Proceedings Article | 18 November 2014
Proc. SPIE. 9263, Multispectral, Hyperspectral, and Ultraspectral Remote Sensing Technology, Techniques and Applications V
KEYWORDS: Image processing algorithms and systems, Sensors, Satellites, Image segmentation, Remote sensing, Image analysis, Vegetation, Image classification, Spatial resolution, Accuracy assessment
This paper assesses the suitability of 8-band Worldview-2 (WV2) satellite data and object-based random forest algorithm
for the classification of avocado growth stages in Mexico. We tested both pixel-based with minimum distance (MD) and
maximum likelihood (MLC) and object-based with Random Forest (RF) algorithm for this task. Training samples and
verification data were selected by visual interpreting the WV2 images for seven thematic classes: fully grown, middle
stage, and early stage of avocado crops, bare land, two types of natural forests, and water body. To examine the
contribution of the four new spectral bands of WV2 sensor, all the tested classifications were carried out with and
without the four new spectral bands. Classification accuracy assessment results show that object-based classification
with RF algorithm obtained higher overall higher accuracy (93.06%) than pixel-based MD (69.37%) and MLC (64.03%)
method. For both pixel-based and object-based methods, the classifications with the four new spectral bands (overall
accuracy obtained higher accuracy than those without: overall accuracy of object-based RF classification with vs
without: 93.06% vs 83.59%, pixel-based MD: 69.37% vs 67.2%, pixel-based MLC: 64.03% vs 36.05%, suggesting that
the four new spectral bands in WV2 sensor contributed to the increase of the classification accuracy.
