QuickBird. AVIRIS

QuickBird and AVIRIS

1. QuickBird:

- QuickBird is a commercial high-resolution Earth observation satellite system launched by DigitalGlobe in 2001.

- Spatial Resolution: It is known for its very high spatial resolution, with panchromatic (black and white) imagery at 61 centimeters and multispectral imagery at 2.44 meters.

- Spectral Bands: QuickBird is equipped with multispectral sensors, which means it captures imagery in multiple spectral bands, including visible, near-infrared, and short-wave infrared.

- Applications: QuickBird data is used for various applications, such as urban planning, land cover mapping, disaster management, and environmental monitoring. Its high spatial resolution makes it suitable for detailed feature extraction and analysis in urban and natural environments.

- Availability: QuickBird data was widely used by commercial and government entities for both civil and defense purposes. However, it's important to note that satellite missions have lifespans, and data availability may have changed since my last knowledge update in September 2021.

2. AVIRIS (Airborne Visible/Infrared Imaging Spectrometer):

- AVIRIS is an airborne remote sensing instrument designed to capture high-resolution spectral data.

- Spectral Resolution: It offers high spectral resolution, covering a wide range of wavelengths from visible to short-wave infrared.

- Applications: AVIRIS is often used for geological and environmental studies, mineral exploration, vegetation analysis, and atmospheric research. Its ability to capture detailed spectral signatures helps scientists and researchers identify and study various materials and components on the Earth's surface.

- Flexibility: Unlike satellites, AVIRIS is mounted on aircraft, providing flexibility in data collection. Researchers can target specific areas of interest for detailed spectral analysis.

- Scientific Research: AVIRIS has contributed significantly to scientific research in fields such as geology, agriculture, forestry, and climate science by providing rich spectral data for analysis.

- Availability: AVIRIS data is typically acquired through airborne campaigns conducted by research institutions and agencies. Availability may vary depending on specific research projects and campaigns.

In summary, QuickBird is a high-resolution commercial satellite known for its detailed imagery and multispectral capabilities, while AVIRIS is an airborne imaging spectrometer designed for capturing high-resolution spectral data. Both instruments have played significant roles in remote sensing, contributing to various scientific, environmental, and commercial applications. Please note that the availability and specific details of these systems may have evolved since my last knowledge update in September 2021.

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...

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...

Pre During and Post Disaster

Disaster management is a structured approach aimed at reducing risks, responding effectively, and ensuring a swift recovery from disasters. It consists of three main phases: Pre-Disaster (Mitigation & Preparedness), During Disaster (Response), and Post-Disaster (Recovery). These phases involve various strategies, policies, and actions to protect lives, property, and the environment. Below is a breakdown of each phase with key concepts, terminologies, and examples. 1. Pre-Disaster Phase (Mitigation and Preparedness) Mitigation: This phase focuses on reducing the severity of a disaster by minimizing risks and vulnerabilities. It involves structural and non-structural measures. Hazard Identification: Recognizing potential natural and human-made hazards (e.g., earthquakes, floods, industrial accidents). Risk Assessment: Evaluating the probability and consequences of disasters using GIS, remote sensing, and historical data. Vulnerability Analysis: Identifying areas and p...

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...

Atmospheric Correction

It is the process of removing the influence of the atmosphere from remotely sensed images so that the data accurately represent the true reflectance of Earth's surface . When a satellite sensor captures an image, the radiation reaching the sensor is affected by gases, water vapor, aerosols, and dust in the atmosphere. These factors scatter and absorb light, changing the brightness and color of the features seen in the image. Although these atmospheric effects are part of the recorded signal, they can distort surface reflectance values , especially when images are compared across different dates or sensors . Therefore, corrections are necessary to make data consistent and physically meaningful. 🔹 Why Do We Need Atmospheric Correction? To retrieve true surface reflectance – It separates the surface signal from atmospheric influence. To ensure comparability – Enables comparing images from different times, seasons, or sensors. To improve visual quality – Remo...

Vineesh V, Geography

Search This Blog