Automate generation of DEM from DSM in Forest Area using artificial neural networks - Bandara, K. R. M. U.; Lal Lamarakoon; kamiya, Yoshikazu; Shrestha, Rajendra Prasad; Geoinformatics Center, Asian Institute of Technology; North {u2013}South Center, Zurich, Switzerland; Institute of Photogrammetry & Remote Sensing, ETH, Hoenggerburg, Zurich, Switzerland (Pathum Thani : Asian Institute of Technology, 2011)
Thisresearch was carried out touse the Digital Surface Model (DSM) to obtain a Digital Terrain Model (DEM) in a forest area by using Artificial Neural Networks (ANN) instead of removing DSM point clouds and then interpolation the formed hollow area using surrounding DEMpoints. The study was carried out for one of the dense forest areas ofMeegahakivula in Badulla District, Sri Lanka wherethe elevation is varying from 230 m to 403 m with approximately 16 m mean heights of trees. Initially, onlythe perimeter DEMdata was used to train the ANN and secondly, the training was done using only nine well distributed DEMpoints inside the forest area. An application oriented ANN software module was designed with the facilities to use all relevant parameters and it can be used to trainand use for any coordinate system transformations. The developed application oriented ANN was trained by using sample data. It was trained to 1.5 m accuracy for 172 points of the perimeter and 3.9 m for the nine points. By using trained ANN, the whole DSMdata of the area and the extracted DSM with the range of [min, max] and [mean {u2013}standard deviation, mean + standard deviation] of the used sample data sets were projected to DEM. The above projected DEMwith the two ranges were interpolated using severalinterpolation methods. All ANN projected DEMs were compared with the reference DEM to check the fitness of the ANN projected DEMsand obtained the RMSE as the deviation of projected DEMs with reference DEM. The highest accuracy as the lowest overall deviation (lowest RMSE)was obtained by the [min, max] range DSM projection with the nine points trained ANN and by Topo to Raster interpolation method and it was 1.240 m. Other DEMaccuracies were also closer to this and it was proven that the ability to project DSM to DEMwith ANN.DEM projected by ANN can be used to slope determination, landslide monitoring, soil erosion monitoring, orthophoto generation, eliminate of foreshortening and layover of SAR images, etc. according to the accuracy obtained presently, but has to be validated with different forest areas as well as all other areas separately. Further, the developed program has to be improved for mini-batch processing as well as to use for other applications.
A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Remote Sensing and Geographic Information Systems
Thesis (Ph.D.) - Asian Institute of Technology, 2011
Keywords: Remote-sensing images; Forests and forestry -- Remote sensing