Automatic detection and segmentation of orchards using very high resolution imagery

I. Zeki Yalniz

Abstract:

Spectral information alone is often not sufficient to
distinguish certain terrain classes such as permanent crops like
orchards, vineyards, and olive groves from other types of vegetation. However, instances of these classes possess distinctive
spatial structures that can be observable in detail in very high
spatial resolution images. This paper proposes a novel unsupervised algorithm for the detection and segmentation of orchards.
The detection step uses a texture model that is based on the
idea that textures are made up of primitives (trees) appearing
in a near-regular repetitive arrangement (planting patterns). The
algorithm starts with the enhancement of potential tree locations
by using multi-granularity isotropic filters. Then, the regularity of
the planting patterns is quantified using projection profiles of the
filter responses at multiple orientations. The result is a regularity
score at each pixel for each granularity and orientation. Finally,
the segmentation step iteratively merges neighboring pixels and
regions belonging to similar planting patterns according to the
similarities of their regularity scores and obtains the boundaries of
individual orchards along with estimates of their granularities and
orientations. Extensive experiments using Ikonos and QuickBird
imagery as well as images taken from Google Earth show that the
proposed algorithm provides good localization of the target objects
even when no sharp boundaries exist in the image data.