تدريب مصنف مجموعة ايزو Train ISO Cluster Classifier

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تدريب مصنف مجموعة ايزو Train ISO Cluster Classifier

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Train ISO Cluster Classifier Tool

أداة تدريب مصنف مجموعة ايزو

ArcMap ArcGIS

How to use Train ISO Cluster Classifier Tool in Arc Toolbox??

كيفية استخدام أداة تدريب مصنف مجموعة ايزو ؟؟

كيفية استخدام أداة تدريب مصنف مجموعة ايزو ؟؟

Path to access the toolمسار الوصول الى الأداة

:

Train ISO Cluster Classifier Tool, Segmentation and Classification Toolset, Spatial Analyst Tools Toolbox

Train ISO Cluster Classifier Tool 

Train ISO Cluster Classifier

Generates an Esri classifier definition (.ecd) file using the Iso Cluster classification definition.

This tool performs an unsupervised classification.

يولد ملف تعريف مصنف Esri (.ecd) باستخدام تعريف تصنيف Iso Cluster.

تقوم هذه الأداة بتصنيف غير خاضع للرقابة.

Input Raster

The raster dataset to classify.

Max Number Of Classes / Clusters

Maximum number of desired classes to group pixels or segments. This should be set to be greater than the number of classes in your legend.

It is possible that you will get fewer classes than what you specified for this parameter. If you need more, increase this value and aggregate classes after the training process is complete.

Output Classifier Definition File

The output JSON file that contains attribute information, statistics, hyperplane vectors, and other information for the classifier. An .ecd file 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 Iterations (optional)

The maximum number of iterations for the clustering process to run.

The recommended range is between 10 and 20 iterations. Increasing this value will linearly increase the processing time.

Max Number of Cluster Merges per Iteration (optional)

The maximum number of cluster merges per iteration. Increasing the number of merges will reduce the number of classes that are created. A lower value will result in more classes.

Max Merge Distance (optional)

The maximum distance between cluster centers in feature space. Increasing the distance will allow more clusters to merge, resulting in fewer classes. A lower value will result in more classes. Values from 0 to 5 tend to give the best results.

Min Number Of Samples Per Cluster (optional)

The minimum number of pixels or segments in a valid cluster or class.

The default value of 20 has shown to be effective in creating statistically significant classes. You can increase this number for more robust classes; however, it may limit the overall number of classes that are created.

Skip Factor (optional)

Number of pixels to skip for a pixel image input. If a segmented image is an input, specify the number of segments to skip.

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.

مجموعة البيانات النقطية المطلوب تصنيفها.

Max Number Of Classes / Clusters

Maximum number of desired classes to group pixels or segments. This should be set to be greater than the number of classes in your legend.

It is possible that you will get fewer classes than what you specified for this parameter. If you need more, increase this value and aggregate classes after the training process is complete.

Output Classifier Definition File

The output JSON file that contains attribute information, statistics, hyperplane vectors, and other information for the classifier. An .ecd file 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 Iterations (optional)

The maximum number of iterations for the clustering process to run.

The recommended range is between 10 and 20 iterations. Increasing this value will linearly increase the processing time.

Max Number of Cluster Merges per Iteration (optional)

The maximum number of cluster merges per iteration. Increasing the number of merges will reduce the number of classes that are created. A lower value will result in more classes.

Max Merge Distance (optional)

The maximum distance between cluster centers in feature space. Increasing the distance will allow more clusters to merge, resulting in fewer classes. A lower value will result in more classes. Values from 0 to 5 tend to give the best results.

Min Number Of Samples Per Cluster (optional)

The minimum number of pixels or segments in a valid cluster or class.

The default value of 20 has shown to be effective in creating statistically significant classes. You can increase this number for more robust classes; however, it may limit the overall number of classes that are created.

Skip Factor (optional)

Number of pixels to skip for a pixel image input. If a segmented image is an input, specify the number of segments to skip.

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.    Max Number Of Classes / Clusters أقصى عدد من الفئات / المجموعات

Maximum number of desired classes to group pixels or segments. This should be set to be greater than the number of classes in your legend.

It is possible that you will get fewer classes than what you specified for this parameter. If you need more, increase this value and aggregate classes after the training process is complete.

