Object Features For Classification in Remote Sensing

Remote sensing
OBIA
eCognition
Object-based classification using eCognition, making use of advanced segmentation, feature selection, and hierarchical segmentation.

Introduction

The classification of remote sensing imagery has traditionally relied on pixel-based analysis, which considers spectral values . However, object-based classification provides a more advanced approach by incorporating shape, texture, and contextual relationships.

This study explores the segmentation and classification process applied to a Quickbird image of Salzburg and pseudo airquality image, using eCognition software to extract meaningful features for classification.

Segmentation

Segmentation is a fundamental step in object-based image analysis. Two segmentation approaches were used:

  1. Chessboard Segmentation: This method creates uniform grid-based objects, which is useful for initial feature assessment. This method is not suitable for distinguishing meaningful land cover features. A comparison of segmented objects, such as a ship and water for example, revealed:

    • The NDVI for water is negative due to its low near-infrared (NIR) reflectance and high visible reflectance and the ship exhibits higher reflectance across all bands, likely due to its surface color.

    • However, because all segments are of equal size, the segmentation does not align well with natural object boundaries, making it unsuitable for this classification task.

  • Multiresolution Segmentation: This technique groups pixels into meaningful objects based on spectral and spatial properties. It is more suited for real-world classification as it captures object boundaries more accurately.

Feature Selection and Object Analysis

Object features play a crucial role in classification. The key categories of features examined in this study include:

  • Spectral Features: Mean and maximum pixel values per band.

  • Geometrical Features: Shape index and object area.

  • Textural Features: Haralick-based texture measures.

  • Hierarchical Features: Relationships between super-objects and sub-objects.

  • Class-Related Features: Proximity to specific classified objects

Classification

Step 1: NDVI Calculation

To classify vegetation, the Normalized Difference Vegetation Index (NDVI) was computed using:

NDVI = (NIR-Red)/(NIR+Red)

Objects with NDVI values above 0.25 were classified as vegetation, while those below -0.15 were classified as water bodies.

Step 2: Assigning Object Classes

Using the “Assign Class” algorithm:

  • Vegetation was classified using the NDVI threshold.

  • Water bodies were extracted based on low NDVI values.

  • A “Boat” class was assigned using the “Relative Border to Water” feature, identifying objects completely surrounded by water.

Step 3: Integrating Air Quality Data

The air quality raster layer was used to refine the vegetation class:

  • Vegetation was split into high-air-quality (in the image light blue color) and low-air-quality (in the image below green color) areas based on pixel values from the air quality dataset.

  • This created subclasses, allowing for a more detailed environmental analysis.

Hierarchical classification

To refine object classification, hierarchical segmentation was applied by creating a lower-level segmentation from an existing level using multiresolution segmentation with a smaller scale parameter. This approach enhances detail by generating smaller objects, capturing fine structures such as individual trees, buildings, or narrow water bodies. The multi-level segmentation enables a hierarchical classification process, allowing for the refinement of class boundaries and improving overall classification accuracy.

Conclusion

The object-based approach provided improved classification accuracy compared to pixel-based methods.

  • The multiresolution segmentation effectively delineated meaningful objects, making the classification more representative of real-world features.

  • Feature-based classification allowed for the differentiation of vegetation, water, and built-up areas, enhancing thematic mapping.

  • The use of multiple scale parameters enabled hierarchical classification, ensuring different levels of detail could be incorporated into the analysis.

  • The integration of air quality data demonstrated how multi-source information can refine classifications, making results more applicable to environmental studies.