Radar image. Polarization in Remote Sensing

L band radars operate on a wavelength of 15-30 cm and a frequency of 1-2 GHz. L band radars are mostly used for clear air turbulence studies. S band radars operate on a wavelength of 8-15 cm and a frequency of 2-4 GHz. Because of the wavelength and frequency, S band radars are not easily attenuated.

Polarization refers to the direction of travel of an electromagnetic wave vector's tip:

vertical (up and down),

horizontal (left to right), or

circular (rotating in a constant plane left or right).

a synthetic aperture radar (SAR) for high-resolution imaging.

a radar altimeter, to measure the ocean topography.

echo amplitude

a wind scatterometer to measure wind speed and direction.

Other types of radars have been flown for Earth observation missions: precipitation radars such as the

Tropical Rainfall Measuring Mission,

cloud radars like the one used on Cloudsat.

RISAT-1 (SAR, ISRO India, 2012)

RORSAT (SAR, Soviet Union, 1967-1988)

Seasat (SAR, altimeter, scatterometer, US, 1978)

ERS-1 & ERS-2 (European Remote-Sensing Satellite) (altimeter, combined SAR/scatterometer)

TOPEX/Poseidon (altimeter)

Tropical Rainfall Measuring Mission (Precipitation Radar)

Cloudsat (cloud radar)

Metop (scatterometer)

QuickScat (scatterometer)

Polarization in radar imaging refers to the orientation of the electric field of the radar waves that are used to illuminate the target. There are two main types of polarization: linear and circular. Linear polarization has the electric field oscillating in one plane, while circular polarization has the electric field oscillating in a circular pattern. The type of polarization used can affect the radar image, as different types of targets will reflect the radar waves differently based on the polarization. For example, linear polarization is better for detecting targets with smooth surfaces, while circular polarization is better for detecting targets with rougher surfaces. Additionally, using different types of polarization can help to reduce the effects of interference from other sources.

HH, HV, and VV are types of polarization that are used in radar imaging.

HH stands for horizontally polarized transmitted signal and horizontally polarized received signal. This type of polarization is useful for detecting targets with smooth surfaces, as the horizontally polarized radar waves will be reflected more efficiently by these types of targets.

HV stands for horizontally polarized transmitted signal and vertically polarized received signal. This type of polarization is useful for detecting targets with rougher surfaces, as the horizontally polarized radar waves will be scattered in many directions by these types of targets, resulting in a stronger return signal.

VV stands for vertically polarized transmitted signal and vertically polarized received signal. This type of polarization is useful for detecting targets with rougher surfaces, as the vertically polarized radar waves will be scattered in many directions by these types of targets, resulting in a stronger return signal.

Using these different types of polarization can help to enhance the contrast and details in radar images, and also it can be used to extract information about the targets, such as their shape, size, and surface roughness.

Comments

Supervised Classification

Image Classification in Remote Sensing Image classification in remote sensing involves categorizing pixels in an image into thematic classes to produce a map. This process is essential for land use and land cover mapping, environmental studies, and resource management. The two primary methods for classification are Supervised and Unsupervised Classification . Here's a breakdown of these methods and the key stages of image classification. 1. Types of Classification Supervised Classification In supervised classification, the analyst manually defines classes of interest (known as information classes ), such as "water," "urban," or "vegetation," and identifies training areas —sections of the image that are representative of these classes. Using these training areas, the algorithm learns the spectral characteristics of each class and applies them to classify the entire image. When to Use Supervised Classification: - You have prior knowledge about the c...

Image Transformation

In remote sensing, image transformation is used to create new images by manipulating existing ones, enhancing the information extracted from each pixel. Multi-image manipulation and other transformations allow analysts to highlight specific features, compare time-series data, or reduce data dimensions for better interpretation. 1. Multi-Image Manipulation This technique involves applying mathematical operations to multiple images, typically from different bands, dates, or sensors, to highlight specific features or changes. Common methods include: a. Image Addition Purpose : Highlights areas of similarity or enhances features when the same objects appear in multiple images. How it Works : By adding pixel values from two images, the output image emphasizes areas where brightness is high in both. Example : Enhancing overall brightness or detecting seasonal changes by adding images from different times. b. Image Subtraction Purpose : Identifies differences between images, useful for chan...

Isodata clustering

Iso Cluster Classification in Unsupervised Image Classification Iso Cluster Classification is a common unsupervised classification technique used in remote sensing. The "Iso Cluster" algorithm groups pixels with similar spectral characteristics into clusters, or spectral classes, based solely on the data's statistical properties. Unlike supervised classification, Iso Cluster classification doesn't require the analyst to predefine classes or training areas; instead, the algorithm analyzes the image data to find natural groupings of pixels. The analyst interprets these groups afterward to label them with meaningful information classes (e.g., water, forest, urban). How Iso Cluster Classification Works The Iso Cluster algorithm follows several steps to group pixels: Initial Data Analysis : The algorithm examines the entire dataset to understand the spectral distribution of the pixels across the spectral bands. Clustering Process : - The algorithm starts by divid...

Spectral Signature vs. Spectral Reflectance Curve

Spectral Signature A spectral signature is the unique pattern in which an object: absorbs energy reflects energy emits energy across different wavelengths of the electromagnetic spectrum. ✔ Key Points Every natural and man-made object on Earth interacts with sunlight differently. These interactions produce a distinct pattern , just like a "fingerprint". Sensors on satellites record these patterns as digital numbers (DN values) . These patterns help to identify and differentiate objects such as vegetation, soil, water, snow, buildings, minerals, etc. ✔ Examples of Spectral Signatures Healthy vegetation → High reflectance in NIR , strong absorption in red Water → Strong absorption in NIR and SWIR , low reflectance Dry soil → Gradual increase in reflectance from visible to NIR Snow → High reflectance in visible , low in SWIR ✔ Why Spectral Signature Matters It allows: Land cover classification Chan...

REMOTE SENSING INDICES

Remote sensing indices are band ratios designed to highlight specific surface features (vegetation, soil, water, urban areas, snow, burned areas, etc.) using the spectral reflectance properties of the Earth's surface. They improve classification accuracy and environmental monitoring. 1. Vegetation Indices NDVI – Normalized Difference Vegetation Index Formula: (NIR – RED) / (NIR + RED) Concept: Vegetation reflects strongly in NIR and absorbs in RED due to chlorophyll. Measures: Vegetation greenness & health Uses: Agriculture, drought monitoring, biomass estimation EVI – Enhanced Vegetation Index Formula: G × (NIR – RED) / (NIR + C1×RED – C2×BLUE + L) Concept: Corrects for soil and atmospheric noise. Measures: Vegetation vigor in dense canopies Uses: Tropical rainforest mapping, high biomass regions GNDVI – Green Normalized Difference Vegetation Index Formula: (NIR – GREEN) / (NIR + GREEN) Concept: Uses Green instead of Red ...

Vineesh V, Geography

Search This Blog