison of these findings with the Oceanic Niño Index (ONI) showed a relative delayed coupling to the erosion process and a coupling of progradation with dry and wet seasons.

Speckle reduction in Synthetic Aperture Radar (SAR) images is a crucial challenge for effective image analysis and interpretation in remote sensing applications. This study proposes a novel deep learning-based approach using autoencoder architectures for SAR image despeckling, incorporating analysis of variance (ANOVA) for hyperparameter optimization. The research addresses significant gaps in existing methods, such as the lack of rigorous model evaluation and the absence of systematic optimization techniques for deep learning models in SAR image processing. The methodology involves training 240 autoencoder models on real-world SAR data, with performance metrics evaluated using Mean Squared Error (MSE), Structural Similarity Index (SSIM), Peak Signal-to-Noise Ratio (PSNR), and Equivalent Number of Looks (ENL). By employing Pareto frontier optimization, the study identifies models that effectively balance denoising performance with the preservation of image fidelity. The results demonstrate substantial improvements in speckle reduction and image quality, validating the effectiveness of the proposed approach. This work advances the application of deep learning in SAR image denoising, offering a comprehensive framework for model evaluation and optimization.

This article presents a comprehensive dataset combining Synthetic Aperture Radar (SAR) imagery from the Sentinel-1 mission with optical imagery, including RGB and Normalized Difference Vegetation Index (NDVI), from the Sentinel-2 mission. The dataset consists of 8800 images, organized into four folders—SAR_VV, SAR_VH, RGB, and NDVI—each containing 2200 images with dimensions of 512 × 512 pixels. These images were collected from various global locations using random geographic coordinates and strict criteria for cloud cover, snow presence, and water percentage, ensuring high-quality and diverse data. The primary motivation for creating this dataset is to address the limitations of optical sensors, which are often hindered by cloud cover and atmospheric conditions. By integrating SAR data, which is unaffected by these factors, the dataset offers a robust tool for a wide range of applications, including land cover classification, vegetation monitoring, and environmental change detection. The dataset is particularly valuable for training machine learning models that require multimodal inputs, such as translating SAR images to optical imagery or enhancing the quality of noisy data. Additionally, the structure of the dataset and the preprocessing steps applied make it readily usable for various research purposes. The SAR images are processed to Level-1 Ground Range Detected (GRD) format, including radiometric calibration and terrain correction, while the optical images are filtered to ensure minimal cloud interference.

This paper presents an analysis of different despeckling filters applied on both synthetically corrupted optical images and actual Synthetic Aperture Radar (SAR) images. Several authors use optical images as ground truth and then the images are corrupted by using a Gamma model to simulate the speckle, while other approaches use methods like multitemporal fusion to generate a ground truth using actual SAR images, which provides a result somehow equivalent to the one from the common multi look technique. Well-known filters, like local, and non-local and some of them based on artificial intelligence and deep learning, are applied to these two types of images and their performance is assessed by a quantitative analysis. One last validation is performed with a newly proposed method by using ratio images, resulting from the mathematical division (Hadamard division) of filtered and noisy images, to measure how similar the initial and the remaining speckle are by considering its Gamma distribution and divergence measurement. Our findings suggest that despeckling models relying on artificial intelligence exhibit notable efficiency, albeit concurrently displaying inflexibility when applied to particular image types based on the training dataset. Additionally, our experiments underscore the utility of the divergence measurement in ratio images in facilitating both visual inspection and quantitative evaluation of residual speckles within the filtered images.

The Aburrá Valley, located in the northwestern region of Colombia, has undergone significant land cover changes and urban expansion in recent decades, driven by rapid population growth and infrastructure development. This region, known for its steep topography and dense urbanization, faces considerable environmental challenges. Monitoring these transformations is essential for informed territorial planning and sustainable development. This study leverages Synthetic Aperture Radar (SAR) imagery from the Sentinel-1 mission, covering 2017–2024, to propose a methodology for the multitemporal analysis of land cover dynamics and urban expansion in the valley. The novel proposed methodology comprises several steps: first, monthly SAR images were acquired for every year under study from 2017 to 2024, ensuring the capture of surface changes. These images were properly calibrated, rescaled, and co-registered. Then, various multitemporal fusions using statistics operations were proposed to detect and find different phenomena related to land cover and urban expansion. The methodology also involved statistical fusion techniques—median, mean, and standard deviation—to capture urbanization dynamics. The kurtosis calculations highlighted areas where infrequent but significant changes occurred, such as large-scale construction projects or sudden shifts in land use, providing a statistical measure of surface variability throughout the study period. An advanced clustering technique segmented images into distinctive classes, utilizing fuzzy logic and a kernel-based method, enhancing the analysis of changes. Additionally, Pearson correlation coefficients were calculated to explore the relationships between identified land cover change classes and their spatial distribution across nine distinct geographic zones in the Aburrá Valley. The results highlight a marked increase in urbanization, particularly along the valley’s periphery, where previously vegetated areas have been replaced by built environments. Additionally, the visual inspection analysis revealed areas of high variability near river courses and industrial zones, indicating ongoing infrastructure and construction projects. These findings emphasize the rapid and often unplanned nature of urban growth in the region, posing challenges to both natural resource management and environmental conservation efforts. The study underscores the need for the continuous monitoring of land cover changes using advanced remote sensing techniques like SAR, which can overcome the limitations posed by cloud cover and rugged terrain. The conclusions drawn suggest that SAR-based multitemporal analysis is a robust tool for detecting and understanding urbanization’s spatial and temporal dynamics in regions like the Aburrá Valley, providing vital data for policymakers and planners to promote sustainable urban development and mitigate environmental degradation.

The speckle is a granular undesired pattern present in Synthetic-Aperture Radar (SAR) imagery. Despeckling has been an active field of research during the last decades, with approaches from local filters to non-local filters that calculate the new value of a pixel according to characteristics of other pixels that are not close, the more advanced paradigms based on deep learning, and the newer based on generative artificial intelligence. For the latter, it is necessary to have a large enough labeled dataset for training and validation. In this study, we propose using a dataset designed entirely from actual SAR imagery, calculated by multitemporal fusion operations to generate a ground truth reference, which will yield the models to be trained with the actual speckle patterns in the noisy images. Then, a comparative analysis of the impacts of including the generative capacity in the models is performed through visual and quantitative assessment. From the findings, it is concluded that the use of generative artificial intelligence with actual speckle exhibits notable efficiency compared to other approaches, which makes this a promising path for research in the context of SAR imagery.