Generate and Inspect Training Samples from Seed Points Tools
Generate Training Samples from Seed
Points
أداة توليد عينات تدريب من نقاط الأولية
ArcMap
ArcGIS
How to use Generate
Training Samples from Seed Points Tool in Arc Toolbox??
كيفية استخدام أداة توليد عينات تدريب من نقاط الأولية
؟؟
Path to access the toolمسار الوصول الى الأداة
:
Generate Training Samples from Seed
Points Tool, Segmentation and Classification Toolset, Spatial
Analyst Tools Toolbox
Generate Training Samples from Seed
Points
Generates training
samples from seed points, such as accuracy assessment points or training sample
points. A typical use case is generating training samples from an existing
source, such as a thematic raster or a feature class.
يولد عينات تدريب من نقاط التأسيس ، مثل نقاط
تقييم الدقة أو نقاط عينة التدريب. حالة الاستخدام النموذجية هي إنشاء عينات
تدريبية من مصدر موجود ، مثل البيانات النقطية الموضوعية أو فئة الميزات.
1.
Input Raster Or Feature Class أدخل
البيانات النقطية او فئة المعلم
The data source that
labels the training samples.
مصدر البيانات الذي يصنف عينات التدريب.
Input Seed Points
A point shapefile or feature class to provide the centers of training
sample polygons.
Output Training Sample Feature
Class
The output training sample feature class in the format that can be used in
training tools, including shapefiles. The output feature class can be either a
polygon feature class or a point feature class.
Min Sample Area (optional)
The minimum area needed for each training sample, in square meters. The
minimum value must be greater than or equal to 0.
Max Sample Radius (optional)
The longest distance (in meters) from any point within the training sample
to its center seed point. If set to 0, the output training sample will be
points instead of polygons. The minimum value must be greater than or equal to
0.
2.
Input Seed Points أدخل النقاط
الأولية
A point shapefile or
feature class to provide the centers of training sample polygons.
ملف شكل نقطة أو فئة ميزة لتوفير مراكز تدريب عينة
المضلعات.
Output Training Sample Feature
Class
The output training sample feature class in the format that can be used in
training tools, including shapefiles. The output feature class can be either a
polygon feature class or a point feature class.
Min Sample Area (optional)
The minimum area needed for each training sample, in square meters. The
minimum value must be greater than or equal to 0.
Max Sample Radius (optional)
The longest distance (in meters) from any point within the training sample
to its center seed point. If set to 0, the output training sample will be
points instead of polygons. The minimum value must be greater than or equal to
0.
3.
Output Training Sample Feature Class نموذج تدريب فئة الميزات المخرج
The output training
sample feature class in the format that can be used in training tools, including
shapefiles. The output feature class can be either a polygon feature class or a
point feature class.
فئة ميزة نموذج تدريب الإخراج بالتنسيق الذي يمكن
استخدامه في أدوات التدريب ، بما في ذلك ملفات الأشكال. يمكن أن تكون فئة معلم
الإخراج إما فئة معلم مضلع أو فئة معلم نقطي.
Min Sample Area (optional)
The minimum area needed for each training sample, in square meters. The
minimum value must be greater than or equal to 0.
Max Sample Radius (optional)
The longest distance (in meters) from any point within the training sample
to its center seed point. If set to 0, the output training sample will be
points instead of polygons. The minimum value must be greater than or equal to
0.
4.
Min Sample Area (optional) منطقة
العينة الصغرى (اختياري)
The minimum area needed
for each training sample, in square meters. The minimum value must be greater
than or equal to 0.
الحد الأدنى للمساحة المطلوبة لكل عينة تدريب
بالمتر المربع. يجب أن تكون القيمة الدنيا أكبر من أو تساوي 0.
Max Sample Radius (optional)
The longest distance (in meters) from any point within the training sample
to its center seed point. If set to 0, the output training sample will be
points instead of polygons. The minimum value must be greater than or equal to
0.
5.
Max Sample Radius (optional) الحد
الأقصى لنصف قطر العينة (اختياري)
The longest distance (in
meters) from any point within the training sample to its center seed point. If
set to 0, the output training sample will be points instead of polygons. The
minimum value must be greater than or equal to 0.
