Minimum distance. Gaussian maximum likelihood . Parallelepiped

Minimum distance to means classification is a supervised classification technique in remote sensing that works by dividing the data into a number of classes based on the mean value of each class. The algorithm works as follows:

First, the mean value of each class is calculated. This is done by taking the average of all the data points in each class.

Next, for each data point, the distance to the mean of each class is calculated. This is done using a distance metric, such as Euclidean distance.

The data point is then assigned to the class with the minimum distance to the mean.

This process is repeated for all data points in the dataset.

Minimum distance to means classification is simple and easy to implement, but it can be sensitive to noise and outliers in the data. It is generally not as accurate as more complex classification algorithms, such as support vector machines or neural networks.

Gaussian maximum likelihood classification is a method of image analysis in remote sensing that involves estimating the probability density function (PDF) of each class in the image and then classifying each pixel based on the class with the highest PDF value.

The PDF of a class is a statistical model that describes the distribution of pixel values within that class. In the case of Gaussian maximum likelihood classification, the PDF is assumed to be a Gaussian, or normal, distribution. This means that the pixel values within the class are assumed to be normally distributed around a mean value, with a certain standard deviation.

To classify each pixel, the PDFs of all classes are calculated using the mean and standard deviation values estimated from the training data. The class with the highest PDF value is then assigned to the pixel.

One advantage of Gaussian maximum likelihood classification is that it can handle continuous variables, such as spectral reflectance values, which can be difficult to classify using other methods. It is also relatively simple to implement and can be easily modified to incorporate additional features or constraints.

However, Gaussian maximum likelihood classification has some limitations. It assumes that the classes are normally distributed, which may not always be the case in real-world data. It is also sensitive to the presence of mixed pixels, or pixels that contain multiple types of land cover.

In remote sensing, a parallelepiped is a three-dimensional model used to classify spectral data obtained from a remote sensor. It is a parallelogram with opposite sides parallel, and all its faces are parallelograms.

Parallelepiped classification is a method of image analysis that involves dividing the image data into a set of smaller parallelepipeds, or "bins," and then assigning a class label to each bin based on the characteristics of the pixels within it. This can be done using various techniques, such as k-means clustering or decision tree analysis.

One advantage of parallelepiped classification is that it can be used to analyze large volumes of data quickly, as the bins can be processed in parallel. It is also relatively simple to implement and can be easily modified to incorporate additional features or constraints.

However, parallelepiped classification has some limitations. It can be sensitive to the size and orientation of the bins, and the choice of bin size and orientation can significantly affect the accuracy of the classification. It can also be sensitive to the presence of mixed pixels, or pixels that contain multiple types of land cover.

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...

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...

History of GIS

The history of Geographic Information Systems (GIS) is rooted in early efforts to understand spatial relationships and patterns, long before the advent of digital computers. While modern GIS emerged in the mid-20th century with advances in computing, its conceptual foundations lie in cartography, spatial analysis, and thematic mapping. Early Roots of Spatial Analysis (Pre-1960s) One of the earliest documented applications of spatial analysis dates back to 1832 , when Charles Picquet , a French geographer and cartographer, produced a cholera mortality map of Paris. In his report Rapport sur la marche et les effets du choléra dans Paris et le département de la Seine , Picquet used graduated color shading to represent cholera deaths per 1,000 inhabitants across 48 districts. This work is widely regarded as an early example of choropleth mapping and thematic cartography applied to epidemiology. A landmark moment in the history of spatial analysis occurred in 1854 , when John Snow inv...

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...

Vineesh V, Geography

Search This Blog