Kerala Nipah outbreak LIVE UPDATES: Death toll rises to 10, two critical

നിപാ വൈറസ് ബാധ നിയന്ത്രിക്കുന്നതിന് എല്ലാ നടപടികളും സംസ്ഥാന സർക്കാർ സ്വീകരിച്ചിട്ടുണ്ട് എന്നു മുഖ്യമന്ത്രി അറിയിച്ചു. മുൻകരുതൽ എടുക്കുകയും ജാഗ്രത പുലർത്തുകയും വേണമെങ്കിലും പരിഭ്രാന്തിക്ക് ഒരടിസ്ഥാനവുമില്ലന്ന് അദ്ദേഹം പറഞ്ഞു. പരിഭ്രാന്തി പരത്തുന്ന പ്രചാരണം സമൂഹമാധ്യമങ്ങളിൽ നടക്കുന്നത് ദൗര്‍ഭാഗ്യകരമാണെന്നും ഇവ ശ്രദ്ധയിൽ പെട്ടിട്ടുണ്ട് എന്നും അദ്ദേഹം പറഞ്ഞു. ഇത്തരം പ്രചാരണങ്ങളിൽ നിന്ന് എല്ലാവരും മാറിനിൽക്കണമെന്ന് അഭ്യർത്ഥിക്കുന്നു. കേരളത്തിന് പുറത്തുനിന്ന് അടിസ്ഥാനമില്ലാത്ത ഒരുപാട് പോസ്റ്റുകൾ സമൂഹമാധ്യമങ്ങളിൽ വരുന്നുണ്ട്. ഇത്തരം പ്രചാരണങ്ങളിൽ നാം കുടുങ്ങിപ്പോകരുത്. ഭീതിയുണ്ടാക്കുന്ന രീതിയിൽ സമൂഹ മാധ്യമങ്ങളിലൂടെ പ്രചാരണം നടത്തുന്നത് കേരളത്തിന്റെ പൊതുതാൽപര്യത്തിന് ഹാനികരമാണെന്ന് തിരിച്ചറിയണമെന്നും മുഖ്യമന്ത്രി അഭ്യർത്ഥിച്ചു.

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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...

Geometric Correction

When satellite or aerial images are captured, they often contain distortions (errors in shape, scale, or position) caused by many factors — like Earth's curvature, satellite motion, terrain height (relief), or the Earth's rotation . These distortions make the image not properly aligned with real-world coordinates (latitude and longitude). 👉 Geometric correction is the process of removing these distortions so that every pixel in the image correctly represents its location on the Earth's surface. After geometric correction, the image becomes geographically referenced and can be used with maps and GIS data. Types 1. Systematic Correction Systematic errors are predictable and can be modeled mathematically. They occur due to the geometry and movement of the satellite sensor or the Earth. Common systematic distortions: Scan skew – due to the motion of the sensor as it scans the Earth. Mirror velocity variation – scanning mirror moves at a va...

Image Transformation

In remote sensing, image transformation is used to create new images by manipulating existing ones, enhancing the information extracted from each pixel. Multi-image manipulation and other transformations allow analysts to highlight specific features, compare time-series data, or reduce data dimensions for better interpretation. 1. Multi-Image Manipulation This technique involves applying mathematical operations to multiple images, typically from different bands, dates, or sensors, to highlight specific features or changes. Common methods include: a. Image Addition Purpose : Highlights areas of similarity or enhances features when the same objects appear in multiple images. How it Works : By adding pixel values from two images, the output image emphasizes areas where brightness is high in both. Example : Enhancing overall brightness or detecting seasonal changes by adding images from different times. b. Image Subtraction Purpose : Identifies differences between images, useful for chan...

Spatial Feature Manipulation and Filtering

1. Spatial Feature Manipulation: Spatial Frequency Spatial frequency describes how quickly brightness changes across an image. It can reveal different levels of detail, based on how often these changes occur: High Spatial Frequency : Areas where brightness changes quickly, like edges, fine textures, or detailed patterns. These parts contain high detail but can also include noise. Low Spatial Frequency : Areas with gradual changes in brightness, like smooth surfaces or broad homogeneous areas. These regions are usually featureless, such as water bodies or large agricultural fields. Zero Spatial Frequency : Represents a completely uniform area with no brightness variation. This area is very smooth and has no texture or detail. By manipulating spatial frequencies, we can enhance or suppress certain features in an image. Spatial filtering is one of the primary methods to perform this manipulation. 2. Spatial Filtering and Spatial Domain Spatial filtering is the process of applying mathe...

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...

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