After a 5-year hiatus, we have decided to revive the DIRSIG blog in an effort to increase communication with the DIRSIG user community. We get a lot of emails asking the same questions and it would be a lot easier if we wrote up the answers to these questions and published them in an easy to find location. But the primary reason for that is that there is a lot going on these days and we want to tell everyone about it.
Over the next few weeks and months our goal is to try and get everyone up to speed on important developments in DIRSIG land. Specifically, we will start getting everyone up to speed on DIRSIG5, which is the rewrite of DIRSIG4 that uses a new numerical radiometry approach and leverages multi-threading and multi-processing (compute clusters). We are also discuss our progress on GPU-acceleration of some key parts of the DIRSIG calculation (spoiler alert, it is incredibly difficult to migrate an entire code like DIRSIG to the GPU). It should also be noted that DIRSIG5 has a large number of plugin interfaces that will allow end users/developers to extend the DIRSIG model in key areas. For example, highly customized sensor models can be coded and injected into the DIRSIG calculation, which avoids the clumsy approach of creating very large, highly spatially and spectrally oversampled data cubes that are externally degraded by sensor models.
In years past, we also used this blog to discuss in-house projects and how we utilize DIRSIG. We expect to revive that aspect as well, providing insights into student and staff research projects and how we solved problems that others might be exploring as well.
So please stay tuned or even subscribe to automatically get updates.
Over the next few weeks and months our goal is to try and get everyone up to speed on important developments in DIRSIG land. Specifically, we will start getting everyone up to speed on DIRSIG5, which is the rewrite of DIRSIG4 that uses a new numerical radiometry approach and leverages multi-threading and multi-processing (compute clusters). We are also discuss our progress on GPU-acceleration of some key parts of the DIRSIG calculation (spoiler alert, it is incredibly difficult to migrate an entire code like DIRSIG to the GPU). It should also be noted that DIRSIG5 has a large number of plugin interfaces that will allow end users/developers to extend the DIRSIG model in key areas. For example, highly customized sensor models can be coded and injected into the DIRSIG calculation, which avoids the clumsy approach of creating very large, highly spatially and spectrally oversampled data cubes that are externally degraded by sensor models.
In years past, we also used this blog to discuss in-house projects and how we utilize DIRSIG. We expect to revive that aspect as well, providing insights into student and staff research projects and how we solved problems that others might be exploring as well.
So please stay tuned or even subscribe to automatically get updates.
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