Maximum Likelihood Classification and Principal Components Tools
Maximum Likelihood Classification
أداة تصنيف الاحتمالية القصوى
ArcMap
ArcGIS
How to use Maximum
Likelihood Classification Tool in Arc Toolbox??
كيفية استخدام أداة تصنيف الاحتمالية القصوى ؟؟
Path to access the toolمسار الوصول الى الأداة
:
Maximum Likelihood Classification Tool, Multivariate Toolset,
Spatial Analyst Tools Toolbox
Maximum Likelihood Classification
Performs a maximum
likelihood classification on a set of raster bands and creates a classified
raster as output.
يقوم بتصنيف احتمالية قصوى على مجموعة من نطاقات
البيانات النقطية وينشئ البيانات النقطية المصنفة كمخرجات.
1.
Input raster bands أدخل نطاقات
البيانات النقطية
The input raster bands.
While the bands can be
integer or floating point type, the signature file only allows integer class
values.
نطاقات الإدخال النقطية.
بينما يمكن أن تكون النطاقات عددًا صحيحًا أو من
نوع النقطة العائمة ، فإن ملف التوقيع يسمح فقط بقيم فئة عدد صحيح.
Input signature file
The input signature file whose class signatures are used by the maximum
likelihood classifier.
A .gsg extension is required.
Output classified raster
The output classified raster.
It will be of integer type.
Reject fraction (optional)
Choose a reject fraction, which determines whether a cell will be
classified based on its likelihood of being correctly assigned to one of the
classes. Cells whose likelihood of being correctly assigned to any of the
classes is lower than the reject fraction will be given a value of NoData in
the output classified raster.
The default is 0.0, which means that every cell will be classified.
A priori probability weighting
(optional)
Specifies how a priori probabilities will be determined.
· EQUAL— All classes will have the same a priori probability.
· SAMPLE— A priori probabilities will be proportional to the number of cells
in each class relative to the total number of cells sampled in all classes in
the signature file.
· FILE—The a priori probabilities will be assigned to each class from an
input ASCII a priori probability file.
Input a priori probability file
(optional)
A text file containing a priori probabilities for the input signature
classes.
An input for the a priori probability file is only required when the FILE
option is used.
The extension for the a priori file can be .txt or .asc.
Output confidence raster
(optional)
The output confidence raster dataset shows the certainty of the
classification in 14 levels of confidence, with the lowest values representing
the highest reliability. If there are no cells classified at a particular
confidence level, that confidence level will not be present in the output
confidence raster.
It will be of integer type.
2.
Input signature file أدخل ملف التوقيع
The input signature file
whose class signatures are used by the maximum likelihood classifier.
A .gsg extension is
required.
ملف توقيع الإدخال الخاص به يتم استخدام توقيعات
الفئة الخاصة به بواسطة مصنف الاحتمالية القصوى.
مطلوب امتداد .gsg.
Output classified raster
The output classified raster.
It will be of integer type.
Reject fraction (optional)
Choose a reject fraction, which determines whether a cell will be
classified based on its likelihood of being correctly assigned to one of the
classes. Cells whose likelihood of being correctly assigned to any of the
classes is lower than the reject fraction will be given a value of NoData in
the output classified raster.
The default is 0.0, which means that every cell will be classified.
A priori probability weighting
(optional)
Specifies how a priori probabilities will be determined.
· EQUAL— All classes will have the same a priori probability.
· SAMPLE— A priori probabilities will be proportional to the number of cells
in each class relative to the total number of cells sampled in all classes in
the signature file.
· FILE—The a priori probabilities will be assigned to each class from an
input ASCII a priori probability file.
Input a priori probability file
(optional)
A text file containing a priori probabilities for the input signature
classes.
An input for the a priori probability file is only required when the FILE
option is used.
The extension for the a priori file can be .txt or .asc.
Output confidence raster
(optional)
The output confidence raster dataset shows the certainty of the
classification in 14 levels of confidence, with the lowest values representing
the highest reliability. If there are no cells classified at a particular
confidence level, that confidence level will not be present in the output
confidence raster.
It will be of integer type.
3.
Output classified raster البيانات
النقطية المصنفه المخرجة
The output classified
raster.
It will be of integer
type.
الناتج البيانات النقطية المصنفه.
سيكون من نوع عدد صحيح.
Reject fraction (optional)
Choose a reject fraction, which determines whether a cell will be
classified based on its likelihood of being correctly assigned to one of the
classes. Cells whose likelihood of being correctly assigned to any of the
classes is lower than the reject fraction will be given a value of NoData in
the output classified raster.
The default is 0.0, which means that every cell will be classified.
A priori probability weighting
(optional)
Specifies how a priori probabilities will be determined.
· EQUAL— All classes will have the same a priori probability.
· SAMPLE— A priori probabilities will be proportional to the number of cells
in each class relative to the total number of cells sampled in all classes in
the signature file.
