SRCNN

Sliding Region based Convolutional Neural Network Modified Faster RCNN for Remote Sensing Imagery (Spatial Resolution and size Independent)

Prerequisites

1. Python 3
2. Anaconda Navigator
3. Minimum i5 processor with 8GB RAM
4. GPU (NVIDIA Preferred)

Python Libraries

1. Create a new anaconda environment (in command prompt)

C:\> conda create -n tensorflow1 pip python=3.5

2. Activate the enviromnet and update

C:\> activate tensorflow1
(tensorflow1) C:\>python -m pip install --upgrade pip

3. Install tensorflow-GPU (tensorflow1) C:\> pip install --ignore-installed --upgrade tensorflow-gpu
4. Install following tools and Packages

(tensorflow1) C:\> conda install -c anaconda protobuf
(tensorflow1) C:\> pip install pillow
(tensorflow1) C:\> pip install lxml
(tensorflow1) C:\> pip install Cython
(tensorflow1) C:\> pip install contextlib2
(tensorflow1) C:\> pip install jupyter
(tensorflow1) C:\> pip install matplotlib
(tensorflow1) C:\> pip install pandas
(tensorflow1) C:\> pip install opencv-python

Instructions

1. Clone or download the repository.
2. Download the inference graph from https://drive.google.com/open?id=13_kBwvTKLzY6HODNXM2IPcOd1ol47v2K and store it in a new subfolder named "inference_graph" in the srcnn folder.
3. Dpen the working directory (SRCNN) in command prompt and open jupyter notebook.

C:\SRCNN> activate tensorflow1
(tensorflow1) C:\SRCNN>jupyter notebook

4. Open the SRCNN.ipynb
5. Set the image path, spatial resolution value and overlapping percentange in relivant field.
6. Run all cell

Done