Satellite Image Classification using Deep Learning
Aerial image classification has been a topic of active research in the fields of computer vision and remote sensing for several decades. Early approaches relied on handcrafted features and traditional machine learning algorithms such as Support Vector Machines (SVM) and Random Forests. However, these methods often struggled to capture the complex spatial and spectral characteristics of aerial imagery, leading to limited classification accuracy. aerial imagery has propelled advancements in various fields, including urban planning, environmental monitoring, and disaster management.
The ability to automatically classify scenes in aerial images has become crucial for interpreting vast amounts of visual data efficiently. With the increasing availability of high resolution aerial imagery captured by satellites, drones, and other aerial platforms, there is a growing demand for accurate and efficient classification algorithms to analyze and interpret this vast amount of data.
Object Detection in Satellite Images Python
Software Requirements :-
- Coding Language : Python
- Implementation: Software Framework.
- Operating system : Windows 10 / 11.
- Graphical User Interface : Tkinter
Hardware Requirement:-
- Input Devices : Keyboard, Mouse.
- System : Pentium i3 Processor.
- Hard Disk : 500 GB.
- RAM : 4 GB.
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