A free Python-based software application (GPL) for image processing and classification of high-resolution satellite imagery. The Machine Learning, Computer Vision and Scientific Computing libraries are based on scipy, numpy, OpenCV, scikit-learn GUI is based in Tkinter.

Import functionality for Multi-Format Satellite data (TIFF - Tagged Image File Format, PNG, JPG)

Splitting image into component bands

Merging bands through different colors, to make visible subtle contrasting features.

Creation of user-defined False Color Composite (FCC) images.

Preprocessing

Display/ Enhancement

Spatial Enhancement - Bands are passed through high pass and low pass filters - Mean filters,

Median Filters, Sobel, Kisrch, Gaussian and Fourier Transfom filters.

Spectral Enhancement – NDVI, RVI, TVI. Principal Component Analysis and Fourier transform.

Contrast Enhancement-linear, logarithmic, exponential and piecewise linear

Point Operations, Histogram Equalization

Calculation of important indices

Image Reduction, Magnification, Local Operations

Feature Classification – using Machine Learning algorithms

Personalized results – region selection

Conclusions about resulted Images

##Libraries Used

GDAL – Geospatial Data Abstraction Library ( a C++ library wrapped for Python support )

GDAL is a translator library for raster and vector geospatial data formats that is released under

anX/MIT style Open Source license by the Open Source Geospatial Foundation. As a library, it presents

a single raster abstract data model and vector abstract data model to the calling application for all supported

formats. It also comes with a variety of useful command line utilities for data translation and processing.

OpenCV – standard Image processing library

OpenCV (Open Source Computer Vision Library) is an open source computer vision and machine learning

software library. Being a BSD-licensed product, OpenCV makes it easy for businesses to utilize and modify

the code. The library has more than 2500 optimized algorithms, which includes a comprehensive set of both

classic and state-of-the-art computer vision and machine learning algorithms. These algorithms can be used

to detect and recognize faces, identify objects, classify human actions in videos, track camera movements,

track moving objects, extract 3D models of objects, produce 3D point clouds from stereo cameras, stitch

images together to produce a high resolution image of an entire scene, find similar images from an image

database, remove red eyes from images taken using flash, follow eye movements, recognize scenery and

establish markers to overlay it with augmented reality, etc.

Python Imaging Library – Basic raster image support

The Python Imaging Library (PIL) adds image processing capabilities to your Python interpreter. This

library supports many file formats, and provides powerful image processing and graphics capabilities.

NumPy is the fundamental package for scientific computing with Python. It contains among other things:

a powerful N-dimensional array object

• sophisticated (broadcasting) functions

• tools for integrating C/C++ and Fortran code

• useful linear algebra, Fourier transform, and random number capabilities

Matplotlib – For Data analysis, visualization, etc

Python standard library

Tkinter library – for GUI frontend programming. Tkinter is Python's de-facto standard GUI (Graphical User Interface) package.