· FILE—The a priori probabilities will be assigned to each class from an
input ASCII a priori probability file.
Input a priori probability file
(optional)
A text file containing a priori probabilities for the input signature
classes.
An input for the a priori probability file is only required when the FILE
option is used.
The extension for the a priori file can be .txt or .asc.
Output confidence raster
(optional)
The output confidence raster dataset shows the certainty of the
classification in 14 levels of confidence, with the lowest values representing
the highest reliability. If there are no cells classified at a particular
confidence level, that confidence level will not be present in the output
confidence raster.
It will be of integer type.
4.
Reject fraction (optional) رفض
الكسر (اختياري)
Choose a reject
fraction, which determines whether a cell will be classified based on its
likelihood of being correctly assigned to one of the classes. Cells whose
likelihood of being correctly assigned to any of the classes is lower than the
reject fraction will be given a value of NoData in the output classified
raster.
The default is 0.0,
which means that every cell will be classified.
اختر كسر رفض ، والذي يحدد ما إذا كان سيتم تصنيف
الخلية بناءً على احتمالية تعيينها بشكل صحيح إلى إحدى الفئات. الخلايا التي يكون
احتمال تعيينها بشكل صحيح لأي من الفئات أقل من كسر الرفض ، سيتم منحها قيمة NoData في
البيانات النقطية المصنفة للمخرجات.
القيمة الافتراضية هي 0.0 ، مما يعني أنه سيتم
تصنيف كل خلية.
A priori probability weighting
(optional)
Specifies how a priori probabilities will be determined.
· EQUAL— All classes will have the same a priori probability.
· SAMPLE— A priori probabilities will be proportional to the number of cells
in each class relative to the total number of cells sampled in all classes in
the signature file.
· FILE—The a priori probabilities will be assigned to each class from an
input ASCII a priori probability file.
Input a priori probability file
(optional)
A text file containing a priori probabilities for the input signature
classes.
An input for the a priori probability file is only required when the FILE
option is used.
The extension for the a priori file can be .txt or .asc.
Output confidence raster
(optional)
The output confidence raster dataset shows the certainty of the
classification in 14 levels of confidence, with the lowest values representing
the highest reliability. If there are no cells classified at a particular
confidence level, that confidence level will not be present in the output
confidence raster.
It will be of integer type.
5.
A priori probability weighting (optional) ترجيح الاحتمال المسبق (اختياري)
Specifies how a priori
probabilities will be determined.
·
EQUAL— All classes will have the same a priori probability.
·
SAMPLE— A priori probabilities will be proportional to the number
of cells in each class relative to the total number of cells sampled in all
classes in the signature file.
·
FILE—The a priori probabilities will be assigned to each class
from an input ASCII a priori probability file.
يحدد كيف سيتم تحديد الاحتمالات المسبقة.
• EQUAL -
سيكون لجميع الفئات نفس الاحتمال المسبق.
• SAMPLE -
ستكون الاحتمالات المسبقة متناسبة مع عدد الخلايا في كل فئة بالنسبة إلى العدد
الإجمالي للخلايا التي تم أخذ عينات منها في جميع الفئات في ملف التوقيع.
• FILE -
سيتم تعيين احتمالات مسبقة لكل فئة من إدخال ASCII ملف
احتمالية مسبقة.
Input a priori probability file
(optional)
A text file containing a priori probabilities for the input signature
classes.
An input for the a priori probability file is only required when the FILE
option is used.
The extension for the a priori file can be .txt or .asc.
Output confidence raster
(optional)
The output confidence raster dataset shows the certainty of the
classification in 14 levels of confidence, with the lowest values representing
the highest reliability. If there are no cells classified at a particular
confidence level, that confidence level will not be present in the output
confidence raster.
It will be of integer type.
6.
Input a priori probability file (optional) أدخل ملف احتمالية مسبقة (اختياري)
A text file containing a
priori probabilities for the input signature classes.
An input for the a
priori probability file is only required when the FILE option is used.
The extension for the a
priori file can be .txt or .asc.
ملف نصي يحتوي على احتمالات مسبقة لفئات توقيع
الإدخال.
مطلوب إدخال لملف الاحتمال المسبق فقط عند استخدام
خيار FILE.
يمكن أن يكون الامتداد لملف بدهي .txt أو .asc.
Output confidence raster
(optional)
The output confidence raster dataset shows the certainty of the
classification in 14 levels of confidence, with the lowest values representing
the highest reliability. If there are no cells classified at a particular
confidence level, that confidence level will not be present in the output
confidence raster.
It will be of integer type.
7.
Output confidence raster (optional) النقطية الثقة المخرجة (اختياري)
The output confidence
raster dataset shows the certainty of the classification in 14 levels of
confidence, with the lowest values representing the highest reliability. If
there are no cells classified at a particular confidence level, that confidence
level will not be present in the output confidence raster.