أطول مسافة (بالأمتار) من أي نقطة داخل عينة
التدريب إلى نقطة البداية المركزية. إذا تم التعيين على 0 ، فسيكون نموذج التدريب
على الإخراج عبارة عن نقاط بدلاً من المضلعات. يجب أن تكون القيمة الدنيا أكبر من
أو تساوي 0.
Inspect Training Samples
أداة فحص عينات التدريب
ArcMap
ArcGIS
How to use Inspect Training
Samples Tool in Arc Toolbox??
كيفية استخدام أداة فحص عينات التدريب ؟؟
Path to access the toolمسار الوصول الى الأداة
:
Inspect Training Samples Tool, Segmentation and Classification Toolset, Spatial
Analyst Tools Toolbox
Inspect Training Samples
Estimates the accuracy
of individual training samples. The cross validation accuracy is computed using
the previously generated classification training result in an .ecd file and the
training samples. Outputs include a raster dataset containing the misclassified
class values and a training sample dataset with the accuracy score for each
training sample.
يقدر دقة عينات التدريب الفردية. يتم حساب دقة
التحقق المتقاطع باستخدام نتيجة تدريب التصنيف التي تم إنشاؤها مسبقًا في ملف .ecd
وعينات التدريب. تتضمن المخرجات مجموعة بيانات نقطية تحتوي على قيم فئة تم تصنيفها
بشكل خاطئ ومجموعة بيانات عينة تدريب مع درجة الدقة لكل عينة تدريب.
Input Raster
The input raster to be classified.
Input Training Sample Features
A training sample feature class created in the Training Samples Manager
pane.
Input Classifier Definition File
The .ecd output classifier file from any of the train classifier tools.
The .ecd file is a JSON file that contains attribute information, statistics,
or other information needed for the classifier.
Output Training Sample Feature
Class With Score
The output individual training samples saved as a feature class. The
associated attribute table contains an addition field listing the accuracy
score.
Output Misclassified Raster
The output misclassified raster having NoData outside training samples. In
training samples, correctly classified pixels are represented as NoData, and
misclassified pixels are represented by their class value. The results is an
index map of misclassified class values.
Additional Input Raster (optional)
Incorporate ancillary raster datasets, such as a multispectral image or a
DEM, to generate attributes and other required information for the classifier.
This raster will be needed when calculating attributes such as mean or standard
deviation. This parameter is optional.
1.
Input Raster أدخل البيانات النقطية
The input raster to be
classified.
البيانات النقطية المدخلات المراد تصنيفها.
Input Training Sample Features
A training sample feature class created in the Training Samples Manager
pane.
Input Classifier Definition File
The .ecd output classifier file from any of the train classifier tools.
The .ecd file is a JSON file that contains attribute information, statistics,
or other information needed for the classifier.
Output Training Sample Feature
Class With Score
The output individual training samples saved as a feature class. The
associated attribute table contains an addition field listing the accuracy
score.
Output Misclassified Raster
The output misclassified raster having NoData outside training samples. In
training samples, correctly classified pixels are represented as NoData, and
misclassified pixels are represented by their class value. The results is an
index map of misclassified class values.
Additional Input Raster (optional)
Incorporate ancillary raster datasets, such as a multispectral image or a
DEM, to generate attributes and other required information for the classifier.
This raster will be needed when calculating attributes such as mean or standard
deviation. This parameter is optional.
2.
Input Training Sample Features أدخل
معالم نموذج التدريب
A training sample
feature class created in the Training Samples Manager pane.
فئة معلم نموذج تدريب تم إنشاؤها في جزء مدير
عينات التدريب.
Input Classifier Definition File
The .ecd output classifier file from any of the train classifier tools.
The .ecd file is a JSON file that contains attribute information, statistics,
or other information needed for the classifier.
Output Training Sample Feature
Class With Score
The output individual training samples saved as a feature class. The
associated attribute table contains an addition field listing the accuracy
score.
Output Misclassified Raster
The output misclassified raster having NoData outside training samples. In
training samples, correctly classified pixels are represented as NoData, and
misclassified pixels are represented by their class value. The results is an
index map of misclassified class values.
