تدريب مصنف الأشجار العشوائية Train Random Trees Classifier

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تدريب مصنف الأشجار العشوائية Train Random Trees Classifier

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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 ؟؟

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 Tool 

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:

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.

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:

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.

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.

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.

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.

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.

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.

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.

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