It will be of integer
type.
تُظهر مجموعة البيانات النقطية لثقة المخرجات يقين
التصنيف في 14 مستوى من الثقة ، مع أقل القيم التي تمثل أعلى موثوقية. إذا لم تكن
هناك خلايا مصنفة عند مستوى ثقة معين ، فلن يكون مستوى الثقة هذا موجودًا في
البيانات النقطية لثقة المخرجات.
سيكون من نوع عدد صحيح.
Principal Components
أداة المكونات الرئيسية
ArcMap
ArcGIS
How to use Principal
Components Tool in Arc Toolbox??
كيفية استخدام أداة المكونات الرئيسية ؟؟
Path to access the toolمسار الوصول الى الأداة
:
Principal Components Tool, Multivariate Toolset, Spatial Analyst Tools Toolbox
Principal Components
Performs Principal
Component Analysis (PCA) on a set of raster bands and generates a single
multiband raster as output.
ينفذ تحليل المكونات الرئيسية (PCA) على
مجموعة من نطاقات البيانات النقطية وينشئ خطًا نقطيًا متعدد النطاقات كإخراج.
1.
Input raster bands أدخل نطاقات
البيانات النقطية
The input raster bands.
They can be integer or
floating point type.
نطاقات الإدخال النقطية.
يمكن أن تكون من نوع عدد صحيح أو فاصلة عشرية.
Output multiband raster
The output multiband raster dataset.
If all of the input bands are integer type, the output raster bands will
be integer. If any of the input bands are floating point, the output will be
floating point.
If the output is an Esri Grid raster, the name must have less than 10
characters.
Number of Principal components
(optional)
Number of principal components.
The number must be greater than zero and less than or equal to the total
number of input raster bands.
The default is the total number of rasters in the input.
Output data file (optional)
Output ASCII data file storing principal component parameters.
The output data file records the correlation and covariance matrices, the
eigenvalues and eigenvectors, the percent variance each eigenvalue captures,
and the accumulative variance described by the eigenvalues.
The extension for the output file can be .txt or .asc.
2.
Output multiband raster أدخل
النقطية المتعددة
The output multiband
raster dataset.
If all of the input
bands are integer type, the output raster bands will be integer. If any of the
input bands are floating point, the output will be floating point.
If the output is an Esri
Grid raster, the name must have less than 10 characters.
مجموعة البيانات النقطية متعددة النطاقات الناتجة.
إذا كانت جميع نطاقات الإدخال من نوع عدد صحيح ،
فستكون نطاقات الإخراج النقطية عددًا صحيحًا. إذا كان أي من نطاقات الإدخال نقطة
عائمة ، فسيكون الناتج نقطة عائمة.
إذا كان الإخراج عبارة عن شبكة نقطية Esri ، يجب
أن يحتوي الاسم على أقل من 10 أحرف.
Number of Principal components
(optional)
Number of principal components.
The number must be greater than zero and less than or equal to the total
number of input raster bands.
The default is the total number of rasters in the input.
Output data file (optional)
Output ASCII data file storing principal component parameters.
The output data file records the correlation and covariance matrices, the
eigenvalues and eigenvectors, the percent variance each eigenvalue captures,
and the accumulative variance described by the eigenvalues.
The extension for the output file can be .txt or .asc.
3.
Number of Principal components (optional) عدد المكونات الرئيسية (اختياري)
Number of principal
components.
The number must be
greater than zero and less than or equal to the total number of input raster
bands.
The default is the total
number of rasters in the input.
عدد المكونات الرئيسية.
يجب أن يكون الرقم أكبر من الصفر وأقل من أو يساوي
العدد الإجمالي لنطاقات البيانات النقطية المدخلة.
الافتراضي هو العدد الإجمالي للنقطيات في الإدخال.
Output data file (optional)
Output ASCII data file storing principal component parameters.
The output data file records the correlation and covariance matrices, the
eigenvalues and eigenvectors, the percent variance each eigenvalue captures,
and the accumulative variance described by the eigenvalues.
The extension for the output file can be .txt or .asc.
4.
Output data file (optional) ملف البيانات
المخرج (اختياري)
Output ASCII data file
storing principal component parameters.
The output data file
records the correlation and covariance matrices, the eigenvalues and
eigenvectors, the percent variance each eigenvalue captures, and the
accumulative variance described by the eigenvalues.
The extension for the
output file can be .txt or .asc.
إخراج ملف بيانات ASCII يخزن
معلمات المكون الأساسي.
يسجل ملف بيانات المخرجات مصفوفات الارتباط
والتغاير ، وقيم eigenvalues والمتجهات الذاتية ،
ونسبة التباين المئوية التي تلتقطها كل قيمة ذاتية ، والتباين التراكمي الموصوف
بواسطة قيم eigenvalues.
يمكن أن يكون امتداد ملف الإخراج .txt أو .asc.
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