Abstract
The monitoring and management of consistently changing landscape patterns are accomplished through a large amount of remote sensing data using satellite images and aerial photography that requires lossy compression for effective storage and transmission. Lossy compression brings the necessity to evaluate the image quality to preserve the important and detailed visual features of the data. We proposed and verified a weighted combination of qualitative parameters for the multi-criteria decision-making (MCDM) framework to evaluate the quality of the compressed aerial images. The aerial imagery of different contents and resolutions was tested using the transform-based lossy compression algorithms. We formulated an MCDM problem dedicated to the rating of lossy compression algorithms, governed by the set of qualitative parameters of the images and visually acceptable lossy compression ratios. We performed the lossy compression algorithms’ ranking with different compression ratios by their suitability for the aerial images using the neutrosophic weighted aggregated sum product assessment (WASPAS) method. The novelty of our methodology is the use of a weighted combination of different qualitative parameters for lossy compression estimation to get a more precise evaluation of the effect of lossy compression on the image content. Our methodology includes means of solving different subtasks, either by altering the weights or the set of aspects.
Highlights
The use of modern technologies increases the capabilities to explore the landscape using satellite images and aerial photography
We proposed the original multi-criteria decision-making methodology for the qualitative selection of the lossy compression for the aerial images based on their resolution and content
The transform-based lossy compression algorithms with the appropriate compression ratios were ranked by their suitability for aerial images using multi-criteria decision-making (MCDM) weighted aggregated sum product assessment (WASPAS)-SVNS and direct criteria weights evaluation methods
Summary
The use of modern technologies increases the capabilities to explore the landscape using satellite images and aerial photography. The monitoring of land cover changes and use, and emergency management [1] is accomplished through a large amount of remote sensing data. These changes are related to urban planning [2], deforestation, biodiversity loss [3,4,5] and other causes, like natural disasters [6]. This amount of data requires compression for effective management–storage, transmission, view, manipulation, processing, etc
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
