Train Random Trees Classifier Tool
أداة تدريب مصنف الأشجار العشوائية
ArcMap
ArcGIS
How to use Train Random
Trees Classifier Tool in Arc Toolbox??
كيفية استخدام أداة تدريب مصنف الأشجار العشوائية ؟؟
Path to access the toolمسار الوصول الى الأداة
:
Train Random Trees Classifier Tool, Segmentation and Classification Toolset, Spatial
Analyst Tools Toolbox
Train Random Trees Classifier
Generates an Esri
classifier definition (.ecd) file using the Random Trees classification method.
The random trees
classifier is a powerful technique for image classification that is resistant
to overfitting and can work with segmented images and other ancillary raster
datasets. For standard image inputs, the tool accepts multiple-band imagery
with any bit depth, and it will perform the Random Trees classification on a
pixel basis or segment, based on the input training feature file.
يولد ملف تعريف مصنف Esri (.ecd) باستخدام طريقة تصنيف الأشجار العشوائية.
يُعد مصنف الأشجار العشوائية تقنية قوية لتصنيف
الصور ومقاومة للتركيب الزائد ويمكن أن تعمل مع الصور المجزأة ومجموعات البيانات
النقطية المساعدة الأخرى. بالنسبة لمدخلات الصور القياسية ، تقبل الأداة صورًا
متعددة النطاقات بأي عمق بت ، وستقوم بتصنيف Random Trees على أساس البكسل أو المقطع ، بناءً على ملف ميزة تدريب الإدخال.
Input Raster
The raster dataset to classify.
You can use any Esri-supported raster dataset. Options include a 3-band,
8-bit segmented raster dataset, where all the pixels in the same segment have
the same color. The input can also be a 1-band, 8-bit, grayscale segmented
raster.
Input Training Sample Features
The training sample file or layer that delineates your training sites.
These can be either shapefiles or feature classes that contain your
training samples. The following field names are required in the training sample
file:
· classname—A text field indicating the name of the class category.
· classvalue—A long integer field containing the integer value for each
class category.
Output Classifier Definition File
A JSON file that contains attribute information, statistics, or other
information needed for the classifier. A file with an .ecd extension is
created.
Additional Input Raster (optional)
Incorporate ancillary raster datasets, such as a multispectral image or a
DEM, to generate attributes and other required information for classification.
This parameter is optional.
Max Number of Trees (optional)
The maximum number of trees in the forest. Increasing the number of trees
will lead to higher accuracy rates, although this improvement will level off
eventually. The number of trees increases the processing time linearly.
Max Tree Depth (optional)
The maximum depth of each tree in the forest. Depth is another way of
saying the number of rules each tree is allowed to create to come to a
decision. Trees will not grow any deeper than this setting.
Max Number Of Samples Per Class
(optional)
The maximum number of samples to use for defining each class.
The default value of 1000 is recommended when the inputs are nonsegmented
rasters. A value that is less than or equal to 0 means that the system will use
all the samples from the training sites to train the classifier.
Segment Attributes Used (optional)
Specifies the attributes to be included in the attribute table associated
with the output raster.
· COLOR—The RGB color values are derived from the input raster, on a
per-segment basis.
· MEAN—The average digital number (DN), derived from the optional pixel
image, on a per-segment basis.
· STD—The standard deviation, derived from the optional pixel image, on a
per-segment basis.
· COUNT—The number of pixels comprising the segment, on a per-segment basis.
· COMPACTNESS—The degree to which a segment is compact or circular, on a
per-segment basis. The values range from 0 to 1, where 1 is a circle.
· RECTANGULARITY—The degree to which the segment is rectangular, on a
per-segment basis. The values range from 0 to 1, where 1 is a rectangle.
This parameter is only active if the Segmented key property is set to true
on the input raster. If the only input to the tool is a segmented image, the default
attributes are COLOR, COUNT, COMPACTNESS, and RECTANGULARITY. If an Additional
Input Raster is included as an input with a segmented image, MEAN and STD are
also available attributes.
1.
Input Raster ادخل البيانات النقطية
The raster dataset to classify.
