Interpretation of Radar Images: Microwave Signal and Object, Interactions, Applications of Radar, and Advanced Radar Processing Techniques (Especially for GATE-Geospatial 2022)

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The brightness of features in a radar image depends on the strength of the backscattered signal. In turn, the amount of energy that is backscattered depends on various factors. An understanding of these factors will help you to interpret radar images properly.

Illustration 2 for Interpretation of Radar Images: …

Microwave Signal and Object Interactions

The amount of energy that is received at the radar antenna depends on the illuminating signal (radar system parameters such as wavelength, polarization, viewing geometry, et cetera) and the characteristics of the illuminated object (roughness, shape, orientation, dielectric constant, etc.) .

Influence of the Illuminating Signal

To a certain degree, the wavelength of a radar system influences the depth to which the waves penetrate the object surface. In addition, it determines the size of the objects that the waves interact with. For example, a short microwave will only penetrate the leaves on top of the trees (e. g. , X-band = 3 cm) whereas in the case of L-band (23 cm) , the radiation penetrates into the canopy. The polarization of the microwave plays an important role in the interpretation of the form and the orientation of small scattering elements that compose the surface object. Therefore, the use of microwaves with different polarizations yields different images that might help in the identification of objects.

Influence of the Illuminated Surface

An absolute measure for the backscattering behaviour of an object like reflectance in optical systems is calculated from the ratio of the emitted and received signal, considering the range to the object. This is called the radar cross section sigma (Οƒ) and it is expressed in decibels (db) . The amount of energy backscattered from an object depends on its characteristics such as surface roughness, moisture content (electrical properties of the object) , its orientation with respect to the illuminating signal (local incidence angle) and its shape. Apart from topography, the surface roughness is the terrain property that most strongly influences the strength of the radar return. It is a relative aspect, depending upon wavelength and incidence angle. A surface is considered β€˜rough’ if it has height variations of dimensions that are close to the radar wavelength, for example, the size of a leave. In radar images, rough surfaces appear bright, smooth surfaces appear dark in radar images. This is a result of the scattering behaviour of radar waves. Another important parameter that influences the microwave backscatter behaviour is the dielectric constant, which describes the electrical properties of the surface material. The moisture content of the object affects the electrical properties and therefore the dielectric constant.

Scattering Patterns

The radar is scattered in different ways depending on the above-mentioned characteristics of the signal and object. Changes in the electrical properties influence the absorption, transmission and reflection of microwaves. This means that the moisture content of the surface is an essential component of the total scattering. If an object is wet, surface scattering takes place. The type of reflection (ranging from specular to diffuse) and the strength depend on the roughness of the material. Generally, reflectivity, and therefore image brightness increases with increasing moisture content.

Applications of Radar

There are many useful applications of radar images. Radar data provide complementary information to visible and infrared remote sensing data. In the case of forestry, radar images can be used to obtain information about forest canopy, biomass and different forest types. Radar images also allow the differentiation of different land cover types such as urban areas, agricultural fields, water bodies, et cetera. In agricultural crop identification, the use of radar images acquired using different polarization (mainly airborne) is quite effective. It is crucial for agricultural applications to acquire data at a certain point in time (season) to obtain the necessary parameters. This is possible because radar can operate independently of whether or daylight conditions. In geology and geomorphology, the fact that radar provides information about surface texture and roughness plays an important role in lineament detection and geological mapping. Other successful applications of radar include hydrological modelling and soil moisture estimation, based on the sensitivity of the microwave to the dielectric properties of the observed surface. The interaction of microwaves with ocean surfaces and ice provides useful data for oceanography and ice monitoring. Operational systems use data from the European SAR system ERS-2 and the Canadian Radarsat programme. In this framework, the data is also used for oil slick monitoring and environmental protection. Looking at SAR interferometry there are plenty of interesting examples in the field of natural disaster monitoring and assessment, i.e.. , earthquakes, volcano eruptions, flooding, et cetera.

Advanced Radar Processing Techniques

Radar data provide a wealth of information that is not only based on a derived intensity image but also on other data properties that measure characteristics of the objects. One example is radar interferometry, an advanced processing method that takes advantage of the phase information of the microwave. If you look at two waves that are emitted with a slight offset you obtain a phase difference between the two waves. The offset can be based on two antennas mounted on the same platform (e. g. , aircraft or space shuttle) or based on two different orbits/passes. The range difference is calculated by measuring the phase difference between the two backscattered waves received at the antenna. With the knowledge of the position of the platform with respect to the Earth surface, the elevation of the object is determined. Phase differences are displayed in so-called interferograms where different colours represent variations in height. The interferogram is used to produce digital elevation models. Differential interferometry is based on the creation of two interferograms of successive radar data acquisitions. These interferograms are subtracted from each other in order to illustrate the changes that have occurred. These are useful for change detection, e. g. , earthquake damage assessment.

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