Convolution in remote sensing

Convolution is a mathematical operation used in remote sensing to combine two functions, typically a signal and a kernel, in order to extract specific features from the data. It is based on the principle of convolution, which states that the output of the operation is the integral of the product of the two functions over a specified interval.

In remote sensing, convolution is often used to apply a spatial filter to an image in order to highlight specific features or patterns. This is typically done by defining a kernel, which is a small matrix of weights that is applied to the image in a sliding window fashion. The kernel is then convolved with the image, resulting in a new image that has been filtered to emphasize specific features.

Convolution is an important tool in remote sensing for a number of reasons. It can be used to enhance image contrast and improve the visual appearance of the data. It can also be used to extract specific features from the data, such as edges or textures, which can be useful for image interpretation and classification.

Convolution is a mathematical operation used in remote sensing to analyze the spatial and spectral characteristics of a target. It is based on the concept of convolving a signal with a function, which produces a new signal that contains information about the original signal and the function used for convolution.

In remote sensing, convolution is typically used to filter or process data in order to extract specific features or patterns. For example, a convolution filter may be used to highlight sharp edges or boundaries in an image, or to enhance the contrast of a spectral signature.

The convolution operation is performed by multiplying each pixel in the original data by a corresponding value in the convolution function, and then summing the results. This produces a new image that contains information about the original data and the convolution function.

Convolution is an important tool in remote sensing for a number of reasons. It can be used to improve image interpretation and classification by highlighting specific features or patterns in the data. It can also be used to reduce noise or improve the spatial and spectral resolution of an image.

Overall, convolution is a valuable tool in remote sensing for analyzing and processing data in order to extract valuable information about a target.

Comments

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...

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...

Accuracy Assessment

Accuracy assessment is the process of checking how correct your classified satellite image is . 👉 After supervised classification, the satellite image is divided into classes like: Water Forest Agriculture Built-up land Barren land But classification is done using computer algorithms, so some areas may be wrongly classified . 👉 Accuracy assessment helps to answer this question: ✔ "How much of my classified map is correct compared to real ground conditions?" Goal The main goal is to: Measure reliability of classified maps Identify classification errors Improve classification results Provide scientific validity to research 👉 Without accuracy assessment, a classified map is not considered scientifically reliable . Reference Data (Ground Truth Data) Reference data is real-world information used to check classification accuracy. It can be collected from: ✔ Field survey using GPS ✔ High-resolution satellite images (Google Earth etc.) ✔ Existing maps or survey reports 🧭 Exampl...

Representation of Spatial and Temporal Relationships

In GIS, spatial and temporal relationships allow the integration of location (the "where") and time (the "when") to analyze phenomena across space and time. This combination is fundamental to studying dynamic processes such as urban growth, land-use changes, or natural disasters. Key Concepts and Terminologies Geographic Coordinates : Define the position of features on Earth using latitude, longitude, or other coordinate systems. Example: A building's location can be represented as (11.6994° N, 76.0773° E). Timestamp : Represents the temporal aspect of data, such as the date or time a phenomenon was observed. Example: A landslide occurrence recorded on 30/07/2024 . Spatial and Temporal Relationships : Describes how features relate in space and time. These relationships can be: Spatial : Topological (e.g., "intersects"), directional (e.g., "north of"), or proximity-based (e.g., "near"). Temporal : Sequential (e....

GIS: Real World and Representations - Modeling and Maps

Geographic Information Systems (GIS) serve as a bridge between the real world and digital representations of geographic phenomena. These representations allow users to store, analyze, and visualize spatial data for informed decision-making. Two key aspects of GIS in this context are modeling and maps , both of which are used to represent real-world geographic features and phenomena in a structured, analyzable format. Let's delve into these concepts, terminologies, and examples in detail. 1. Real World and Representations in GIS Concept: The real world comprises physical, tangible phenomena, such as landforms, rivers, cities, and infrastructure, as well as more abstract elements like weather patterns, population densities, and traffic flow. GIS allows us to represent these real-world phenomena digitally, enabling spatial analysis, decision-making, and visualization. The representation of the real world in GIS is achieved through various models and maps , which simplify...

Vineesh V, Geography

Search This Blog