Zhaoying Wei
GEOG 8350
Comparison of Land use
detection in Niagara Falls using
LIDAR and orthoimagery
Outlines
 Goal
 Area of Interest
 Data
 Method and Result
 Conclusion and
Future Work
Land use
detection
orthophoto
LIDAR
Goal
AOI
Data
Data
• point cloud Lidar data
• orthoimagery
Tools
•LAStools
•Lidar Analyst
•ArcGIS 10.0
• ArcGIS 10.0
• Erdas
• EN...
Method (LIDAR)
 Extract Bare earth
• strip the existing classes from the LAS file and
set the coordinate system and proje...
Method (LIDAR)
From
From raw LAS
 Extract Bare earth
Method (LIDAR)
Extract buildings footprints
• compute the height above the ground
• Classify trees and Buidlings (treeNbu...
Extract building footprints
• point cloud building extraction
Method (LIDAR)
Extract building footprints
• edit building
Method (LIDAR)
Before After
Remove building
Add to building
Merge building
Before After
Extract building footprints
Method (LIDAR)
Create new building and close holes
Final
footprints
Extract tree
• point cloud tree extraction
• visually edit forests and trees
Method (LIDAR)
Method (Orthophoto)
Supervised classification
• Create the signature file and AOI for each class
• Combine the signature ...
Method (Orthophoto)
Reclassify
• manually digitize AOI
• assign new class value to water
Conclusion
 LIDAR:
• vector results consistent with real
terrains
• limited classes
Orthoimage
• numerous classes
• low ...
Future work
Measure accuracy of the classification result
 improve the quality of signature
 better water detection
 o...
Upcoming SlideShare
Loading in …5
×

Land Use Detection

93
-1

Published on

Published in: Technology, Business
0 Comments
0 Likes
Statistics
Notes
  • Be the first to comment

  • Be the first to like this

No Downloads
Views
Total Views
93
On Slideshare
0
From Embeds
0
Number of Embeds
0
Actions
Shares
0
Downloads
6
Comments
0
Likes
0
Embeds 0
No embeds

No notes for slide

  • ----- Meeting Notes (12/5/12 09:57) -----
    I generate the land use classification by using LIDAR and orthoimagery respectively.

  • ----- Meeting Notes (12/5/12 10:07) -----
    My goal is to use current data and method that we learn in class to find a better way to do the land use detection

  • ----- Meeting Notes (12/5/12 10:25) -----
    This is where my AOI is in Google map
    Here are two data that I use
  • Further filter ground.las
    I can get better result by using groundonly las instead of raw las file to extract bare earth
  • Six classes set
  • Worthy to try
  • Land Use Detection

    1. 1. Zhaoying Wei GEOG 8350 Comparison of Land use detection in Niagara Falls using LIDAR and orthoimagery
    2. 2. Outlines  Goal  Area of Interest  Data  Method and Result  Conclusion and Future Work
    3. 3. Land use detection orthophoto LIDAR Goal
    4. 4. AOI
    5. 5. Data Data • point cloud Lidar data • orthoimagery Tools •LAStools •Lidar Analyst •ArcGIS 10.0 • ArcGIS 10.0 • Erdas • ENVI
    6. 6. Method (LIDAR)  Extract Bare earth • strip the existing classes from the LAS file and set the coordinate system and projection (noclass.las) • classify the ground points (ground.las) • create the only ground points (groundonly.las) • generate DEM of groundonly .las • extract bare earth DEM from groundonly DEM
    7. 7. Method (LIDAR) From From raw LAS  Extract Bare earth
    8. 8. Method (LIDAR) Extract buildings footprints • compute the height above the ground • Classify trees and Buidlings (treeNbuilding.las) • generate new bare earth for building and trees extraction
    9. 9. Extract building footprints • point cloud building extraction Method (LIDAR)
    10. 10. Extract building footprints • edit building Method (LIDAR) Before After Remove building Add to building Merge building
    11. 11. Before After Extract building footprints Method (LIDAR) Create new building and close holes Final footprints
    12. 12. Extract tree • point cloud tree extraction • visually edit forests and trees Method (LIDAR)
    13. 13. Method (Orthophoto) Supervised classification • Create the signature file and AOI for each class • Combine the signature file of all classes
    14. 14. Method (Orthophoto) Reclassify • manually digitize AOI • assign new class value to water
    15. 15. Conclusion  LIDAR: • vector results consistent with real terrains • limited classes Orthoimage • numerous classes • low quality and accuracy, water
    16. 16. Future work Measure accuracy of the classification result  improve the quality of signature  better water detection  object-based classification Fusion of LIDAR and aerial image
    1. A particular slide catching your eye?

      Clipping is a handy way to collect important slides you want to go back to later.

    ×