I've had to map ditches from 1 m LiDAR derived DEMs of agricultural landscapes before. It's certainly a challenging task to come up with a workflow that is suitable. You're ability to successfully extract a ditch network will depend on a number of factors. For example, are you only interested in roadside ditches? If so, are the roads on embankments (as is ...
I would follow as below in version 9:
Use sample selection algorithm(use brush and set class) and select samples and save these layer as TTA mask
Then you can generate TTA mask from sample
Now you can perform AC.
Yes, there are free object-oriented (segmentation) software available. A few that come to mind are Spring, ITK, Orfeo toolbox and GRASS GIS.
I would however point out that image segmentation is a poor direction to peruse when trying to model fractional cover. A segmentation algorithm is designed to minimize within unit variance and maximize between unit ...
Look at the Layer Values in the Image Object Information window. From here you will be able to determine the pixel value/DN/radiance/reflectance (or whatever your image format is). You will have to add these Layer Values from the Feature View window:
Feature View > Object Features > Layer Values > Mean > [right-click] Layer... > Display in Image Object ...
What about using pyshp? I installed it with pip and what I tried below is pretty much straight out of the README:
>>> import shapefile
>>> sf = shapefile.Reader("/Users/chad/CoalOutcrops.shp")
>>> shapes = sf.shapes()
>>> records = sf.records()
From the sound of your question, it seems like all you really want to do is determine whether or not a shapefile has issues with it (in this case, mismatched records). If you just need to identify those with issues, you don't actually need to count the records in the DBF and Shapefile to determine if it is in error. Here's why:
If you try to run the ...
Solution for your first question:
Create your interested class by right clicking in the 'Class Hierarchy' window and select 'Insert class'. Give the name and select color as you want.
Double click on Contained >> and(min). The 'Insert expression' dialog will open.
Double click on feature you supposed to give a value; for example Object features >&...
To calculate overall accuracy assessment in eCognition using shapefiles you need to do following steps:
First, add shapefiles in to eCognition as thematic layer by modifying the project.
(make sure that points has the same projected coordinates system as your classification image)
Further two steps you can find on ecognition user guide "Creating Samples ...
Classify the broad classes initially.
Create the sub class for example 'Deciduous forest' in class hierarchy windowand Select your broad class say 'Forest' in 'Parent class for display' field.
Or drag and drop the sub class to the main.
When creating rule set for classify the 'Deciduous forest', select 'Forest' in 'Class filter' field. It means, the image ...
You can not directly view the shape file in eCognition. But you can use the shape file in the process.
Import the shape as 'Thematic layer'. If you want to use the shape file to segment the image, use chessboard segmentation with the object size more than your image file and select 'yes' to select which shape file you want to use in the place of 'Thematic ...
Why keep the entire analysis in eCognition? Once you have your image objects derived, export them and run the model in R. You have far more control of the model in R (e.g., model specification, multi-colinearty test, model selection, etc...) and there is no problem fitting a model to all of the data and predicting it to subsets represented by the tiles.
U can do this task in eCognition. The process steps are
Do segmentation; preferably multiresolution (of scale parameter 5) or chessboard segmentation ( of scale parameter 1; this will useful to understand the pixel values)
Now in the Feature View, you can see the Object features >> Layer values >> Mean >> in which your uploaded image layers.
Double click ...
As far as I know there is no native way to do morphological operations in QGIS, but there are two tools in the Processing Toolbox that can help you (I have never used them). Both require to install another software, though:
if you install SAGA, you'll have access to the "Morphological filter" tool, which allows you to perform basic morphological operations (...
I recommend reading Russell Congalton's book Assessing the Accuracy of Remotely Sensed Data: Principles and Practices for a comprehensive analysis of the subject.
Looking at your accuracy assessment, I see two red flags suggesting that the accuracy of the classified product is poor. First, an overall accuracy of 0.4 can be interpreted as saying 6 times out ...
The shapefile format is documented. I would guess the number of records in the shp file does not correspond to the number of records in the dbf file.
The shp file format is documented here. So you could write a program to count the number of shapes. The dbf format is documented in many places and you should be able to find samples for counting rows, e.g. ...
The attached script loops through a directory and checks if the number of shapes matches the number of records for each shapefile.
import arcpy, os, shapefile
from arcpy import env
env.workspace = r"C:\path\to\shapefiles"
Dir = env.workspace
fclist = arcpy.ListFeatureClasses()
for fc in fclist:
myfc = os.path.join(Dir, fc)
sf = shapefile....
There is a sample ruleset on the eCognition Community Ruleset Exchange that shows a work-around. Note that you may need to register to access the ruleset exchange link. The rule-set description states the following:
This zip archive contains example data to train and apply the
classifier algorithm on multiple scenes. At the moment you can do this
This is not the exact answer but could be used as a workaround.
I guess that you are not using the 8 tiles together for memory reason, but your area seems to be quite homogeneous. So you could degrade the resolution of your images (e.g. with a factor 2 or 3) and create a mosaic. Then you train your classifier on the mosaic image and you "save to file" the ...
Instead of trying to import your shapefile into another remote sensing program, I would rather suggest that you export the attributes of your object as csv (there is a csv export in eCognition), then you can run the classification from data analysis softwares (Matlab, R, Numpy...). Then you can join the table to your shapefile in a GIS. Or you can continue ...
Give you've got some experience in coding in Python I'd also recommend looking at the scikit-learn which has a large number of methods available for supervised, unsupervised, and semi-supervised classification, as well as regression.
If as @Jeffrey-Evans says you're looking to perform binary classification you could look at Support Vector Classification, or ...
I have attended training in use of the Semi-Automatic Classification Plugin for QGIS. From what I could see it could be what you are looking for. The whole rig is available for free, so money should not be an issue.
Admittedly I am no expert in these tools, but the author says it includes several classification algorithms (Minimum Distance, Maximum ...
I think I had the same issue. First check that all your classes including unclassified class, are selected in your Class filter (see image). I hope that will solve your problem. I have to say as great as eCognition is, it's interface does tack some time to know
for a great step by step guide look at this page
This is one of the best GIS/RS blogs.
one tip ...
In the trial version of eCognition all functions to export your results have been disabled. Outside of that, no features are unavailable in the trial and there is no time limit.
As such, the short answer is "you do not export images from eCognition Essentials Trial".
I'm pretty certain that it's not possible with eCognition. You could try other software to define the coordinate system;
ArcGIS - "Define Coordinate System" tool
QGIS - If your raster file has no coordinate system, you will be prompted to select one. Then export the layer to a new file.
eCognition is not right place to pre-process input data. Currently, there is no option to georeference, project/reproject input data. The pre-processing operations like projection/reprojection, radiometric/atmospheric corrections etc. are supposed to be readily performed on input image data to work with.
The eCognition merely provides an option to whether ...
If you want to use training polygons as samples in eCognition you need to add them as a TTA mask (Training and Test Area), also this can be used in Accuracy Assessment process.
A TTA mask is a raster layer with training classes as integers. You also need to create a .csv file with Class name, ID, Red, Green and Blue (0-255 value for coloring each training ...
The usual approach to this would be to do it in multiple steps:
Chessboard segmentation with a very large object size, using thematic layers
Assign class, using 'Number of overlaps', into two classes, "Outside AOI" and "Inside AOI"
Do additional segmentation on object level, rather than pixel level, using the "Inside AOI" class only.