Synthetic aperture radar (SAR) image processing is a technically complex task that involves transforming raw data into usable products for specific applications, including advanced artificial intelligence (AI) systems. Due to the nature of SAR images, which are formed from reflected radar signals, processing must address challenges such as noise, geometric distortions, and polarimetric interpretation. Below, we detail the most important technical steps in SAR image processing and their relevance for specific applications.
Converting raw data to radar images
SAR image processing begins with the conversion of raw radar signal data into interpretable images. This process is critical to preserving the geometric and amplitude characteristics of the observed surface. The technique of SAR approach involves a reconstruction that simulates a virtual long “aperture” by combining radar returns from various positions on the satellite during its trajectory. To achieve this, advanced algorithms such as correlation and Fast Fourier Transform (FFT) are used, which are essential for obtaining high-resolution images.
One of the key methods is the Backprojection, which reconstructs SAR images by projecting echo data onto a pixel grid, allowing the information to be displayed clearly. On the other hand, the Range-Doppler algorithm It is used to differentiate objects in the lateral direction, taking advantage of the Doppler frequency. Fast Fourier Transform (FFT) It transforms signals into the frequency domain, facilitating efficient and rapid image reconstruction.
Geometric correction
Geometric correction in SAR image processing is essential to address the distortions inherent to this technology. SAR images can present alterations in range and azimuth, which prevents them from having an accurate projection like traditional optical images. This process seeks to transform SAR images to a standard geographic coordinate system, such as WGS84.
Filtering and noise reduction
Filtering and noise reduction in SAR images is crucial due to the presence of speckle, a type of noise that affects the visual quality and clarity of data. This noise arises from coherent interference between radar waves reflected from various surfaces within a single pixel, making it difficult to identify important details.
To mitigate speckle without compromising essential details, advanced filtering techniques are employed. Among the most common are Lee filter, which smooths homogeneous areas by calculating the variance within a local window. Another method is the Frost filter, which is adaptive and adjusts its behavior based on local content, preserving edges while reducing noise in uniform regions. Gamma MAP filter It is also popular for its ability to optimize signal-to-noise ratio and maintain contrast in key image structures.
This stage is crucial for automatic object or pattern detection applications, where accurate interpretation of relative intensities is important for AI algorithms.
Radiometric correction
Radiometric correction is essential to improve the accuracy of SAR image interpretation, as the intensity of radar echoes can vary due to factors such as terrain geometry or radar incidence angle. This correction ensures that reflectivity values are consistent throughout the image, which is crucial for applications such as automatic object detection.
Two key adjustments are the atmospheric attenuation correction, which compensates for the effects of the atmosphere (such as absorption and scattering of SAR signals), and the angular incidence correction, which normalizes intensity variations caused by the radar’s tilt angle relative to the ground. This ensures accurate interpretation of intensities, which is critical for AI algorithms that rely on an accurate reading of radiometric data in the image.
Polarimetric decomposition
Radar can transmit and receive waves in different polarizations (HH, HV, VH, VV). Polarimetric decomposition makes it possible to take advantage of the additional information obtained by transmitting and receiving signals in different polarizations. This process analyzes the different responses of surfaces or objects in the image to extract valuable details about their structure and properties.
Among the most commonly used methods is the Freeman-Durden decomposition, which divides the signal into three key components: volumetric scattering, single scattering and double scattering, helping to more accurately characterize the observed surface. On the other hand, the Cloude-Pottier decomposition, which uses coherence matrices, allows to classify the types of scattering in the scene, providing detailed information on the nature of the objects and materials present.
This type of polarimetric analysis is essential for applications such as the classification of soil or vegetation types, the identification of water bodies or artificial infrastructures, and for security and defense applications, such as the detection of unusual objects or activities in complex scenarios.
SAR interferometry
SAR interferometry (InSAR) is an advanced technique that measures small displacements on the Earth's surface by comparing the phase of radar signals captured at different times. The key to InSAR is to generate an interferogram that reflects these phase differences between two SAR images. Effective processing requires correcting atmospheric errors using specific models and post-processing techniques. The algorithm then Phase Unwrapping or phase unfolding translates these differences into quantifiable displacements, allowing data on millimetric deformations to be obtained.
Multi-sensor fusion
Multi-sensor fusion combines SAR images with other data sources, such as optical or hyperspectral images, to improve the accuracy and richness of the information obtained. This process involves advanced alignment and registration techniques to ensure that data from different sensors are perfectly synchronized and aligned.
The fusion process can be carried out at several levels. At the pixel level, the signals captured by different sensors are directly combined in each pixel, providing an image enriched with complementary details. At the feature level, key attributes of each image, such as textures or edges, are extracted and then merged to improve the identification of objects or patterns. Finally, in the fusion at the decision level, the results of different algorithms that analyze the images separately are integrated, combining their conclusions to obtain a more complete view.
Multisensory fusion is crucial in AI systems, as it allows leveraging the best of different data sources. This approach improves the robustness and accuracy of models, especially in multispectral scenarios or with varying visibility conditions.
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