A Contextual Classification Approach for Remote Sensing Image Classification of Hyperspatial Imagery

Abstract:

One of the most important tasks of remote sensing is the use of imagery to classify and delineate different objects on the earth’s surface. Conventional methods of pixel-based classification classify each pixel independently by only considering its spectral properties. These pixel-based techniques are most applicable to medium and coarse-scale remote sensing, but often become less accurate at high spatial resolutions (pixels <= 1m) as the scene objects become larger than a pixel. Contextual classification techniques use not only the spectral properties of the pixel, but also the local spatial information to improve pixel labeling and classification. In this study, we use a focal pixel as predictor variables to use with a machine learning classifier. We applied this technique to a set of remotely sensed, multispectral hyperspatial imagery (Worldview-2) to map the type, distribution, and structure of vegetation in a Sierra Nevada forest.