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 dividing the dataset into a specified number of clusters. The analyst can set the desired number of clusters, or if uncertain, can allow the system to determine an optimal number. - Iso Cluster uses the Iterative Self-Organizing Data Analysis Technique Algorithm (ISODATA) to refine these clusters through an iterative process. The ISODATA algorithm analyzes the clusters repeatedly to maximize separation between clusters while minimizing within-cluster variance.
Cluster Refinement: - During each iteration, the algorithm recalculates the center (mean vector) of each cluster based on the pixels within it. - If two clusters are too similar, they may be merged, while larger clusters with high variability may be split into smaller clusters. This adjustment continues until clusters are well-separated and stable.
Final Clustering: - Once the iterative process stabilizes, the final clusters are assigned. Each pixel is labeled with a cluster ID based on its spectral similarity to a particular cluster center. - The analyst interprets these clusters and assigns labels according to the types of land cover or features represented (e.g., identifying a cluster as water, forest, etc.).

When to Use Iso Cluster Classification

Iso Cluster classification is particularly useful in situations where:

The analyst lacks specific knowledge about the classes in the area and wants the algorithm to reveal patterns within the data.
There are complex or diverse land cover types, making it difficult to predefine training sites.
Exploratory analysis is needed to understand the range of spectral characteristics in an unfamiliar region.

Advantages and Limitations

Advantages:

No Training Required: Iso Cluster doesn't need predefined training areas, so it's simpler to apply in regions where ground truth data is unavailable.
Automated Grouping: Automatically identifies patterns and clusters, helping analysts explore the data.
Flexibility: Useful for large datasets and areas with high spectral variability.

Limitations:

Interpretation Required: Iso Cluster outputs unlabeled spectral clusters, so the analyst must interpret and assign meaningful class labels afterward.
Less Precision: Without ground-truthing, the cluster groups may not perfectly match real-world classes.
Dependency on Parameters: The quality of clustering can depend on the parameters set by the analyst, such as the initial number of clusters.

Summary Table

Aspect	Iso Cluster Classification
Type	Unsupervised Classification
Process	Uses ISODATA algorithm for iterative clustering
Training Required	No
Output	Unlabeled spectral clusters
Best Use Case	Exploratory analysis in unknown or complex regions
Advantages	No training data needed, reveals natural patterns in data
Limitations	Requires interpretation, results depend on clustering parameters

PG and Research Department of Geography,
Government College Chittur, Palakkad
https://g.page/vineeshvc

Comments

Platforms in Remote Sensing

In remote sensing, a platform is the physical structure or vehicle that carries a sensor (camera, scanner, radar, etc.) to observe and collect information about the Earth's surface. Platforms are classified mainly by their altitude and mobility : Ground-Based Platforms Definition : Sensors mounted on the Earth's surface or very close to it. Examples : Tripods, towers, ground vehicles, handheld instruments. Applications : Calibration and validation of satellite data Detailed local studies (e.g., soil properties, vegetation health, air quality) Strength : High spatial detail but limited coverage. Airborne Platforms Definition : Sensors carried by aircraft, balloons, or drones (UAVs). Altitude : A few hundred meters to ~20 km. Examples : Airplanes with multispectral scanners UAVs with high-resolution cameras or LiDAR High-altitude balloons (stratospheric platforms) Applications : Local-to-regional mapping ...

Optical Sensors in Remote Sensing

1. What Are Optical Sensors? Optical sensors are remote sensing instruments that detect solar radiation reflected or emitted from the Earth's surface in specific portions of the electromagnetic spectrum (EMS) . They mainly work in: Visible region (0.4–0.7 µm) Near-Infrared – NIR (0.7–1.3 µm) Shortwave Infrared – SWIR (1.3–3.0 µm) Thermal Infrared – TIR (8–14 µm) — emitted energy, not reflected Optical sensors capture spectral signatures of surface features. Each object reflects/absorbs energy differently, creating a unique spectral response pattern . a) Electromagnetic Spectrum (EMS) The continuous range of wavelengths. Optical sensing uses solar reflective bands and sometimes thermal bands . b) Spectral Signature The unique pattern of reflectance or absorbance of an object across wavelengths. Example: Vegetation reflects strongly in NIR Water absorbs strongly in NIR and SWIR (appears dark) c) Radiance and Reflectance Radi...

Types of Remote Sensing

Remote Sensing means collecting information about the Earth's surface without touching it , usually using satellites, aircraft, or drones . There are different types of remote sensing based on the energy source and the wavelength region used. 🛰️ 1. Active Remote Sensing 📘 Concept: In active remote sensing , the sensor sends out its own energy (like a signal or pulse) to the Earth's surface. The sensor then records the reflected or backscattered energy that comes back from the surface. ⚙️ Key Terminology: Transmitter: sends energy (like a radar pulse or laser beam). Receiver: detects the energy that bounces back. Backscatter: energy that is reflected back to the sensor. 📊 Examples of Active Sensors: RADAR (Radio Detection and Ranging): Uses microwave signals to detect surface roughness, soil moisture, or ocean waves. LiDAR (Light Detection and Ranging): Uses laser light (near-infrared) to measure elevation, vegetation...

Resolution of Sensors in Remote Sensing

Spatial Resolution 🗺️ Definition : The smallest size of an object on the ground that a sensor can detect. Measured as : The size of a pixel on the ground (in meters). Example : Landsat → 30 m (each pixel = 30 × 30 m on Earth). WorldView-3 → 0.31 m (very detailed, you can see cars). Fact : Higher spatial resolution = finer details, but smaller coverage. Spectral Resolution 🌈 Definition : The ability of a sensor to capture information in different parts (bands) of the electromagnetic spectrum . Measured as : The number and width of spectral bands. Types : Panchromatic (1 broad band, e.g., black & white image). Multispectral (several broad bands, e.g., Landsat with 7–13 bands). Hyperspectral (hundreds of very narrow bands, e.g., AVIRIS). Fact : Higher spectral resolution = better identification of materials (e.g., minerals, vegetation types). Radiometric Resolution 📊 Definition : The ability of a sensor to ...

Radar Sensors in Remote Sensing

Radar sensors are active remote sensing instruments that use microwave radiation to detect and measure Earth's surface features. They transmit their own energy (radio waves) toward the Earth and record the backscattered signal that returns to the sensor. Since they do not depend on sunlight, radar systems can collect data: day or night through clouds, fog, smoke, and rain in all weather conditions This makes radar extremely useful for Earth observation. 1. Active Sensor A radar sensor produces and transmits its own microwaves. This is different from optical and thermal sensors, which depend on sunlight or emitted heat. 2. Microwave Region Radar operates in the microwave region of the electromagnetic spectrum , typically from 1 mm to 1 m wavelength. Common radar frequency bands: P-band (70 cm) L-band (23 cm) S-band (9 cm) C-band (5.6 cm) X-band (3 cm) Each band penetrates and interacts with surfaces differently: Lo...

Vineesh V, Geography

Search This Blog