Generating LiDAR DEMs from unclassified point clouds with:
MCC-LIDAR - Multiscale Curvature Classification (MCC) algorithm.
(supports LAS versions 1.1 to 1.3)
MCC-LIDAR is a command-line tool for processing discrete-return LIDAR data in forested environments (Evans & Hudak, 2007).
a) unclassified point cloud.
b) ground returns ...
"the range is then divided by the number of classes"
" for visualizing continuous data that is not distributed normally"
"method designed to optimize the arrangement of a ...
As far as pixel-based classification is concerned, you are spot on. Each pixel is an n-dimensional vector and will be assigned to some class according to some metric, whether using Support Vector Machines, MLE, some kind of knn classifier, etc.
As far as region based classifiers are concerned, though, there have been huge developments in the last few years, ...
I have tried supervised classification in ArcGIS.
Firstly I would say that it is not the best software for classification.
As I did it, you can create training sites as points. Just create a shapefile (or geodatabase), add Integer field, click points over your image and assign classes as numbers. (I think you can also use polygon shapefile).
For a fantastic way to detect, visualize, and report your findings to the public, check out the Landtrendr (Landsat-based Detection of Trends in Disturbance and Recovery) program from OSU. The Landtrendr program is one of the most exciting recent developments in change detection research. There is very good documentation on the methods, and Landtrendr code ...
You might want to try Orfeo Toolbox.
OTB is based on the medical image processing library ITK and offers
particular functionalities for remote sensing image processing in
general and for high spatial resolution images in particular. Targeted
algorithms for high resolution optical images (SPOT, Quickbird,
Worldview, Landsat, Ikonos), hyperspectral ...
There is a considerable body of literature on individual crown detection in spectral and lidar data. Methods wise, perhaps start with:
Falkowski, M.J., A.M.S. Smith, P.E. Gessler, A.T. Hudak, L.A. Vierling and J.S. Evans. (2008). The influence of conifer forest canopy cover on the accuracy of two individual tree measurement algorithms using lidar data. ...
You need to use Rule based style to set the scale for primary, secondary and tertiary network, as you can see below (but with different data):
You can double-click each styled label to get more details:
Your understanding is generally correct, however, there are dangers in your description of the object based classification - the term 'object' refers to the group of pixels, not whether or not it contains a given object.
Furthermore, the central goal in a object-based classification is not to have segments of equal size, but to have "chopped"/segmented the ...
LASTools can perform a ground classification using "lasground" and then can perform some limited feature classification using "lasclassify". The performance and quality of feature classification in point clouds is strongly influenced by the type of landscape collected. Some landscapes just do not lend themselves to acceptable automated results. The best ...
At the start of your question you ask about going from 32 bit to 8 bit and at the end you ask about going the other way, so this will be a generic answer.
Most of the GDAL functions allow you to specify the pixel depth with the commandline tag -ot (for instance see the documentation on gdal_translate or gdal_rasterize). The -ot switch can take the values ...
I think that LasTools might suit your needs, see LASGround. The license is a bit funny depending on what tools. The tools can be downloaded and evaluated prior to purchase; also the product is relatively inexpensive.
The term spectral signature refers to the relationship between the wavelength (or frequency) of electromagnetic radiation and the reflectance of the surface. The signature is affected by several things including the material composition and structure. Some parts of the EMR spectrum, such as the microwave region, are more sensitive to surface structure than ...
The Supplementary Materials (SM) for the Science article provides references to a number of different journal-articles that outline various parts of the methodology.
The SM can be found here
Extending the time-series to include Landsat-5 (and potentially Landsat-8 to make the methodology something that can be rerun "easily") data will be a challenging task,...
The intensity image should be used for calibration and subsequent classification of geophysical features. To radiometrically calibrate the intensity, use the Calibrate tool in the Sentinel-1 Toolbox (SAR Processing > Radiometric > Calibrate). The S-1 Level 1 GRD product includes several Look-Up Tables (LUTs) to convert intensity values into sigma or gamma ...
In terms of something akin to a spectral signature, the only way would be through the return intensity values, which are rarely calibrated. Unfortunately, there is really nothing expected in the characteristics of the return intensity that would separate rock and soil, the answer really is that this is not a likely outcome.
Now, if you used surface texture ...
You may apply a reclassification by right-clicking on the layer in the Layers Panel and then clicking on Properties: from the dialog that appears, go to Style and then set these parameters (you can adapt them following your specific needs):
Please note that in step No.5 you set Equal interval as Mode, but you only do this for the possibility of editing the ...
those are different things.
Image classification is the process of creating a thematic image where each pixel is assigned a number representing a class (can include the class 'unclassified'). In an aerial image the classes can be soil, vegetation, water etc. image classification algorithms examples are k-means or ISO-DATA.
Pattern recognition is the ...
As troubleshot by @Paul, the error message is being triggered because you have placed your *.gsg file inside of a file geodatabase folder (*.gdb).
It seems like the Maximum Likelihood Classification tool is getting confused by this.
However, the error can be easily avoided by ensuring that your *.gsg file is NOT inside of a file geodatabase folder (*.gdb).
A spectral signature is some measurable quantity (e.g., reflectivity, emissivity), which varies as a function of wavelength and can be used to identify a material. To obtain a signature, the quantity must be measured at a sufficient number of wavelengths (and at fine enough spectral resolution) such that the material can be discriminated from other materials....
Classification algorithms such as Maximum Liklihood, random forests, and SVM are statistical methods for grouping data. These data may be words, colors, sounds or anything you can imagine. In a remote sensing context, these algorithms are used to group pixels or image objects (segments) based on statistical properties, or spectral profiles.
To answer ...
The general name for the operation that will allow you to compare two classified images is cross tabulation or what is sometimes called a contingency table. This will allow you to calculate change in class values. In the SAGA toolbox of QGIS there is a tool called Cross-classification and tabulation that will perform this operation.
I am the main developer of MGET.
The first step in your problem is to obtain values of the covariates that you will use to fit the model to your 90 GPS points. It sounds like you want to use the 8 bands as your covariates. You need to add 8 fields to your shapefile (one for each band) and populate them using a tool such as Extract Multi Values to Points ...
The problem is you need to be escaping the pathnames. In Python (and many other programming languages) the single backslash \ is an escape character. See this page for an explanation.
in the path "C:\Project\GIS\Grayscale\aug1951_clip_bw.gsg", the \a is an ascii Bell. You can see all the string literals here.
You can properly format your pathnames many ...
Creating landuse database, you need sensor technologies to detect and classify objects. i think you already know that:
There are two main types of remote sensing: passive remote sensing and
active remote sensing. Passive sensors detect natural radiation that
is emitted or reflected by the object or surrounding areas. Reflected
sunlight is the most ...
First of all I should mention that this question is addressing very limited space, though important question too.
The first thing that comes to my mind on this subject that you can consider temporal information in short time interval. if certain values are changed in certain areas, it may be easier to detect snow.
and second solution is here from National ...
It is really is worth the time to learn the code interface. Here is some annotated code for specifying a simple Random Forest image classification using spatial data.
# Add required libraries
# SET WORKING DIRECTORY
# Read point shapefile with ...