Body: <p>I'm starting to get involved into machine learning but still have some troubles to select the approriate tool or algorithm.
  My basic task is to compare remotly sensed images of individual trees at two time steps (e.g. 2012 and 2016). Between images I'd like to determine if any individual tree was cut or not.</p>
  <p>Problems that arise are different colors, slightly different shapes (due to lateral growth of tree crowns) and distortions of perspective (due to off-nadir images, meaning that the position of the plane in relation to the tree differs between both time steps).</p>
  <p>When I determine by &quot;hand&quot;/eye if trees were cut, I heavily rely on the shadows of trees facing to the upper left corner.</p>
  <p>Here are some images to illustrate my problem (left image: 2012, right image: 2016):</p>
  <p>The tree in the image below remains, but the colors differs and the shape (perimeter) differs slightly.
  <a href="https://i.sstatic.net/KSwW2.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/KSwW2.png" alt="Tree remains" /></a></p>
  <p>Here, the tree in the image below remains as well but due to distortions of persective both images look (from a machine point of view) quite different.</p>
  <p><a href="https://i.sstatic.net/TaAFY.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/TaAFY.png" alt="Tree remains2" /></a></p>
  <p>The tree in image below was cut and the area around was converted to urban area/ buildings.</p>
  <p><a href="https://i.sstatic.net/WDE5E.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/WDE5E.png" alt="Tree cut" /></a></p>
  <p>And finally, the tree in the image below was cut but the understorey and accompanying vegetation remains.<br />
  <a href="https://i.sstatic.net/H13Go.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/H13Go.png" alt="Tree cut2" /></a></p>
  <p>I'll have to do this comparison many thousand times - therefor I'd like to train an neural network.
  My focus of interest are those trees that were cut between both images. I'd tried to use the NDVI value (&quot;greennes of vegetation&quot;) to detect major changes but this approach worked only for some cases.</p>
  <p>During my research I found many image change detection papers using CNNs but they seemed to be focussed on more substantial changes like grassland conversion into buildings or deforestation ...</p>
  <p>Some additional research lead me to face verification algorithms that in theory might fit:
  I have two known images to compare and like human faces there might be minor changes (perspective, haircut ...) but the verification still works.</p>
  <p>Is this approach possible or do I miss some important tool or is this kind of tree verification even to difficult???</p>
  <p>Thank you for your time and responses!</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2022-03-17T11:19:02.227Z
Comments.Text: Do you have already labelled data or are you leading toward an unsupervised methods?
Comments.UserDisplayName: Edoardo Guerriero
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2022-03-17T12:25:25.193Z
Comments.Text: Hi Edoardo! I could generate labeled data easily from a step prior in the processing chain. But it is weak data (not picked by a human) and comes in a ratio of 3:1 (~5000 labeled images to 1700 not-labeled images). Thanks!
Comments.UserDisplayName: andreas
Tags: <convolutional-neural-networks><image-recognition>
Title: Is AI able to detect major changes in pairs of images while ignoring minor changes (due to tree crown growth, color and perspectiv disstortions)?
UserDisplayName: andreas
ThreadText: # Is AI able to detect major changes in pairs of images while ignoring minor changes (due to tree crown growth, color and perspectiv disstortions)?
  
  (Asked by: username_0 on 2022-03-17)
  
  I'm starting to get involved into machine learning but still have some troubles to select the approriate tool or algorithm.
  My basic task is to compare remotly sensed images of individual trees at two time steps (e.g. 2012 and 2016). Between images I'd like to determine if any individual tree was cut or not.
  
  Problems that arise are different colors, slightly different shapes (due to lateral growth of tree crowns) and distortions of perspective (due to off-nadir images, meaning that the position of the plane in relation to the tree differs between both time steps).
  
  When I determine by "hand"/eye if trees were cut, I heavily rely on the shadows of trees facing to the upper left corner.
  
  Here are some images to illustrate my problem (left image: 2012, right image: 2016):
  
  The tree in the image below remains, but the colors differs and the shape (perimeter) differs slightly.
  [![Tree remains](https://i.sstatic.net/KSwW2.png)](https://i.sstatic.net/KSwW2.png)
  
  Here, the tree in the image below remains as well but due to distortions of persective both images look (from a machine point of view) quite different.
  
  [![Tree remains2](https://i.sstatic.net/TaAFY.png)](https://i.sstatic.net/TaAFY.png)
  
  The tree in image below was cut and the area around was converted to urban area/ buildings.
  
  [![Tree cut](https://i.sstatic.net/WDE5E.png)](https://i.sstatic.net/WDE5E.png)
  
  And finally, the tree in the image below was cut but the understorey and accompanying vegetation remains.  
  [![Tree cut2](https://i.sstatic.net/H13Go.png)](https://i.sstatic.net/H13Go.png)
  
  I'll have to do this comparison many thousand times - therefor I'd like to train an neural network.
  My focus of interest are those trees that were cut between both images. I'd tried to use the NDVI value ("greennes of vegetation") to detect major changes but this approach worked only for some cases.
  
  During my research I found many image change detection papers using CNNs but they seemed to be focussed on more substantial changes like grassland conversion into buildings or deforestation ...
  
  Some additional research lead me to face verification algorithms that in theory might fit:
  I have two known images to compare and like human faces there might be minor changes (perspective, haircut ...) but the verification still works.
  
  Is this approach possible or do I miss some important tool or is this kind of tree verification even to difficult???
  
  Thank you for your time and responses!
  
  **Tags:** convolutional-neural-networks, image-recognition
  
  > **Comments:**
  > **username_1**: Do you have already labelled data or are you leading toward an unsupervised methods?  
  > **username_0**: Hi username_1! I could generate labeled data easily from a step prior in the processing chain. But it is weak data (not picked by a human) and comes in a ratio of 3:1 (~5000 labeled images to 1700 not-labeled images). Thanks!  
  
  ---
  
  ## 0 answers
  
  