The increasing volume of data produced by hyperspectral image sensors have forced researches and developers
to seek out new and more ecient ways of analyzing the data as quick as possible. Medical, scientic, and
military applications present performance requirements for tools that perform operations on hyperspectral sensor
data. By providing a hyperspectral image analysis library, we aim to accelerate hyperspectral image application
development. Development of a cross-platform library, Libdect, with GPU support for hyperspectral image
analysis is presented.
Coupling library development with ecient hyperspectral algorithms escalates into a signicant time invest-
ment in many projects or prototypes. Provided a solution to these issues, developers can implement hyperspectral
image analysis applications in less time. Developers will not be focused on implementing target detection code
and potential issues related to platform or GPU architecture dierences.
Libdect's development team counts with previously implemented detection algorithms. By utilizing proven
tools, such as CMake and CTest, to develop Libdect's infrastructure, we were able to develop and test a prototype
library that provides target detection code with GPU support on Linux platforms. As a whole, Libdect is an
early prototype of an open and documented example of Software Engineering practices and tools. They are
put together in an eort to increase developer productivity and encourage new developers into the eld of
hyperspectral image application development.