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 ؟؟
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
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.
· 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.
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.
· 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.
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.
· 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.
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.
· 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 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.
· 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 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.
· 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 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.
· 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 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.
· 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.
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.
· 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.
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.
· 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.
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.
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.
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