Crown Segmentation

Segment Tree Crowns from RGB, Hyperspectral and LiDAR Images using Clustering techniques

Dataset

NIST DSE Plant Identification with NEON Remote Sensing Data

• Training Inputs(GeoTIFF Format)
  • 37 RGB Images (320,320,3)
  • 43 Hyperspectral Images (80,80,420)
  • 43 LiDAR Images (80,80)
  • 43 LiDAR PointCloud 3D Maps (20665,20665,20665)
• Training Outputs
  • ShapeFiles highlighting Tree Crowns for 70% of the input data
• hyper_bands.csv containing noisy bands for hyperspectral images

Download Link
Modify ~/utils/datapaths.py accordingly as per extraction/unzip tool used.

The code expects Dataset to be extracted as ~/ECODSEdataset/ECODSEdataset/

Observations and Inferences

- Most RGB Images either show a blur or tilt or both along with not matching with their hyperspectral and lidar counterparts
    - RGB Images Not Used
- Higher bands seem to have noisy information attributing to poor sensor calibration
    - Smoothed out using convolutions
    - End noise clipped out
- Three LiDAR Elevation Maps have missing data
    - Shape of (77,80) instead of (80,80)
    - Not used to create composite masks with ndvi information
    - Clustering outputs available along with NDVI

Prerequisites

- Ubuntu 18.04 LTS Recommended
    - Default Python3 installation  
    - Pip3 Installation
        - sudo apt-get -y -q install python3-pip
- GDAL Installation(Ubuntu instructions only)
    - sudo apt-get -y -q install gdal-bin
    - sudo apt-get -y -q install python3-numpy
    - sudo apt-get -y -q install python3-gdal
- Install Requirements
    - pip3 install -r requirements.txt

Additional Information

NDVI Reference Range

NDVI	Vegetation Type
-1	Water
-0.1 - 0.1	Rock/Dirt
0.2 - 0.4	Shrubs/Grasslands
>0.4	Trees/Rainforests

AppConfig

All Parameters used are present in ~/appConfig.ini and can be modified as per need

Outputs

All Outputs are stored in ~/OUT/ following a similar filename pattern structure to the dataset

Methodology

• Convert all GeoTIFF Files into easy to access and modify matlab files (.mat)
  • python3 utils/convert_to_mat.py --help
    • python3 utils/convert_to_mat.py --file-type=rgb
    • python3 utils/convert_to_mat.py --file-type=lidar
    • python3 utils/convert_to_mat.py --file-type=hyper
• View Images
  • Hyperspectral Image Bands
    • python3 utils/display_bands --help
      • python3 utils/display_bands --image-id=23 -x=40 -y=25
    • Add --trim-wavelengths=False until noisy bands have been purged from the hyperspectral images(executed in the following instruction)
• Hyperspectral Image Dimensionality Reduction
  • Purge Noisy Bands and Smooth out intensities in the higher bands for all pixels in all images
    • python3 utils/reduce_dimensionality.py --help
      • python3 utils/reduce_dimensionality.py
• Create Normalized Difference Vegetation Index(NDVI) for each hyperspectral image
  • python3 utils/calculate_ndvi.py
• Convert created NDVI from .tif to .mat format
  • python3 utils/convert_to_mat.py --file-type=ndvi
• Threshold NDVI for Rainforests/Trees to a binary image and use as a mask over LiDAR CHM to further segment LiDAR into tree or no tree - assists in differentiating further between trees and other artifacts
  • python3 utils/create_ndvi_lidar_mask.py
• Execute Clustering Algorithms on Images(store output labels in .mat files)
  • python3 clustering.py
    • K Means
    • Gaussian Mixture Model
    • Fuzzy C Means
    • Self Organizing Maps
    • Spectral Clustering
• Put the composite NDVI + LiDAR CHM as a mask over the cluster labels ensuring everything except crowns is soft thresholded to zero
  • python3 mask_clusters.py

Future Work

• Being FCM, SOM and Spectral Clustering to working shape
• Convert ShapeFile Training Outputs to Image and then a matrix for comparison with
• Performance Evaluation
  • Compare Output Labels with available shapefile outputs using Jaccard's Coefficient to fine tune clustering algorithms' hyperparameters