You can use any
Esri-supported raster dataset. Options include a 3-band, 8-bit segmented raster
dataset, where all the pixels in the same segment have the same color. The
input can also be a 1-band, 8-bit, grayscale segmented raster.
مجموعة البيانات النقطية المطلوب تصنيفها.
يمكنك استخدام أي مجموعة بيانات نقطية مدعومة من Esri.
تتضمن الخيارات مجموعة بيانات نقطية مجزأة ذات 3 نطاقات و 8 بت ، حيث يكون لجميع
وحدات البكسل في نفس المقطع نفس اللون. يمكن أن يكون الإدخال أيضًا نقطية مجزأة
ذات نطاق واحد ، و 8 بت ، وتدرج الرمادي.
Input Training Sample Features
The training sample file or layer that delineates your training sites.
These can be either shapefiles or feature classes that contain your
training samples. The following field names are required in the training sample
file:
· classname—A text field indicating the name of the class category.
· classvalue—A long integer field containing the integer value for each
class category.
Output Classifier Definition File
A JSON file that contains attribute information, statistics, or other
information needed for the classifier. A file with an .ecd extension is
created.
Additional Input Raster (optional)
Incorporate ancillary raster datasets, such as a multispectral image or a
DEM, to generate attributes and other required information for classification.
This parameter is optional.
Max Number of Trees (optional)
The maximum number of trees in the forest. Increasing the number of trees
will lead to higher accuracy rates, although this improvement will level off
eventually. The number of trees increases the processing time linearly.
Max Tree Depth (optional)
The maximum depth of each tree in the forest. Depth is another way of
saying the number of rules each tree is allowed to create to come to a
decision. Trees will not grow any deeper than this setting.
Max Number Of Samples Per Class
(optional)
The maximum number of samples to use for defining each class.
The default value of 1000 is recommended when the inputs are nonsegmented
rasters. A value that is less than or equal to 0 means that the system will use
all the samples from the training sites to train the classifier.
Segment Attributes Used (optional)
Specifies the attributes to be included in the attribute table associated
with the output raster.
· COLOR—The RGB color values are derived from the input raster, on a
per-segment basis.
· MEAN—The average digital number (DN), derived from the optional pixel
image, on a per-segment basis.
· STD—The standard deviation, derived from the optional pixel image, on a
per-segment basis.
· COUNT—The number of pixels comprising the segment, on a per-segment basis.
· COMPACTNESS—The degree to which a segment is compact or circular, on a
per-segment basis. The values range from 0 to 1, where 1 is a circle.
· RECTANGULARITY—The degree to which the segment is rectangular, on a
per-segment basis. The values range from 0 to 1, where 1 is a rectangle.
This parameter is only active if the Segmented key property is set to true
on the input raster. If the only input to the tool is a segmented image, the
default attributes are COLOR, COUNT, COMPACTNESS, and RECTANGULARITY. If an
Additional Input Raster is included as an input with a segmented image, MEAN
and STD are also available attributes.
2.
Input Training Sample Features ادخل
معالم نموذج التدريب
The training sample file
or layer that delineates your training sites.
These can be either
shapefiles or feature classes that contain your training samples. The following
field names are required in the training sample file:
·
classname—A text field indicating the name of the class category.
·
classvalue—A long integer field containing the integer value for
each class category.
ملف نموذج التدريب أو الطبقة التي تحدد مواقع
التدريب الخاصة بك.
يمكن أن تكون هذه إما ملفات أشكال أو فئات ميزات تحتوي
على عينات التدريب الخاصة بك. أسماء الحقول التالية مطلوبة في ملف عينة التدريب:
• اسم الفئة - حقل نص
يشير إلى اسم فئة الفئة.
• قيمة الفئة - حقل عدد
صحيح طويل يحتوي على قيمة عدد صحيح لكل فئة فئة.
3.
Output Classifier Definition File ملف
تعريف مصنف المخرج
A JSON file that
contains attribute information, statistics, or other information needed for the
classifier. A file with an .ecd extension is created.
ملف JSON يحتوي
على معلومات السمات أو الإحصائيات أو المعلومات الأخرى المطلوبة للمصنف. يتم إنشاء
ملف بملحق .ecd.
