국내 최대 기계 및 로봇 연구정보

기술보고서

기술보고서 게시판 내용
타이틀	Lossless Compression of Classification-Map Data
저자	Hua, Xie;; Klimesh, Matthew
Keyword	CLASSIFICATIONS;; IMAGE PROCESSING;; REMOTE SENSING;; MAPPING;; DATA ACQUISITION;; LOSSLESS MATERIALS;; DATA COMPRESSION;; ALGORITHMS;; DATA REDUCTION;; CODING;; MINERALS;; PIXELS;; GROUND STATIONS;; ENTROPY;;
URL	http://hdl.handle.net/2060/20090032129
보고서번호	NPO-45103
발행년도	2009
출처	NTRS (NASA Technical Report Server)
ABSTRACT	A lossless image-data-compression algorithm intended specifically for application to classification-map data is based on prediction, context modeling, and entropy coding. The algorithm was formulated, in consideration of the differences between classification maps and ordinary images of natural scenes, so as to be capable of compressing classification- map data more effectively than do general-purpose image-data-compression algorithms. Classification maps are typically generated from remote-sensing images acquired by instruments aboard aircraft (see figure) and spacecraft. A classification map is a synthetic image that summarizes information derived from one or more original remote-sensing image(s) of a scene. The value assigned to each pixel in such a map is the index of a class that represents some type of content deduced from the original image data for example, a type of vegetation, a mineral, or a body of water at the corresponding location in the scene. When classification maps are generated onboard the aircraft or spacecraft, it is desirable to compress the classification-map data in order to reduce the volume of data that must be transmitted to a ground station.