أقصى عدد للفئات المرغوبة لتجميع وحدات البكسل أو المقاطع. يجب تعيين هذا ليكون أكبر من عدد الفئات في وسيلة الإيضاح الخاصة بك.

من الممكن أن تحصل على فئات أقل مما حددته لهذه المعلمة. إذا كنت بحاجة إلى المزيد ، فقم بزيادة هذه القيمة وتجميع الفصول الدراسية بعد اكتمال عملية التدريب.

Output Classifier Definition File

The output JSON file that contains attribute information, statistics, hyperplane vectors, and other information for the classifier. An .ecd file 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 Iterations (optional)

The maximum number of iterations for the clustering process to run.

The recommended range is between 10 and 20 iterations. Increasing this value will linearly increase the processing time.

Max Number of Cluster Merges per Iteration (optional)

The maximum number of cluster merges per iteration. Increasing the number of merges will reduce the number of classes that are created. A lower value will result in more classes.

Max Merge Distance (optional)

The maximum distance between cluster centers in feature space. Increasing the distance will allow more clusters to merge, resulting in fewer classes. A lower value will result in more classes. Values from 0 to 5 tend to give the best results.

Min Number Of Samples Per Cluster (optional)

The minimum number of pixels or segments in a valid cluster or class.

The default value of 20 has shown to be effective in creating statistically significant classes. You can increase this number for more robust classes; however, it may limit the overall number of classes that are created.

Skip Factor (optional)

Number of pixels to skip for a pixel image input. If a segmented image is an input, specify the number of segments to skip.

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.

3.    Output Classifier Definition File ملف تعريف مصنف المخرج

The output JSON file that contains attribute information, statistics, hyperplane vectors, and other information for the classifier. An .ecd file is created.

ملف JSON الناتج الذي يحتوي على معلومات البيانات الجدولية والإحصائيات ومتجهات المستوى الفائق ومعلومات أخرى للمصنف. يتم إنشاء ملف .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 Iterations (optional)

The maximum number of iterations for the clustering process to run.

The recommended range is between 10 and 20 iterations. Increasing this value will linearly increase the processing time.

Max Number of Cluster Merges per Iteration (optional)

The maximum number of cluster merges per iteration. Increasing the number of merges will reduce the number of classes that are created. A lower value will result in more classes.

Max Merge Distance (optional)

The maximum distance between cluster centers in feature space. Increasing the distance will allow more clusters to merge, resulting in fewer classes. A lower value will result in more classes. Values from 0 to 5 tend to give the best results.

Min Number Of Samples Per Cluster (optional)

The minimum number of pixels or segments in a valid cluster or class.

The default value of 20 has shown to be effective in creating statistically significant classes. You can increase this number for more robust classes; however, it may limit the overall number of classes that are created.

Skip Factor (optional)

Number of pixels to skip for a pixel image input. If a segmented image is an input, specify the number of segments to skip.

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 Iterations (optional)

The maximum number of iterations for the clustering process to run.

The recommended range is between 10 and 20 iterations. Increasing this value will linearly increase the processing time.

Max Number of Cluster Merges per Iteration (optional)

The maximum number of cluster merges per iteration. Increasing the number of merges will reduce the number of classes that are created. A lower value will result in more classes.

Max Merge Distance (optional)

The maximum distance between cluster centers in feature space. Increasing the distance will allow more clusters to merge, resulting in fewer classes. A lower value will result in more classes. Values from 0 to 5 tend to give the best results.

Min Number Of Samples Per Cluster (optional)

The minimum number of pixels or segments in a valid cluster or class.

The default value of 20 has shown to be effective in creating statistically significant classes. You can increase this number for more robust classes; however, it may limit the overall number of classes that are created.

Skip Factor (optional)

Number of pixels to skip for a pixel image input. If a segmented image is an input, specify the number of segments to skip.

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 Iterations (optional) أقصى عدد من التكرارات (اختياري)

The maximum number of iterations for the clustering process to run.

The recommended range is between 10 and 20 iterations. Increasing this value will linearly increase the processing time.

الحد الأقصى لعدد التكرارات لتشغيل عملية نظام المجموعات.

النطاق الموصى به بين 10 و 20 تكرارًا. ستؤدي زيادة هذه القيمة إلى زيادة وقت المعالجة خطيًا.