Additional Input Raster (optional)
Incorporate ancillary raster datasets, such as a multispectral image or a
DEM, to generate attributes and other required information for classification.
This parameter is optional.
Max Number of Trees (optional)
The maximum number of trees in the forest. Increasing the number of trees
will lead to higher accuracy rates, although this improvement will level off
eventually. The number of trees increases the processing time linearly.
Max Tree Depth (optional)
The maximum depth of each tree in the forest. Depth is another way of
saying the number of rules each tree is allowed to create to come to a
decision. Trees will not grow any deeper than this setting.
Max Number Of Samples Per Class
(optional)
The maximum number of samples to use for defining each class.
The default value of 1000 is recommended when the inputs are nonsegmented
rasters. A value that is less than or equal to 0 means that the system will use
all the samples from the training sites to train the classifier.
Segment Attributes Used (optional)
Specifies the attributes to be included in the attribute table associated
with the output raster.
· COLOR—The RGB color values are derived from the input raster, on a
per-segment basis.
· MEAN—The average digital number (DN), derived from the optional pixel
image, on a per-segment basis.
· STD—The standard deviation, derived from the optional pixel image, on a
per-segment basis.
· COUNT—The number of pixels comprising the segment, on a per-segment basis.
· COMPACTNESS—The degree to which a segment is compact or circular, on a
per-segment basis. The values range from 0 to 1, where 1 is a circle.
· RECTANGULARITY—The degree to which the segment is rectangular, on a
per-segment basis. The values range from 0 to 1, where 1 is a rectangle.
This parameter is only active if the Segmented key property is set to true
on the input raster. If the only input to the tool is a segmented image, the
default attributes are COLOR, COUNT, COMPACTNESS, and RECTANGULARITY. If an
Additional Input Raster is included as an input with a segmented image, MEAN
and STD are also available attributes.
4.
Additional Input Raster (optional) مدخلات
نقطية إضافية (اختياري)
Incorporate ancillary
raster datasets, such as a multispectral image or a DEM, to generate attributes
and other required information for classification. This parameter is optional.
قم بتضمين مجموعات البيانات النقطية المساعدة ،
مثل صورة متعددة الأطياف أو DEM ،
لإنشاء سمات ومعلومات أخرى مطلوبة للتصنيف. هذه المعلمة اختيارية.
Max Number of Trees (optional)
The maximum number of trees in the forest. Increasing the number of trees
will lead to higher accuracy rates, although this improvement will level off
eventually. The number of trees increases the processing time linearly.
Max Tree Depth (optional)
The maximum depth of each tree in the forest. Depth is another way of
saying the number of rules each tree is allowed to create to come to a
decision. Trees will not grow any deeper than this setting.
Max Number Of Samples Per Class
(optional)
The maximum number of samples to use for defining each class.
The default value of 1000 is recommended when the inputs are nonsegmented
rasters. A value that is less than or equal to 0 means that the system will use
all the samples from the training sites to train the classifier.
Segment Attributes Used (optional)
Specifies the attributes to be included in the attribute table associated
with the output raster.
· COLOR—The RGB color values are derived from the input raster, on a
per-segment basis.
· MEAN—The average digital number (DN), derived from the optional pixel
image, on a per-segment basis.
· STD—The standard deviation, derived from the optional pixel image, on a
per-segment basis.
· COUNT—The number of pixels comprising the segment, on a per-segment basis.
· COMPACTNESS—The degree to which a segment is compact or circular, on a
per-segment basis. The values range from 0 to 1, where 1 is a circle.
· RECTANGULARITY—The degree to which the segment is rectangular, on a
per-segment basis. The values range from 0 to 1, where 1 is a rectangle.
This parameter is only active if the Segmented key property is set to true
on the input raster. If the only input to the tool is a segmented image, the
default attributes are COLOR, COUNT, COMPACTNESS, and RECTANGULARITY. If an
Additional Input Raster is included as an input with a segmented image, MEAN
and STD are also available attributes.
5.
Max Number of Trees (optional) أقصى
عدد من الأشجار (اختياري)
The maximum number of
trees in the forest. Increasing the number of trees will lead to higher
accuracy rates, although this improvement will level off eventually. The number
of trees increases the processing time linearly.
