K-means clustering in Remote Sensing

K-means clustering is a method of unsupervised machine learning used to classify data into k clusters based on their similarity. In remote sensing, it is often used to classify satellite or aerial imagery based on the spectral characteristics of each pixel.

Here's an example of how K-means clustering might be used in remote sensing:

A satellite captures an image of a region, and the data is collected for each pixel in the image.

Each pixel in the image has several spectral bands, such as red, green, and blue.

The K-means algorithm is used to classify each pixel in the image based on the values of these spectral bands.

The algorithm starts by randomly selecting k centroids (representative points) within the data set.

Then, it assigns each pixel to the closest centroid based on the distance between the pixel's values and the centroid's values.

The algorithm then calculates the mean of all the pixels in each cluster, and uses these means as the new centroids.

This process is repeated until the clusters no longer change.

The resulting image is a classification map, where each pixel is assigned to one of k clusters, or classes, based on its spectral characteristics.

In remote sensing, the resulting image is usually a color composite image, where each class is assigned a unique color.

Comments

Remote Sensing Technology

Remote sensing is a rapidly evolving geospatial technology used to collect information about the Earth's surface and atmosphere without direct physical contact . It involves detecting and measuring electromagnetic radiation (EMR) reflected or emitted from objects using sensors mounted on satellites, aircraft, or drones. Remote sensing systems are fundamentally classified based on (1) the energy source used for illumination and (2) the region of the electromagnetic spectrum utilized for sensing . 1. Types of Remote Sensing Based on Energy Source Remote sensing systems are commonly categorized according to whether the sensor generates its own energy or relies on naturally available radiation . Passive Remote Sensing Principle: Passive remote sensing relies on natural sources of electromagnetic energy , primarily solar radiation reflected from the Earth's surface or thermal radiation emitted by objects. Operation: Most passive sensors operate during daylight when sunlight is av...

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...

Spatial Entity and Spatial Object

Concepts Spatial Entity : Refers to any real-world feature or phenomenon that exists in a specific location and can be identified in space. This emphasizes the actual physical or conceptual presence of the feature. Spatial Object : Represents the digital or computational representation of a spatial entity within a Geographic Information System (GIS). This includes its geometry (e.g., points, lines, polygons) and associated attributes. Key Distinction : While the terms are often interchangeable, spatial entity tends to focus on the real-world phenomenon, whereas spatial object highlights its representation in GIS. Key Terminologies Geographic Coordinates : Define the location of spatial entities using a coordinate system (e.g., latitude and longitude). Example: A building at 40.748817° N, 73.985428° W . Geometry Types : Point : Represents a single location (e.g., a well or a bus stop). Line : Represents linear features (e.g., roads, rivers). Polyg...

History of GIS

1. 1832 - Early Spatial Analysis in Epidemiology: - Charles Picquet creates a map in Paris detailing cholera deaths per 1,000 inhabitants. - Utilizes halftone color gradients for visual representation. 2. 1854 - John Snow's Cholera Outbreak Analysis: - Epidemiologist John Snow identifies cholera outbreak source in London using spatial analysis. - Maps casualties' residences and nearby water sources to pinpoint the outbreak's origin. 3. Early 20th Century - Photozincography and Layered Mapping: - Photozincography development allows maps to be split into layers for vegetation, water, etc. - Introduction of layers, later a key feature in GIS, for separate printing plates. 4. Mid-20th Century - Computer Facilitation of Cartography: - Waldo Tobler's 1959 publication details using computers for cartography. - Computer hardware development, driven by nuclear weapon research, leads to broader mapping applications by early 1960s. 5. 1960 - Canada Geograph...

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