OBIA

OBIA stands for Object-Based Image Analysis. It is a methodology used in remote sensing and geographic information systems for classifying and analyzing raster images. Unlike traditional pixel-based classification, which assigns a class to each individual pixel based on its spectral properties, OBIA groups pixels into meaningful objects based on their spectral characteristics, shape, texture, and spatial relationships.

The process of OBIA typically involves two main steps: segmentation and classification. Segmentation is the process of partitioning an image into a set of non-overlapping regions, or objects, that are homogeneous with respect to a defined set of criteria. These criteria can include spectral similarity, shape, size, and context. Once segmentation is complete, each object is then classified into a thematic class. This classification can be performed using various methods, including rule-based systems, machine learning algorithms, or nearest neighbor approaches, taking into account not only the spectral information of the object but also its characteristics derived from the segmentation, such as shape and texture.

OBIA has proven to be effective in various applications, including land cover mapping, urban planning, environmental monitoring, and precision agriculture. Its strength lies in its ability to incorporate contextual information and geometric properties of image features, leading to more accurate and meaningful classifications, especially in complex landscapes where spectral overlap between different features is common. By considering the relationships between neighboring pixels and the form of the features, OBIA often produces more visually coherent and ecologically relevant thematic maps than pixel-based methods.