Max Number of Cluster Merges per Iteration (optional)

The maximum number of cluster merges per iteration. Increasing the number of merges will reduce the number of classes that are created. A lower value will result in more classes.

Max Merge Distance (optional)

The maximum distance between cluster centers in feature space. Increasing the distance will allow more clusters to merge, resulting in fewer classes. A lower value will result in more classes. Values from 0 to 5 tend to give the best results.

Min Number Of Samples Per Cluster (optional)

The minimum number of pixels or segments in a valid cluster or class.

The default value of 20 has shown to be effective in creating statistically significant classes. You can increase this number for more robust classes; however, it may limit the overall number of classes that are created.

Skip Factor (optional)

Number of pixels to skip for a pixel image input. If a segmented image is an input, specify the number of segments to skip.

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 Number of Cluster Merges per Iteration (optional) الحد الأقصى لعدد عمليات دمج الكتلة لكل تكرار (اختياري)

The maximum number of cluster merges per iteration. Increasing the number of merges will reduce the number of classes that are created. A lower value will result in more classes.

الحد الأقصى لعدد عمليات الدمج في نظام المجموعة لكل تكرار. ستؤدي زيادة عدد عمليات الدمج إلى تقليل عدد الفئات التي تم إنشاؤها. ستؤدي القيمة الأقل إلى المزيد من الفئات.

Max Merge Distance (optional)

The maximum distance between cluster centers in feature space. Increasing the distance will allow more clusters to merge, resulting in fewer classes. A lower value will result in more classes. Values from 0 to 5 tend to give the best results.

Min Number Of Samples Per Cluster (optional)

The minimum number of pixels or segments in a valid cluster or class.

The default value of 20 has shown to be effective in creating statistically significant classes. You can increase this number for more robust classes; however, it may limit the overall number of classes that are created.

Skip Factor (optional)

Number of pixels to skip for a pixel image input. If a segmented image is an input, specify the number of segments to skip.

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 Merge Distance (optional) أقصى مسافة دمج (اختياري)

The maximum distance between cluster centers in feature space. Increasing the distance will allow more clusters to merge, resulting in fewer classes. A lower value will result in more classes. Values from 0 to 5 tend to give the best results.

أقصى مسافة بين مراكز المجموعة في مساحة الميزة. ستسمح زيادة المسافة بدمج المزيد من المجموعات ، مما ينتج عنه عدد أقل من الفئات. ستؤدي القيمة الأقل إلى المزيد من الفئات. تميل القيم من 0 إلى 5 إلى إعطاء أفضل النتائج.

Min Number Of Samples Per Cluster (optional)

The minimum number of pixels or segments in a valid cluster or class.

The default value of 20 has shown to be effective in creating statistically significant classes. You can increase this number for more robust classes; however, it may limit the overall number of classes that are created.

Skip Factor (optional)

Number of pixels to skip for a pixel image input. If a segmented image is an input, specify the number of segments to skip.

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.    Min Number Of Samples Per Cluster (optional) أقل عدد من العينات لكل مجموعة (اختياري)

The minimum number of pixels or segments in a valid cluster or class.

The default value of 20 has shown to be effective in creating statistically significant classes. You can increase this number for more robust classes; however, it may limit the overall number of classes that are created.

الحد الأدنى لعدد وحدات البكسل أو المقاطع في مجموعة أو فئة صالحة.

أظهرت القيمة الافتراضية 20 أنها فعالة في إنشاء فئات ذات دلالة إحصائية. يمكنك زيادة هذا العدد لفصول أكثر قوة ؛ ومع ذلك ، قد يحد من العدد الإجمالي للفئات التي تم إنشاؤها.

Skip Factor (optional)

Number of pixels to skip for a pixel image input. If a segmented image is an input, specify the number of segments to skip.

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.

9.    Skip Factor (optional) عامل التخطي (اختياري)

Number of pixels to skip for a pixel image input. If a segmented image is an input, specify the number of segments to skip.

عدد وحدات البكسل المطلوب تخطيها لإدخال صورة بكسل. إذا كانت الصورة المجزأة عبارة عن إدخال ، فحدد عدد المقاطع التي تريد تخطيها.

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.

10. Segment Attributes Used (optional) جداول البيانات 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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