Skip to main content

Discrete Detectors and Scanning mirrors Across the track scanner Whisk broom scanner.

Multispectral Imaging Using Discrete Detectors and Scanning Mirrors (Across-Track Scanner or Whisk Broom Scanner)

Multispectral Imaging: This technique involves capturing images of the Earth's surface using multiple sensors that are sensitive to different wavelengths of electromagnetic radiation. This allows for the identification of various features and materials based on their spectral signatures.

Discrete Detectors: These are individual sensors that are arranged in a linear or array configuration. Each detector is responsible for measuring the radiation within a specific wavelength band.

Scanning Mirrors: These are optical components that are used to deflect the incoming radiation onto the discrete detectors. By moving the mirrors, the sensor can scan across the scene, capturing data from different points.

Across-Track Scanner or Whisk Broom Scanner: This refers to the scanning mechanism where the mirror moves perpendicular to the direction of flight. This allows for the collection of data along a swath, covering a wide area on the ground.

Remote Sensing Terminologies

A. Rotating Mirror

  • Definition: A mechanical component in some satellite-based remote sensing systems that rotates to scan the Earth's surface. It directs sunlight onto a sensor, enabling the collection of data over a wide area.
  • Purpose: To increase the coverage area of the sensor, allowing for rapid data acquisition.

B. Internal Detectors

  • Definition: Sensors within a remote sensing instrument that convert electromagnetic radiation into electrical signals. These signals are then processed to produce images or data.
  • Purpose: To capture and measure the intensity of radiation reflected or emitted from the Earth's surface.

C. Instantaneous Field of View (IFOV)

  • Definition: The smallest area on the ground that can be resolved by a remote sensing sensor at a given time.
  • Purpose: To determine the spatial resolution of the sensor, indicating the level of detail it can capture.

D. Ground Resolution Cell Viewed (GRCV)

  • Definition: The area on the ground corresponding to the IFOV of a sensor at a specific altitude.
  • Purpose: To measure the size of the smallest distinguishable feature on the Earth's surface.

E. Angular Field of View (AFOV)

  • Definition: The angle between the extreme rays of the field of view of a sensor.
  • Purpose: To determine the extent of the area that can be observed by the sensor at a given distance.

F. Swath

  • Definition: The width of the area on the ground that a sensor can cover in a single pass.
  • Purpose: To measure the lateral coverage of the sensor, indicating the efficiency of data collection.

How it works:

  1. Radiation Collection: The scanning mirror deflects incoming radiation from the Earth's surface onto the array of discrete detectors.
  2. Spectral Separation: Each detector measures the radiation within its specific wavelength band, capturing information about different materials and features.
  3. Scanning: The scanning mirror moves across the scene, allowing the sensor to collect data from multiple points.
  4. Data Processing: The collected data is processed to create multispectral images that can be analyzed to identify and classify features based on their spectral signatures.

Key advantages of this approach:

  • High spatial resolution: Can capture detailed images of the Earth's surface.
  • Wide swath coverage: Can cover a large area in a single pass.
  • Versatility: Can be used for various remote sensing applications, such as land use mapping, vegetation monitoring, and mineral exploration.
Warm regards.
..
Vineesh V
AISHE and UGC Nodal Officer
Assistant Professor of Geography,
Government College Chittur, Palakkad
https://g.page/vineeshvc

Comments

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...

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...

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...

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...

Role of Geography in Disaster Management

Geography plays a pivotal role in disaster management by facilitating an understanding of the impact of natural disasters, guiding preparedness efforts, and supporting effective response and recovery. By analyzing geographical features, environmental conditions, and historical data, geography empowers disaster management professionals to identify risks, plan for hazards, respond to emergencies, assess damage, and monitor recovery. Geographic Information Systems (GIS) serve as crucial tools, providing critical spatial data for informed decision-making throughout the disaster management cycle. Key Concepts, Terminologies, and Examples 1. Identifying Risk: Concept: Risk identification involves analyzing geographical areas to understand their susceptibility to specific natural disasters. By studying historical events, topography, climate patterns, and environmental factors, disaster management experts can predict which regions are most vulnerable. Terminologies: Hazard Risk: The pr...