أقصى عدد من الأشجار في الغابة. ستؤدي زيادة عدد
الأشجار إلى معدلات دقة أعلى ، على الرغم من أن هذا التحسين سيستقر في النهاية.
يزيد عدد الأشجار من وقت المعالجة خطيًا.
Max Tree Depth (optional)
The maximum depth of each tree in the forest. Depth is another way of
saying the number of rules each tree is allowed to create to come to a
decision. Trees will not grow any deeper than this setting.
Max Number Of Samples Per Class
(optional)
The maximum number of samples to use for defining each class.
The default value of 1000 is recommended when the inputs are nonsegmented
rasters. A value that is less than or equal to 0 means that the system will use
all the samples from the training sites to train the classifier.
Segment Attributes Used (optional)
Specifies the attributes to be included in the attribute table associated
with the output raster.
· COLOR—The RGB color values are derived from the input raster, on a
per-segment basis.
· MEAN—The average digital number (DN), derived from the optional pixel
image, on a per-segment basis.
· STD—The standard deviation, derived from the optional pixel image, on a
per-segment basis.
· COUNT—The number of pixels comprising the segment, on a per-segment basis.
· COMPACTNESS—The degree to which a segment is compact or circular, on a
per-segment basis. The values range from 0 to 1, where 1 is a circle.
· RECTANGULARITY—The degree to which the segment is rectangular, on a
per-segment basis. The values range from 0 to 1, where 1 is a rectangle.
This parameter is only active if the Segmented key property is set to true
on the input raster. If the only input to the tool is a segmented image, the
default attributes are COLOR, COUNT, COMPACTNESS, and RECTANGULARITY. If an
Additional Input Raster is included as an input with a segmented image, MEAN
and STD are also available attributes.
6.
Max Tree Depth (optional) أقصى عمق
شجرة (اختياري)
The maximum depth of
each tree in the forest. Depth is another way of saying the number of rules
each tree is allowed to create to come to a decision. Trees will not grow any
deeper than this setting.
أقصى عمق لكل شجرة في الغابة. العمق هو طريقة أخرى
لقول عدد القواعد التي يُسمح لكل شجرة بإنشائها للتوصل إلى قرار. لن تنمو الأشجار
أعمق من هذا الإعداد.
Max Number Of Samples Per Class
(optional)
The maximum number of samples to use for defining each class.
The default value of 1000 is recommended when the inputs are nonsegmented
rasters. A value that is less than or equal to 0 means that the system will use
all the samples from the training sites to train the classifier.
Segment Attributes Used (optional)
Specifies the attributes to be included in the attribute table associated
with the output raster.
· COLOR—The RGB color values are derived from the input raster, on a
per-segment basis.
· MEAN—The average digital number (DN), derived from the optional pixel
image, on a per-segment basis.
· STD—The standard deviation, derived from the optional pixel image, on a
per-segment basis.
· COUNT—The number of pixels comprising the segment, on a per-segment basis.
· COMPACTNESS—The degree to which a segment is compact or circular, on a
per-segment basis. The values range from 0 to 1, where 1 is a circle.
· RECTANGULARITY—The degree to which the segment is rectangular, on a
per-segment basis. The values range from 0 to 1, where 1 is a rectangle.
This parameter is only active if the Segmented key property is set to true
on the input raster. If the only input to the tool is a segmented image, the
default attributes are COLOR, COUNT, COMPACTNESS, and RECTANGULARITY. If an
Additional Input Raster is included as an input with a segmented image, MEAN
and STD are also available attributes.
7.
Max Number Of Samples Per Class (optional) أقصى عدد للعينات لكل فئة (اختياري)
The maximum number of
samples to use for defining each class.
The default value of
1000 is recommended when the inputs are nonsegmented rasters. A value that is
less than or equal to 0 means that the system will use all the samples from the
training sites to train the classifier.
أقصى عدد من العينات لاستخدامها في تحديد كل فئة.
يوصى باستخدام القيمة الافتراضية 1000 عندما تكون
المدخلات عبارة عن بيانات نقطية غير مجزأة. تعني القيمة الأقل من أو تساوي 0 أن
النظام سيستخدم جميع العينات من مواقع التدريب لتدريب المصنف.
