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Compression of Large-Scale Aerial Imagery: Exploring Set Redundancy Methods
University of Gävle, Faculty of Engineering and Sustainable Development, Department of Computer and Geospatial Sciences, Computer Science.
2023 (English)Independent thesis Basic level (professional degree), 10 credits / 15 HE creditsStudent thesis
Abstract [en]

Compression of data has been historically always important; more data is gettingproduced and therefore has to be stored. While hardware technology advances,compression should be a must to reduce storage occupied and to keep the data intransmission as small as possible.

Set redundancy has been developed in 1996 but has since then not received a lot ofattention in research. This paper tries to implement two set redundancy methods –the Max-Min-Predictive II and also the Intensity Mapping algorithm to see if thismethod could be used on large scale aerial imagery in the geodata field.

After using the set redundancy methods, different individual image compressionmethods were applied and compared to the standard JPEG2000 in lossless mode.These compression algorithms were Huffman, LZW, and JPEG2000 itself.

The data sets used were two images each taken from 2019, one pair with 60% overlap,the other with 80% overlap. Individual compression of images is still offering abetter compression ratio, but the set redundancy method produces results which areworth investigating further with more images in a set of similar images.

This points to future work of compressing a larger set with more overlap and moreimages, which for greater potential matching should be overlaid more carefully toensure matching pixel values.

Abstract [sv]

Datakomprimering har historiskt alltid varit viktigt; mer data än någonsin producerasoch behöver lagras. Trots teknologiska framsteg inom lagrings- och datateknologierär komprimering ett måste för att reducera mängden lagring som krävs och underlättavid överföringar genom att mindre filmängd måste skickas.

Set redundancy utvecklades 1996, men har sedan dess inte fått så mycket uppmärksamhetinom forskning. Det här pappret försöker implementera två olika set redundancy-metoder – Max-Min-Predictive II och Intensity Mapping algoritmen, för att se omdenna metod kan användas på flygbilder från storskalig flygbildsinsamling.

Efter användandet av set redundancy metoder på ett set av flygbilder, utnyttjadesandra bildkomprimeringsmetoder för enskilda bilder på resultatet, detta jämfördesmed den icke-förstörande JPEG2000 komprimeringen av originalbilderna. Komprimeringsalgoritmernasom användes på set redundancy-resultatet var Huffman, LZW,och JPEG2000.

Det dataset som användes bestod av två par av bilder från 2019, där en hade överlapppå 60% och det andra paret på 80%. Individuell komprimering av dataseten gaven högre komprimeringsgrad än set redundancy metoder, men set redundancy har enskalningspotential när fler bilder läggs till i ett set, vilket är värt att undersöka vidare.

Detta pekar på framtida arbeten där komprimering av större dataset med högreöverlapp mellan bilder, som med en högre geografisk korrekthet läses in ovanpåvarandra, kan testas.

Place, publisher, year, edition, pages
2023. , p. 47
Keywords [en]
Set redundancy, image compression, aerial imagery
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:hig:diva-42022OAI: oai:DiVA.org:hig-42022DiVA, id: diva2:1764454
External cooperation
Lantmäteriet
Subject / course
Computer science
Educational program
Högskoleingenjör
Supervisors
Examiners
Available from: 2023-06-12 Created: 2023-06-08 Last updated: 2023-06-12Bibliographically approved

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CiteExportLink to record
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Citation style
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