Extraction and classification of objects in multispectral imagesPresented here is an algorithm that partitions a digitized multispectral image into parts that correspond to objects in the scene being sensed. The algorithm partitions an image into successively smaller rectangles and produces a partition that tends to minimize a criterion function. Supervised and unsupervised classification techniques can be applied to partitioned images. This partition-then-classify approach is used to process images sensed from aircraft and the ERTS-1 satellite, and the method is shown to give relatively accurate results in classifying agricultural areas and extracting urban areas.
Document ID
19740034818
Acquisition Source
Legacy CDMS
Document Type
Conference Proceedings
Authors
Robertson, T. V. (Purdue University West Lafayette, Ind., United States)
Date Acquired
August 7, 2013
Publication Date
January 1, 1973
Subject Category
Computers
Meeting Information
Meeting: Conference on Machine processing of remotely sensed data