Additional Input Raster (optional)
Incorporate ancillary raster datasets, such as a multispectral image or a
DEM, to generate attributes and other required information for the classifier.
This raster will be needed when calculating attributes such as mean or standard
deviation. This parameter is optional.
3.
Input Classifier Definition File أدخل
ملف تعريف المصنف
The .ecd output
classifier file from any of the train classifier tools. The .ecd file is a JSON
file that contains attribute information, statistics, or other information
needed for the classifier.
ملف مصنف الإخراج .ecd من أي
من أدوات مصنف القطار. ملف .ecd هو
ملف JSON يحتوي على معلومات
السمات أو الإحصائيات أو المعلومات الأخرى اللازمة للمصنف.
Output Training Sample Feature
Class With Score
The output individual training samples saved as a feature class. The
associated attribute table contains an addition field listing the accuracy
score.
Output Misclassified Raster
The output misclassified raster having NoData outside training samples. In
training samples, correctly classified pixels are represented as NoData, and
misclassified pixels are represented by their class value. The results is an
index map of misclassified class values.
Additional Input Raster (optional)
Incorporate ancillary raster datasets, such as a multispectral image or a
DEM, to generate attributes and other required information for the classifier.
This raster will be needed when calculating attributes such as mean or standard
deviation. This parameter is optional.
4.
Output Training Sample Feature Class With Score نموذج تدريب فئة مميزة مع النتيجة المخرج
The output individual
training samples saved as a feature class. The associated attribute table
contains an addition field listing the accuracy score.
تم حفظ عينات التدريب الفردية الناتجة كفئة معالم.
يحتوي جدول البيانات المرتبط على حقل إضافة يسرد درجة الدقة.
Output Misclassified Raster
The output misclassified raster having NoData outside training samples. In
training samples, correctly classified pixels are represented as NoData, and
misclassified pixels are represented by their class value. The results is an
index map of misclassified class values.
Additional Input Raster (optional)
Incorporate ancillary raster datasets, such as a multispectral image or a
DEM, to generate attributes and other required information for the classifier.
This raster will be needed when calculating attributes such as mean or standard
deviation. This parameter is optional.
5.
Output Misclassified Raster خطأ
تصنيف النقطية المخرج
The output misclassified
raster having NoData outside training samples. In training samples, correctly
classified pixels are represented as NoData, and misclassified pixels are
represented by their class value. The results is an index map of misclassified
class values.
الإخراج المصنف بشكل خاطئ النقطية يحتوي على NoData خارج
عينات التدريب. في عينات التدريب ، يتم تمثيل وحدات البكسل المصنفة بشكل صحيح على
أنها NoData ، ويتم تمثيل وحدات
البكسل المصنفة بشكل خاطئ بقيمة فئتها. النتائج عبارة عن خريطة فهرس لقيم الفئة
المصنفة بشكل خاطئ.
Additional Input Raster (optional)
Incorporate ancillary raster datasets, such as a multispectral image or a
DEM, to generate attributes and other required information for the classifier.
This raster will be needed when calculating attributes such as mean or standard
deviation. This parameter is optional.
6.
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 the classifier. This raster will be needed
when calculating attributes such as mean or standard deviation. This parameter
is optional.
قم بتضمين مجموعات البيانات النقطية المساعدة ،
مثل صورة متعددة الأطياف أو DEM ،
لإنشاء سمات ومعلومات أخرى مطلوبة للمصنف. ستكون هناك حاجة إلى هذه البيانات
النقطية عند حساب سمات مثل المتوسط أو الانحراف المعياري. هذه المعلمة اختيارية.
اليك صفحه ومجموعة على الفيس بوك لتعلم أكثر بما يخص نظم المعلومات الجغرافية (GIS) و برنامج ArcGIS Pro من خلال هذه الروابط:
مجموعة على الفيس بوك
ArcGIS Pro من
هنا.
مجموعة على الفيس بوك
GIS for WE - ArcGIS Pro من
هنا.صفحة الفيس بوك
GIS for WE من
هنا.
تعليقات
إرسال تعليق