Overview of using SAR imagery for Data Science

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SAR images are special images that are obtained with a radar that emits and receives radio waves. These images can reveal a lot of detail and information about the Earth’s surface, even in cloudy, rainy, or dark conditions. If you want to find out how this technique works, what are its characteristics, how SAR data is processed and what are its applications, follow my article.

Basic Principles of SAR Image

SAR stands for Synthetic Aperture Radar, a remote sensing technique that uses radio waves to capture images of the Earth’s surface. The operating principle of SAR is based on the transmission and reception of radar pulses by an air or space platform. Radar pulses are reflected by objects on the surface and recorded by the receiver. The distance between the platform and the objects is derived from the round-trip time of the pulses, while the direction is derived from the phase difference between the transmitted and received pulses. The combination of this data makes it possible to reconstruct the image of the surface with a high spatial resolution.

One of the key features of SAR is its ability to illuminate the surface independently of weather conditions and time of day. In fact, radio waves can penetrate clouds and precipitation, and are not dependent on sunlight. This makes SAR a very useful tool for continuous, real-time monitoring of the Earth’s surface.

Another important aspect of SAR is the geometry of the image, which depends on the angle of incidence and the direction of observation of the radar. The angle of incidence is the angle between the direction of the radar beam and the normal to the surface, while the observation direction is the angle between the direction of the radar beam and the flight direction of the platform. The geometry of the SAR image determines the shape and size of the detected objects, as well as the contrast and texture of the image. For example, vertical objects such as buildings or trees appear brighter and more elongated in SAR images than horizontal objects such as roads or fields.

Features of SAR Images

SAR images are characterized by four main parameters: spatial resolution, radiometric resolution, temporal resolution, and polarization.

• Spatial resolution is the ability to distinguish two nearby objects on the surface. It depends on the radar wavelength, angle of incidence, beam width, and synthetic aperture size. The higher the spatial resolution, the more visible the surface details. However, high spatial resolution requires more data and more processing complexity.
• Radiometric resolution is the ability to distinguish differences in the intensity of the reflected radar signal from objects on the surface. It depends on the number of bits used to encode the signal, the signal-to-noise ratio, and the quantization level. The higher the radiometric resolution, the more shades of gray the image are. However, high radiometric resolution requires more bandwidth and more storage memory.
• Temporal resolution is the ability to capture images of the same area at different times. It depends on the frequency of passage of the platform and the coverage of the area of interest. The higher the temporal resolution, the more it is possible to monitor the temporal variations of the surface. However, high temporal resolution requires greater availability of platforms and greater coordination between them.
• Polarization is the property that describes the orientation of the electric field of the radar signal. It can be linear, circular, or elliptical. Polarization affects how the radar signal is reflected by objects on the surface, depending on their shape, structure, and orientation. Polarization can be used to increase the contrast between objects and to extract additional information about the surface.

SAR Data Processing

SAR data is subject to several processing processes to improve image quality and to extract useful information from the surface. The main processing processes are:

• Focusing, which is the process of transforming the raw data acquired by the radar into a coherent and sharp image. Focusing requires the use of mathematical algorithms that compensate for the effects of platform distortion, scattering, and movement. Focusing can be performed in both the spatial domain and the frequency domain, depending on the complexity and precision required.
• Geocoding, which consists of projecting the SAR image onto a geographic or cartographic coordinate system. Geocoding requires the use of mathematical models that describe the geometry of the image and the relationship between the coordinates of the image and the geographic coordinates. Geocoding allows SAR images to be overlaid and compared with other geospatial data sources, such as maps or optical imagery.
• Terrain correction, which is to remove or reduce the effects of topography and terrain slope on the SAR image. Terrain correction requires the use of digital elevation models (DEMs) that provide the elevation and orientation of the terrain at each point in the image. Terrain correction allows you to get a more realistic and uniform representation of the surface, and to calculate parameters such as area, volume and distance of objects.
• Classification, which consists of dividing the SAR image into homogeneous regions based on similarity or affiliation. Classification requires the use of machine learning algorithms that analyze image characteristics, such as intensity, texture, shape, and polarization, and associate them with categories or classes. Classification makes it possible to identify and map objects and phenomena present on the surface, such as soil, vegetation, water, artificial structures, snow cover, landslides, floods, etc.

SAR Applications

SAR images have a wide range of applications in different domains, such as environmental monitoring, natural resource management, natural disaster management, and human activity monitoring. Some examples of SAR applications are:

• Environmental monitoring: SAR images can be used to monitor the state and the changes of the environment, such as the land cover, the land use, the soil moisture, the vegetation biomass, the deforestation, the desertification, the erosion, the urbanization, the pollution, the climate change, etc. SAR images can provide valuable information for the assessment and the conservation of the environment, as well as for the planning and the management of sustainable development policies.
• Natural resource management: SAR images can be used to monitor the availability and the exploitation of natural resources, such as the water, the minerals, the energy, the agriculture, the forestry, the fisheries, the wildlife, etc. SAR images can provide useful information for the estimation and the mapping of the natural resources, as well as for the optimization and the control of their use and distribution.
• Natural disaster management: SAR images can be used to monitor the occurrence and the impact of natural disasters, such as the earthquakes, the volcanoes, the landslides, the floods, the droughts, the fires, the storms, the tsunamis, etc. SAR images can provide timely and reliable information for the detection and the prediction of the natural hazards, as well as for the mitigation and the recovery of the disaster effects.
• Human activity monitoring: SAR images can be used to monitor the activities and the behaviors of human beings, such as the transportation, the communication, the security, the defense, the intelligence, the surveillance, the reconnaissance, the warfare, the terrorism, the smuggling, the illegal immigration, etc. SAR images can provide critical information for the identification and the tracking of the human targets, as well as for the prevention and the response of the human threats.

Conclusion

SAR images are a very powerful and versatile data source for Data Science, as they offer unique and complementary information compared to other remote sensing techniques. SAR images have the advantage of being independent of illumination and atmospheric conditions, having a high spatial resolution, being able to operate in different polarizations and being able to detect temporal variations of the surface. However, SAR images also have limitations, such as processing complexity, geometric distortion, speckle, sensitivity to terrain and vertical objects, and the need for good spatial and temporal coverage.

To make the most of the potential of SAR images, it is necessary to apply advanced processing techniques, such as focusing, geocoding, terrain correction and classification, which require the use of mathematical algorithms, digital models and machine learning methods. In addition, SAR imagery should be integrated with other geospatial data sources, such as optical imagery, lidar data, GPS data, in-situ data, etc., to obtain a more complete and accurate view of the Earth’s surface.

SAR images have a wide range of applications in different fields, such as environmental monitoring, natural resource monitoring, natural disaster monitoring, and human activity monitoring. These applications have a great social, economic and environmental impact, as they can contribute to the protection and enhancement of the territory, the efficient and sustainable management of resources, the prevention and mitigation of risks, and the security and defense of citizens.

In conclusion, SAR images are a very useful and innovative tool for Data Science, which however requires specific skills and a good knowledge of the principles and characteristics of this technique. For this reason, it is important to train and update professionals in the field, and to promote the dissemination and sharing of SAR images among the scientific community and the public.