Unsupervised classification in remote sensing

Unsupervised classification in remote sensing is a method of grouping pixels in an image based on their spectral characteristics, without the use of prior knowledge or training data. The technique uses algorithms to analyze the patterns and features in the image data, and clusters the pixels into different classes based on their similarity. These classes can then be assigned labels or mapped to a color scheme for interpretation. Unsupervised classification is often used for exploratory data analysis, and can help identify patterns and features that may not be immediately apparent.

For example, an unsupervised classification algorithm might be applied to a satellite image of a forested area. The algorithm would analyze the spectral characteristics of the pixels in the image, such as their red, green, and blue values, and group them into clusters based on their similarity. These clusters might represent different types of vegetation, such as deciduous trees, coniferous trees, and shrubs. The pixels in each cluster would then be assigned a different color, and the resulting image would be a "classification map" that shows the different types of vegetation in the area.

In this way, unsupervised classification can be used to identify and map the different land cover types present in an image, such as water, urban areas, vegetation, bare land, and so on. With this information, scientists and researchers can make more informed decisions about land use, conservation, and resource management.

Comments

Geologic and tectonic framework of the Indian shield

Major Terms and Regions Explained 1. Indian Shield The Indian Shield refers to the ancient, stable core of the Indian Plate made of hard crystalline rocks. It comprises Archean to Proterozoic rocks that have remained tectonically stable over billions of years. Important Geological Features and Regions ▪️ Ch – Chhattisgarh Basin A sedimentary basin part of the Bastar Craton . Contains rocks of Proterozoic age , mainly sedimentary. Important for understanding the evolution of central India. ▪️ CIS – Central Indian Shear Zone A major tectonic shear zone , separating the Bundelkhand and Bastar cratons . It records intense deformation and metamorphism . Acts as a suture zone , marking ancient tectonic collisions. ▪️ GR – Godavari Rift A rift valley formed due to stretching and thinning of the Earth's crust. Associated with sedimentary basins and hydrocarbon resources . ▪️ M – Madras Block An Archean crustal block in...

Geology and Tectonic. Indian Shield

1. Ch (Chattisgarh Basin): Chattisgarh Basin is a geological region in central India known for its sedimentary rock formations. It's important for its mineral resources, including coal and iron ore. 2. CIS (Central Indian Shear Zone): CIS is a tectonic boundary in central India where the Indian Plate interacts with the Eurasian Plate. It's characterized by significant faulting and seismic activity. 3. GR (Godavari Rift): The Godavari Rift is a geological feature associated with the rifting and splitting of the Indian Plate. It's located in the Godavari River basin in southeastern India. 4. M (Madras Block): The Madras Block is a stable continental block in southern India. It's part of the Indian Plate and is not associated with active tectonic processes. 5. Mk (Malanjkhand): Malanjkhand is known for its copper deposits and is one of the largest copper mines in India. 6. MR (Mahanadi Rift): The Mahanadi Rift is a geological feature related to the rifting of the Indian Pl...

Evaluation and Characteristics of Himalayas

Time Period Event / Process Geological Evidence Key Terms & Concepts Late Precambrian – Palaeozoic (>541 Ma – ~250 Ma) India part of Gondwana , north bordered by Cimmerian Superterranes, separated from Eurasia by Paleo-Tethys Ocean . Pan-African granitic intrusions (~500 Ma), unconformity between Ordovician conglomerates & Cambrian sediments. Gondwana, Paleo-Tethys Ocean, Pan-African orogeny, unconformity, granitic intrusions, Cimmerian Superterranes. Early Carboniferous – Early Permian (~359 – 272 Ma) Rifting between India & Cimmerian Superterranes → Neotethys Ocean formation. Rift-related sediments, passive margin sequences. Rifting, Neotethys Ocean, passive continental margin. Norian (210 Ma) – Callovian (160–155 Ma) Gondwana split into East & West; India part of East Gondwana with Australia & Antarctica. Rift basins, oceanic crust formation. Continental breakup, East Gondwana, West Gondwana, oceanic crust. Early Cretaceous (130–125 Ma) India broke fr...

Seismicity and Earthquakes, Isostasy and Gravity

1. Seismicity and Earthquakes in the Indian Subcontinent Key Concept: Seismicity Definition : The occurrence, frequency, and magnitude of earthquakes in a region. In India, seismicity is high due to active tectonic processes . Plate Tectonics 🌏 Indian Plate : Moves northward at about 5 cm/year. Collision with Eurasian Plate : Causes intense crustal deformation , mountain building (Himalayas), and earthquakes. This is an example of a continental-continental collision zone . Seismic Zones of India Classified into Zone II, III, IV, V (Bureau of Indian Standards, BIS). Zone V = highest hazard (e.g., Himalayas, Northeast India). Zone II = lowest hazard (e.g., parts of peninsular India). Earthquake Hazards ⚠️ Himalayas: prone to large shallow-focus earthquakes due to active thrust faulting. Northeast India: complex subduction and strike-slip faults . Examples: 1897 Shillong Earthquake (Magnitude ~8.1) 1950 Assam–Tib...

Vector geoprocessing - Clipping, Erase, identify, Union & Intersection

Think of your vector data (points, lines, polygons) like shapes drawn on a transparent sheet. Geoprocessing is just cutting, joining, or comparing those shapes to get new shapes or information. 1. Clipping ✂️ Imagine you have a big map and you only want to keep a part of it (like cutting a photo into a smaller rectangle). You use another shape (like the boundary of a district) to "clip" and keep only what is inside. Result: Only the data inside the clipping shape remains. 2. Erase 🚫 Opposite of clipping. You remove (erase) the area of one shape from another shape. Example: You have a city map and want to remove all the park areas from it. 3. Identify 🔍 This checks which features from one layer fall inside (or touch) another layer. Example: Identify all the schools inside a flood zone. 4. Union 🤝 Combines two shapes together and keeps everything from both. Works like stacking two transparent sheets and redrawing t...

Vineesh V, Geography

Search This Blog