Classification methods for UAS data

Center for Geospatial Analytics at North Carolina State University

Corey White

Outline

  • Pixel, Object, Image Classification
  • Supervised vs Unsupervised learning
  • Segmentation
  • Feature Extraction
  • Model Selection and Validation
  • Real-time UAS analysis

What is image classification?

  • Labling images with sematic lables

Car, house, lake, tree, forest, etc..

Image classification applications

  • Land cover/land use classifcation
  • Flood detection
  • Species Identifcation
  • Surveillance
  • Object Tracking

UAS Data Characteristics

  • High spatial resolution (1–10 cm)
  • Limited spectral bands (RGB or multispectral)
  • High within-class variability
  • Shadows, BRDF effects, and illumination differences

Types of classification

Pixel-based classification

Each pixel is given a semantic label

Object-based classification

The image is segmented into objects (groups of pixels) and the objects are classified

Semantic Scene classification

The entire image is classified into a semantic scene

Machine Learning Methods

  • Supervised Learning
  • Unsupervised Learning
  • Deep Learning

Supervised Learning

  • Labeled features are used to train ML model

  • May require significant amount of training data.

  • Subject to the nonstationarity nature of spatial-temporal data

  • Random Forest, Gradient Boot, SVM

Unsupervised Learning

  • Data is classified through statistical modeling.

  • No training data is required

  • Classes are not defined and require interpretation

  • k-means, ISODATA

Deep Learning

  • Artificial Neural Network (ANN)
  • Convolutional Neural Network (CNN)
  • Deep Neural Networks (UNET, )

Spectral Properties

  • Every surface has a unique spectral reflectance curve
  • UAS sensors capture reflected light in discrete bands:
    • Red, Green, Blue (visible)
    • NIR (if multispectral)
    • Thermal
    • etc..
  • Used to derive spectral indices for classification

Spectral Bands

RGB

red green blue

Common Spectral Indices

Index Formula Purpose
NDVI (NIR - Red) / (NIR + Red) Vegetation health
VARI (G - R) / (G + R - B) Green vegetation from RGB
NDWI (G - NIR) / (G + NIR) Water detection
NDBI (SWIR - NIR) / (SWIR + NIR) Built-up detection

Feature Engineering

  • Normalized Indices
    • NDVI, NDWI, VARI, etc..
  • Low and High Pass Filters
    • Low Pass - Smoothing Kernal
    • High Pass - Edge Detection
  • Textural features
    • Grey level co-occurrence matrix (GLCM)
      • Angular Second Moment
      • Contrast
      • Correlation
  • Geometric Characteristics
  • Principal component analysis (PCA)

VARI \[ VARI = \frac{(Green - Red)}{(Green + Red - Blue)} \]

Sampling Methods

  • Random Sample
    • Sample the entire spatial extent \(n\) number of locations
  • Cluster Sampling
    • Sample by different clusters
  • Stratified Random Sample
    • Sample by different classes (e.g. Developed, Undeveloped)

Training a model

  • Break data into training and testing dataset 80/20
  • Avoid overfitting data
  • May require a large volume of data

Classification Results

Model Validation

Confusion Matrix

Reference  Predicted Forest Water Urban Agriculture User’s Accuracy (%)
Forest 50 2 3 0 90.9
Water 1 45 0 4 88.2
Urban 2 1 40 7 80.0
Agriculture 0 3 5 42 85.7
Producer’s Accuracy (%) 94.3 86.5 83.3 79.2
  • \(Overall\ Accuarcy = \frac{Correctly\ classified\ samples}{Total\ samples}\)
  • Kappa coefficient (Cohen’s Kappa): inter-rater agreement

Challenges

  • Handling multiple spatial scales
  • Nonstationarity
  • Processing/Training