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agraham1

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  1. A little bit late for a reply- but the reason you are likely getting that specific error message is because you didn't calculate the statistics. Set the skip factors to 1 and recalculate the stats then you should be able to run the PCA. Cheers
  2. The thing is that each image needs to be cropped before stacked- otherwise there will be some offset with the combined bands. Batch cropping is key. I looked into avenza but unfortunately its a little costly and not sure if it does batch cropping. I do have erdas, arcgis and agisoft in my hands....
  3. Hi All, I am working with two cameras taking stereo images, one camera is 3 band and the other is one band. I triggered the cameras to capture at the same time, but there is obviously a slight offset in the photo field of view when comparing each camera. So I would like to crop the images so they cover the same FOV, so ultimately I would be able to stack the images in 4 bands. Is there any way using erdas or some other software where I could do this quickly for hundreds of photos? Thanks! Alex
  4. That is some really awesome stuff you are doing. I am now wondering the application of underwater rovers equipped with hyperspectral sensors to detect coral health. We have an underwater rover at the university- its a great idea for a future study- once lightweight hyperspec. sensors become cheaper!
  5. Hey Bruce well, I have a spectrometer handy in the lab so I will be able to fill out the red edge for comparison... it is not however my thesis. I am more looking at canopy chlorophyll response as an indicator of stress in wheat. Anyways, do you use hyperspectral imagery to analyze coral reef health? That really sounds incredible!
  6. Hi Bruce, Thanks for your reply. In respect to leaves- you get high chlorophyll absorbance in the red, low in the NIR which make up the red edge- and I want to map this whole curve. But if I'm getting an average from Band 1, I may only get an average reading somewhere along the curve- I wont have to low and high values.
  7. Hi all, Just a question regarding the fundamentals of sensor channels. Its surprisingly hard to find any info about this online- and in journal articles. Lets say I have two multispectral sensors- each with a red edge channel: 680-730nm 710-720nm What I am wondering is whether for broader channels, like #1, does the reflectance represent an average over that spectrum? I have always assumed it does. In which case, it would be better to have a couple narrow channels in the red edge spectrum to fill out the red edge curve. #1 would then not be ideal because you wont be able to quantify the start and end of the curve- is what I am thinking. Let me know your thoughts.
  8. Yes, exactly. Hyperspectral sensors are still quite far away from being an economically valid solution for farmers and even crop consultants; the sensors are just simply too expensive at the moment. Currently, the best way in detecting specific stress is perhaps having knowledge in the times that certain stressors or diseases typically appear- and to try to detect them in respect of chlorophyll or nitrogen flux at certain windows of the crop's development- is what I am thinking: this is sort of a work around solution. The question is, how early can these be detected with just reflectance data alone. If its not detectable before or at the time a farmer may typically apply blanket treatment of, say a fungicide, then its impractical. I am using micasense - (first number is center wavelength, second number is bandwidth): 475 - 20 560- 20 670-10 720-10 840-40 spatial resolution ~5cm/pixel @60 metres. Im mounting this on a UAV and capturing a mosaik of the field crops. I'll check out Huete's work- thanks. If you'd like, you can private message me on this forum to talk more in depth. These bands are fairly narrow- my main concern is the narrow red edge channel. Anyways, thanks for your help! Alex
  9. Hello mamadouba- Thanks for your reply- I coincidentally checked this thread for the first time in a while and was happy to see that you replied yesterday. Things have changed slightly since my original post--I still have the same sensor and am now more focused towards identifying locations of wheat stress within a single crop over the course of a season. This of course entails many frequent flights and ground truthing work. I am considering the identification of stress with red edge or some combination of the bands- not really sure yet how I will make this work-lots of reading to do. Mainly I am curious as to which types of stress (fusarium, leaf rust, etc.) I can detect- and if I can detect them early- and if they will be distinguishable. My sensor is high resolution (sub 5cm/pixel)- just wondering if you had any ideas or recommendations for me. Thanks
  10. Hi Everybody- this is my first post! I have a specific question regarding SAM and its usefulness in classifying multispectral imagery (not hyperspectral). I am thinking of perhaps getting endmember data with a field spectrometer for different plant types, and then using the endmember data to classify a multispec. image. However, how would this work? As the multispec sensor is only 5 narrow channels, (RGB, red edge and NIR) and the spectrometer would likely cover all of these channels. If I use spectrometer endmember data, it would only classify in relation to whats available in the bandwidth of the 5 channels.....is what I am assuming. Do you guys think this could be a robust way to distinguish between plant types? The multispec. sensor is high spatial res. (5cm/pixel). Let me know what you all think . Thanks! Alex
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