Segment Attributes Used (optional)
Specifies the attributes to be included in the attribute table associated
with the output raster.
· COLOR—The RGB color values are derived from the input raster, on a
per-segment basis.
· MEAN—The average digital number (DN), derived from the optional pixel
image, on a per-segment basis.
· STD—The standard deviation, derived from the optional pixel image, on a
per-segment basis.
· COUNT—The number of pixels comprising the segment, on a per-segment basis.
· COMPACTNESS—The degree to which a segment is compact or circular, on a per-segment
basis. The values range from 0 to 1, where 1 is a circle.
· RECTANGULARITY—The degree to which the segment is rectangular, on a
per-segment basis. The values range from 0 to 1, where 1 is a rectangle.
This parameter is only active if the Segmented key property is set to true
on the input raster. If the only input to the tool is a segmented image, the
default attributes are COLOR, COUNT, COMPACTNESS, and RECTANGULARITY. If an
Additional Input Raster is included as an input with a segmented image, MEAN
and STD are also available attributes.
8.
Segment Attributes Used (optional) جدول
بيانات المقطع المستخدمة (اختياري)
Specifies the attributes
to be included in the attribute table associated with the output raster.
·
COLOR—The RGB color values are derived from the input raster, on a
per-segment basis.
·
MEAN—The average digital number (DN), derived from the optional
pixel image, on a per-segment basis.
·
STD—The standard deviation, derived from the optional pixel image,
on a per-segment basis.
·
COUNT—The number of pixels comprising the segment, on a
per-segment basis.
·
COMPACTNESS—The degree to which a segment is compact or circular,
on a per-segment basis. The values range from 0 to 1, where 1 is a circle.
·
RECTANGULARITY—The degree to which the segment is rectangular, on
a per-segment basis. The values range from 0 to 1, where 1 is a rectangle.
This parameter is only
active if the Segmented key property is set to true on the input raster. If the
only input to the tool is a segmented image, the default attributes are COLOR,
COUNT, COMPACTNESS, and RECTANGULARITY. If an Additional Input Raster is
included as an input with a segmented image, MEAN and STD are also available
attributes.
يحدد السمات المراد تضمينها في جدول البيانات
المرتبط بالمخرجات النقطية.
• COLOR - يتم
اشتقاق قيم ألوان RGB من البيانات النقطية المدخلة ، على أساس كل مقطع.
• يعني - متوسط الرقم
الرقمي (DN) ، المشتق من صورة البكسل الاختيارية ، على أساس كل مقطع.
• STD -
الانحراف المعياري ، المشتق من صورة البكسل الاختيارية ، على أساس كل مقطع.
• COUNT - عدد
وحدات البكسل التي تتألف من المقطع ، على أساس كل مقطع.
• الترابط - الدرجة التي
يكون فيها المقطع مدمجًا أو دائريًا ، على أساس كل جزء. تتراوح القيم من 0 إلى 1 ،
حيث 1 عبارة عن دائرة.
• RECTANGULARITY - الدرجة التي يكون عندها المقطع مستطيلاً ، على أساس كل مقطع.
تتراوح القيم من 0 إلى 1 ، حيث 1 مستطيل.
تكون هذه المعلمة نشطة فقط إذا تم تعيين خاصية
المفتاح المقسم إلى "صواب" في البيانات النقطية للإدخال. إذا كان
الإدخال الوحيد للأداة عبارة عن صورة مجزأة ، فإن السمات الافتراضية هي COLOR و COUNT و COMPACTNESS و RECTANGULARITY. إذا تم تضمين إدخال
نقطي إضافي كمدخل مع صورة مجزأة ، فإن MEAN و STD هما
أيضًا سمات متاحة.
اليك صفحه ومجموعة على الفيس بوك لتعلم أكثر بما يخص نظم المعلومات الجغرافية (GIS) و برنامج ArcGIS Pro من خلال هذه الروابط:
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ArcGIS Pro من
هنا.
مجموعة على الفيس بوك
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هنا.صفحة الفيس بوك
GIS for WE من
هنا.
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