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I don't understand how we can have a the technology for a drone but we can't fly it close enough to get a good quality photograph. I am all for limiting casualt

I don't understand how we can have a the technology for a drone but we can't fly it close enough to get a good quality photograph. I am all for limiting casualties for the US, but how is it not a war crime to just kill people without 100% proof they are the enemy? And seriously, if cell phones are going to 39 megapixels, why can't a drone send back a good photo?

rwmj 13y ago
The advantage of a drone is it can "lurk" too far away to be easily seen. Building a camera that can take very clear pictures from a long way away is impossible (because of distortion from the air and weather). If you make the drone itself smaller, then you hit the Rayleigh limit: https://en.wikipedia.org/wiki/Rayleigh_criterion#Explanation https://en.wikipedia.org/wiki/Rayleigh_criterion#Explanation
- stevenrace 13y ago
  Additonally, bandwidth needs to be taken into account. Even military satellite uplinks are slow and in many cases feeds are being sent to a ground station and then converted into digital for uplink.
- dchichkov 13y ago
  Actually you are sort of wrong in both cases. Because a) drone is moving; b) drone can take multiple pictures; c) multiple drones can take pictures simultaneously. In case of multiple drones taking pictures, these can be synchronized using GPS time, down to a microsecond. And knowing the GPS coordinates of each drone you can recover the image. Pretty much with arbitrary quality. (& If you are really good at it and the object is stationary, you can do it even through heavy fog. I'm not kidding, you really can - I can point you to some fancy research in computer vision, that allows you to do just that.).
  - im3w1l 13y ago
    >I can point you to some fancy research in computer vision, that allows you to do just that. Please do
    - dchichkov 13y ago
      Oh. I've tried finding that exact publication - they took a video of a city scene with dense fog/clouds in the background. If you'd play the video you couldn't see any shapes through the fog. At all. But after passing the whole video through reconstruction/de-noising, they've been able to recover the background (you could see s mountain, instead of dense fog). It is not surprising and it's a pretty standard result actually. Can be called by different names (sparse sensing, denoising, superresolution, ...) and done by different methods (BP, LASSO, ...), but it always comes down to having a model (with some priors, e.g sparsity prior!) and using that model to re-project the data from your measurement projection into the projection of some interest to you. I have no idea at what level you are, so I'd suggest this very well presented lecture (1 hour total): Compressed Sensing by Terence Tao http://www.youtube.com/playlist?list=PLC94A02A1218B24DF http://www.youtube.com/playlist?list=PLC94A02A1218B24DF if you want to learn more. Examples of image recovery are in the part 5, part 6.

rwmj 13y ago

The advantage of a drone is it can "lurk" too far away to be easily seen. Building a camera that can take very clear pictures from a long way away is impossible (because of distortion from the air and weather). If you make the drone itself smaller, then you hit the Rayleigh limit: https://en.wikipedia.org/wiki/Rayleigh_criterion#Explanation https://en.wikipedia.org/wiki/Rayleigh_criterion#Explanation

stevenrace 13y ago
Additonally, bandwidth needs to be taken into account. Even military satellite uplinks are slow and in many cases feeds are being sent to a ground station and then converted into digital for uplink.
dchichkov 13y ago
Actually you are sort of wrong in both cases. Because a) drone is moving; b) drone can take multiple pictures; c) multiple drones can take pictures simultaneously. In case of multiple drones taking pictures, these can be synchronized using GPS time, down to a microsecond. And knowing the GPS coordinates of each drone you can recover the image. Pretty much with arbitrary quality. (& If you are really good at it and the object is stationary, you can do it even through heavy fog. I'm not kidding, you really can - I can point you to some fancy research in computer vision, that allows you to do just that.).
- im3w1l 13y ago
  >I can point you to some fancy research in computer vision, that allows you to do just that. Please do
  - dchichkov 13y ago
    Oh. I've tried finding that exact publication - they took a video of a city scene with dense fog/clouds in the background. If you'd play the video you couldn't see any shapes through the fog. At all. But after passing the whole video through reconstruction/de-noising, they've been able to recover the background (you could see s mountain, instead of dense fog). It is not surprising and it's a pretty standard result actually. Can be called by different names (sparse sensing, denoising, superresolution, ...) and done by different methods (BP, LASSO, ...), but it always comes down to having a model (with some priors, e.g sparsity prior!) and using that model to re-project the data from your measurement projection into the projection of some interest to you. I have no idea at what level you are, so I'd suggest this very well presented lecture (1 hour total): Compressed Sensing by Terence Tao http://www.youtube.com/playlist?list=PLC94A02A1218B24DF http://www.youtube.com/playlist?list=PLC94A02A1218B24DF if you want to learn more. Examples of image recovery are in the part 5, part 6.