Objects classification in google earth images by using fuzzy clustering algorithm and neural networks

مقدم أطروحة جامعية

al-Rashid, Sura Zaki Naji

الجامعة

الجامعة التكنولوجية

الكلية

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القسم الأكاديمي

قسم علوم الحاسوب

دولة الجامعة

العراق

الدرجة العلمية

ماجستير

تاريخ الدرجة العلمية

2006

الملخص الإنجليزي

The data of Google Earth program can provide the object Information of the earth's surface accurately.

Now the commercial satellites can provide the satellite images with the High resolution.

With those high spatial resolution images, shape features and texture features and ether's of the ground objects are very clear.

Therefore, this thesis presents a method to design programming system by using hybrid techniques (Fuzzy Clustering Algorithm and Neural Network Algorithm) and three types from Color Spaces (RGB, YUV, and HSI) to classify these images into the objects with minimum acceptable error depending on their features.

This search is concerned with classifying seven kinds of objects (urban, forests, grassland, streets, water, soil, commercial cities).

Accordingly, firstly we read the image from the Google Earth program with RGB color space (hen we convert this imago another color space is YUV to be used in the segmentation stage only Y band one byte to find the clusters center exact values instead of the three values to clusters center with RGB color space, In the segmentation stage, we use Fuzzy C-Means (FCM) algorithm to segment the image to number of the clusters similar of the attributes.

After this, we extract two types of features for each cluster which are visual features including (Pattern, Shape, Texture, Shadow, Association), and statistical features represented by spectrum features that include (Intensity, Hue, Saturation).

In the last stage, we appoint feed forward neural network from Type error Back-Propagation neural network to determine the class that each feature vector belongs to.

التخصصات الرئيسية

تكنولوجيا المعلومات وعلم الحاسوب

الموضوعات

• الأرض(كوكب)

• APA
• MLA
• AMA

لغة النص

الإنجليزية

نوع البيانات

رسائل جامعية

رقم السجل

BIM-305673