To start I have selected my favourite GIS: GRASS. I'm using GRASS 7.1. The segmentation tool that I used is implemented in a module called i.segment, that was developed by Eric Momsen of North Dakota State University, during his Google Summer of Code in 2012.
The segmentation is performed on a group of bands and not on a single band, it means that the user can select the most significant ones in respect to the features she's investigating. The bands can be grouped using the command i.group (Shapiro, 2011).
The algorithm is iterative and can be time-consuming on large images. The parameter "memory" can be used to speed up the process if the machine has a decent memory.
The main parameter worth to play with is the threshold parameter. The larger the threshold, the more objects are merged together. If the image is big, a useful tip is to test the threshold value on a smaller region then apply it to the region at large.
Another fantastic tools comes from Pietro Zambelli and is called i.segment.hierarchical (available in add-ons). It performs hierarchical segmentation for different threshold values, using the result of the previous run as a seed for the next one. It optimizes the computational time by subdividing the image in tiles and processing them in parallel.
The results of segmentation for increasing threshold values are shown hereinafter.
Threshold = 0.01
Threshold = 0.05
Threshold = 0.1
Threshold = 0.2
Image segmentation in GRASS GIS is the process of dividing a raster image into meaningful regions or objects based on spectral similarity and spatial characteristics. It is widely used in remote sensing and geospatial analysis to classify land cover, detect features, and simplify complex imagery. GRASS GIS provides powerful modules such as i.segment, which performs object-based image segmentation by grouping neighboring pixels into homogeneous regions. The algorithm considers parameters like similarity threshold and minimum segment size to control how segments are formed, allowing users to fine-tune the level of detail in the output.
RispondiEliminaImage Segmentation Projects approach is particularly useful for applications like satellite image analysis, urban mapping, and environmental monitoring. Once segmentation is completed, the resulting segments can be further analyzed or classified using additional GRASS GIS tools. For example, segments can be labeled based on land use types or combined with vector data for advanced spatial analysis. By enabling object-based Image Processing Projects For Final Year analysis instead of pixel-based classification, GRASS GIS improves accuracy and provides more meaningful insights from geospatial datasets.
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