Vineesh V, Geography

Pluvial Fluvial Nival

Supervised classification is a digital image classification method where the analyst guides the classification process by defining classes of interest and providing representative training samples. The classifier uses these training samples to learn the spectral signatures of each class and then assigns every pixel in the image to the most appropriate class. This method relies heavily on prior knowledge of the study area. How Supervised Classification Works ✔ Step 1: Define Information Classes These are real-world land-cover classes such as: water forest agriculture urban barren land ✔ Step 2: Select Training Areas Training areas (also called ROIs—Regions of Interest) are chosen on the image where the analyst is confident about the land-cover type. ✔ Step 3: Extract Spectral Signatures The classifier calculates: mean variance covariance pixel distribution for each class across different spectral bands. ✔ Step 4: Apply ...

Atmosphere 

Unmanned Aerial Vehicles (UAVs) —commonly called drones —are pilotless aircraft used as remote sensing platforms to acquire very high-resolution geospatial data . They fly at low altitudes (typically 50–300 m), enabling them to record centimeter-level details of the Earth's surface. UAVs are increasingly used in remote sensing because they offer on-demand data acquisition , flexible sensor deployment , and the ability to fly under cloud cover , making them ideal for scientific, environmental, and disaster applications. Characteristics ✔ 1. High-Resolution Data Acquisition UAVs can collect imagery with spatial resolutions up to <1 cm . Suitable for detailed mapping of vegetation, buildings, hazards, and micro-topography. ✔ 2. On-Demand and Rapid Deployment Can be launched quickly anytime data is needed. Extremely useful after floods, landslides, earthquakes , or in inaccessible terrain. ✔ 3. Operational Flexibility Able to fly: in rugged ...

Scattering 

Hybrid classification is a combined classification approach that uses both supervised and unsupervised classification techniques together. It is designed to take advantage of the strengths of each method and to overcome their weaknesses. What Is Hybrid Classification? Hybrid classification blends: Unsupervised classification (e.g., ISODATA, K-means) Supervised classification (e.g., Maximum Likelihood, SVM) ✔ Concept First, an unsupervised algorithm groups pixels into spectral clusters without prior knowledge. These clusters are then labeled or merged into meaningful land-cover classes using supervised training data . ✔ Why use hybrid methods? Unsupervised classification captures natural spectral groupings. Supervised classification improves accuracy by using reference samples. Together, they reduce errors caused by poor training data or complex landscapes. ✔ Key Terminology Cluster : a group of pixels with similar spectral cha...