Our Spatial Planet

IGARSS 2010 in Hawaii was a few thousand strong in terms of attendees. There were many presentations we would have liked to attend, but it is difficult to be in 9 streams at once. The topics can be roughly summarized as sensors, techniques and applications.

The new exciting sensors from our perspective include Tandem-X, Tandem-L, Desdyni, Biomass, SMOS and SMAP. Radar seems to be playing a huge role in future missions and there are going to be more space borne Lidars. The focus is on reliable global observation of the terrain independent of weather. There is some importance in observation of land and ocean weather as well, but using tried and tested methods. If the human impact on global warming is in debate, the human impact on clarity of earth observation is certainly being felt. Some of the most sensitive L-band radiometers are being affected by the microwave glow over Europe, India and China.

Figure 6: Tandem-L mission concept with two acquisition modes.

The techniques are evolving to allow more and more automation as the data volume out-paces the availability of skilled human interpreters. New classification techniques, particularly segmentation, support vector machines, random forests and active learning are becoming more popular.

All these focus on applications with commercial, administrative and environmental implications. On the forefront is biomass estimation for balancing the carbon budget. Then there is urban impact assessment and mapping. Urbanization is proceeding at an unprecedented rate and many governments are struggling to map new urban developments. There are also global mapping projects using ALOS-PALSAR and LandSAT. There is focus on community remote sensing. Some agricultural applications are also becoming important as the efficiency required for production increases.

Overall the conference was exciting enough to keep us in-doors in-spite of the great weather outside in Waikiki.

With the advent of easy 64 bit computing and multi-core CPU’s time has come for every company to have its own super-computer, not just research groups in Universities. The nature of data processing in Remote sensing lends itself easily to parallelization. Most of the imagery data is multi-band 2-dimensional rasters, 3-dimensional matrices from a mathematicians viewpoint. From a computational task the major processes of geometry correction, spectral correction, collation of frames and compression for transmission can be done on a pixel by pixel or block by block basis allowing segmentation of the tasks to multiple processors.

With the uptake of more projects single machine based processing became an issue at Apogee and solutions were sought for continuously running general processing on large high resolution datasets. The processing chains have been automated and set up on a beowulf server farm with quad-core CPU’s and identical diskless systems to run in parallel using a Message Passing Interface (MPI) or Parallel Python. This solution enables us to quickly finish larger projects, serve more clients and develop more elaborate processing, since computational complexity is not a barrier any more. The processes are also more fault tolerant due to the use of a more stable and syncronized operating environment with regular checkpointing for major outage recovery.

Beowulf Cluster System Diagram