Neighbourhood Operations

Neighbourhood Operations in GIS?

In GIS and raster data, neighbourhood operations look at a group of nearby pixels (not just one) to understand or change a pixel's value.
Think of it like checking what's around a house before deciding what color to paint it!

Why "Neighbourhood"?

Each pixel has "neighbours" (just like how your house has nearby houses).
Neighbourhood operations check these nearby pixels and do some calculation to get a new value.

1. Aggregations (Summarizing Nearby Values)

Aggregation means combining values of several pixels into one.
We do this to:

Find the average of surrounding pixels
Find the minimum or maximum value
Smooth the map (make it less rough)

🧒🏻 Example: Imagine checking the test scores of 9 students sitting around you and finding the average score. That's aggregation!

2. Filtering Techniques

Filtering is used to improve or highlight features in a raster image, just like filters on Instagram or Snapchat!

There are two main types:

a. Low Pass Filter (Smoothing Filter)

This filter smooths the image by removing sharp edges or noise.

It takes the average of surrounding pixels.
Used to make maps look cleaner and softer.

🧒🏻 Example: Blurring a photo to hide pimples. The map becomes "soft".

b. High Pass Filter (Sharpening Filter)

This filter sharpens the image and highlights edges or sudden changes.

It enhances details like roads, rivers, or building edges.
It shows where things change quickly.

🧒🏻 Example: Making a photo sharper so you can see your hair strands clearly!

3. Edge Enhancement

This is used to highlight the edges (boundaries) between different features on a map.

It helps us see where one land type ends and another begins.
Useful for detecting boundaries, like the edge of a forest or a building.

🧒🏻 Example: Drawing dark outlines around cartoon characters to make them stand out.

Term	Meaning (Simple)	Example
Aggregation	Combines nearby values (average, max, min)	Avg test marks of nearby friends
Low Pass Filter	Smooths the map, reduces noise	Blurring a photo
High Pass Filter	Sharpens details, highlights edges	Making a photo clearer
Edge Enhancement	Highlights boundaries between features	Outlining shapes in a drawing

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...

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...

Vineesh V, Geography

Search This Blog