Edge detection or edge enhancement in remote sensing

-




Edge detection is a common technique used in remote sensing to identify and extract the boundaries or edges of objects in an image. This can be useful for identifying changes in land cover, detecting features such as roads or buildings, and improving the overall interpretation and classification of an image.

Edge detection algorithms typically use mathematical techniques to identify abrupt changes in pixel values within an image. These changes may indicate the presence of an edge or boundary between two distinct objects or areas.

Edge detection is an important tool in remote sensing for a number of reasons. It can be used to identify objects or features in an image, such as roads, buildings, or vegetation. It can also be used to improve image classification by highlighting important features that may not be easily visible in the raw data. Additionally, edge detection can be used to improve image registration and mosaicking, by providing a common reference point for aligning multiple images.

Once the edges are detected, they can be highlighted or enhanced to improve their visibility and make them easier to interpret. This can provide valuable information about the spatial distribution and arrangement of objects in the image, and can be used to support a range of remote sensing applications.

Overall, edge detection is an important technique in remote sensing for improving the interpretation and analysis of imagery, and for extracting valuable information from complex data.




Comments

Post a Comment

Popular posts from this blog

Spatial feature manipulation in remote sensing

-
Image
Spatial feature manipulation in remote sensing refers to the process of altering or modifying the spatial characteristics of a particular feature in an image or data set. This can be done for a variety of reasons, such as to improve the accuracy or clarity of the image, to enhance the interpretability of the data, or to extract specific information from the image. One common method of spatial feature manipulation is resampling, which involves changing the resolution or spatial extent of an image. This can be done to match the resolution of other data sets, to reduce the size of the image for faster processing, or to increase the resolution for greater detail. Another technique is spatial filtering, which involves applying mathematical operations to the image data to remove noise or highlight specific features. This can be done using convolution filters, which apply a pre-defined mathematical function to the image data, or using image enhancement techniques, such as contrast stretching
Read more

Denman Glacier Losing Some of Its Footing

-
Image
Denman Glacier Losing Some of Its Footing Using a combination of satellite sensors, scientists recently found that Denman Glacier has been retreating both above and below the water line. That one glacier in East Antarctica holds as much ice as half of West Antarctica, so scientists are concerned about its stability. From 1996 to 2018, the grounding line along the western flank of Denman Glacier retreated 5.4 kilometers (3.4 miles), according to a new study by scientists from NASA's Jet Propulsion Laboratory and the University of California, Irvine (UCI). The grounding line is the point at which a glacier last touches the seafloor before it begins to float. Behind the grounding line, the ice is attached to the bedrock; beyond it, glacial ice floats on the ocean as an ice tongue or shelf. The retreat of the grounding line at Denman means more of the glacier's underside is now in contact with water that could warm and melt it from below. If the grounding line continues to retreat,
Read more

Difference between Kriging and IDW

-
Image
Difference between Kriging and IDW. What does kriging mean? least squares estimate. Kriging is a type of regression that gives a least squares estimate of data (Remy et. ... Unlike linear regression or inverse distance weighted interpolation, kriging interpolation is based primarily on empirical observations, the observed sample data points, rather than on a pre-assumed model. What is GIS IDW? Inverse Distance Weighted (IDW) is a method of interpolation that estimates cell values by averaging the values of sample data points in the neighborhood of each processing cell. ... Specifying a lower power will give more influence to the points that are farther away, resulting in a smoother surface. Which interpolation method is best? The most used and promising techniques are universal Kriging and linear regression models in combination with Kriging (residual Kriging) or IDW. E.g.: Air temperature data – Kriging is most likely to produce the best estimation of a continuous surface, followed by
Read more