Land cover mapping via image classification is sometimes realized through object-based
image analysis. Objects are typically constructed by partitioning imagery into spatially …

Unsupervised segmentation optimization methods have been proposed to aid in selecting
an “optimal” set of scale parameters quickly and objectively for object-based image analysis …

Segmentation algorithms applied to remote sensing data provide valuable information about
the size, distribution and context of landscape objects at a range of scales. However, there is …

In object-based image analysis, how to produce accurate segmentation is usually a very
important issue that needs to be solved before image classification or target recognition. The …

Segmentation algorithms have already been recognized as a valuable and complementary
approach that similar to human operators perform a region-based rather than a point-based …

The use of an object-based image analysis (OBIA) method has recently become quite
common for classifying high-resolution remote-sensed images. However, despite OBIA's …

The advances in remote sensing sensors during the last two decades have led to the
production of very high spatial resolution multispectral images. In order to adapt to this rapid …

Image segmentation is a critical and important step in (GEographic) Object-Based Image
Analysis (GEOBIA or OBIA). The final feature extraction and classification in OBIA is highly …

Image segmentation is a key technique involved in information extraction from high spatial
resolution remote sensing images. Studying the impact of the evaluation method on the …

In this study, a multi-scale approach is used to improve the segmentation of a high spatial
resolution (30 cm) color infrared image of a residential area. First, a series of 25 image …