Skip to main content

SAR Satalites

1. Sentinel-1A and Sentinel-1B (Sentinel-1): Part of the European Space Agency's Copernicus program, the Sentinel-1 mission consists of a pair of SAR satellites (Sentinel-1A and Sentinel-1B) that provide continuous all-weather, day-and-night radar imaging.

2. TerraSAR-X and TanDEM-X: These are German radar satellites operated by the German Aerospace Center (DLR) in partnership with Airbus Defence and Space. TerraSAR-X and TanDEM-X work together to create high-resolution SAR images and generate precise digital elevation models of the Earth's surface.

3. RADARSAT-2: A Canadian SAR satellite operated by the Canadian Space Agency. It provides radar imagery for various applications, including environmental monitoring, disaster management, and maritime surveillance.

4. Cosmo-Skymed: An Italian constellation of SAR satellites designed for dual-use applications, including civil and military uses. It consists of four satellites: Cosmo-Skymed 1, 2, 3, and 4.

5. ALOS-2 (Advanced Land Observing Satellite 2): A Japanese SAR satellite operated by the Japan Aerospace Exploration Agency (JAXA). ALOS-2 is used for various applications, including disaster monitoring, forestry, and agriculture.

6. RADARSAT Constellation Mission (RCM): A Canadian SAR satellite mission consisting of three identical satellites: RADARSAT-2, RCM-1, and RCM-2. RCM provides frequent radar imaging of Canada and other regions.

7. ICEYE: A private Finnish company operating a constellation of SAR microsatellites. ICEYE's small SAR satellites are known for their agility and ability to provide rapid revisit times.

8. COSMIC-2 (Constellation Observing System for Meteorology, Ionosphere, and Climate-2): While primarily a mission for atmospheric and ionospheric studies, COSMIC-2 includes a GNSS-RO (Global Navigation Satellite System Radio Occultation) payload, which can be used for SAR processing to obtain information about surface properties.





Comments

Post a Comment

Popular posts from this blog

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...
Post a Comment
Read more

Supervised Classification

In the context of Remote Sensing (RS) and Digital Image Processing (DIP) , supervised classification is the process where an analyst defines "training sites" (Areas of Interest or ROIs) representing known land cover classes (e.g., Water, Forest, Urban). The computer then uses these training samples to teach an algorithm how to classify the rest of the image pixels. The algorithms used to classify these pixels are generally divided into two broad categories: Parametric and Nonparametric decision rules. Parametric Decision Rules These algorithms assume that the pixel values in the training data follow a specific statistical distribution—almost always the Gaussian (Normal) distribution (the "Bell Curve"). Key Concept: They model the data using statistical parameters: the Mean vector ( $\mu$ ) and the Covariance matrix ( $\Sigma$ ) . Analogy: Imagine trying to fit a smooth hill over your data points. If a new point lands high up on the hill, it belongs to that cl...
Post a Comment
Read more

Hazard Mapping Spatial Planning Evacuation Planning GIS

Geographic Information Systems (GIS) play a pivotal role in disaster management by providing the tools and frameworks necessary for effective hazard mapping, spatial planning, and evacuation planning. These concepts are integral for understanding disaster risks, preparing for potential hazards, and ensuring that resources are efficiently allocated during and after a disaster. 1. Hazard Mapping: Concept: Hazard mapping involves the process of identifying, assessing, and visually representing the geographical areas that are at risk of certain natural or human-made hazards. Hazard maps display the probability, intensity, and potential impact of specific hazards (e.g., floods, earthquakes, hurricanes, landslides) within a given area. Terminologies: Hazard Zone: An area identified as being vulnerable to a particular hazard (e.g., flood zones, seismic zones). Hazard Risk: The likelihood of a disaster occurring in a specific location, influenced by factors like geography, climate, an...
Post a Comment
Read more

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...
Post a Comment
Read more

Scope of Disaster Management

Disaster management refers to the systematic approach to managing and mitigating the impacts of disasters, encompassing both natural hazards (e.g., earthquakes, floods, hurricanes) and man-made disasters (e.g., industrial accidents, terrorism, nuclear accidents). Its primary objectives are to minimize potential losses, provide timely assistance to those affected, and facilitate swift and effective recovery. The scope of disaster management is multifaceted, encompassing a series of interconnected activities: preparedness, response, recovery, and mitigation. These activities must be strategically implemented before, during, and after a disaster. Key Concepts, Terminologies, and Examples 1. Awareness: Concept: Fostering public understanding of potential hazards and appropriate responses before, during, and after disasters. This involves disseminating information about risks, safety measures, and recommended actions. Terminologies: Hazard Awareness: Recognizing the types of natural...
Post a Comment
Read more