Quantized ternary pattern and singular value decomposition for the efficient mining of sequences in SRSI images

• Published in: SN applied sciences Vol. 2; no. 10; p. 1697
• Main Authors: Preethi, R. Angelin, Anandharaj, G.
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01.10.2020; Springer Nature B.V
• Subjects: Agricultural land; Algorithms; Applied and Technical Physics; Chemistry/Food Science; Computer applications; Data mining; Datasets; Decomposition; Earth Sciences; Engineering; Engineering: Digital Image Processing; Environment; Materials Science; Pattern analysis; Pattern generation; Pixels; Remote sensing; Research Article; Set theory; Singular value decomposition; Spatial data; Urban development; Vegetation; Vegetation cover; Quantized ternary pattern; Serial remote sensing images; Geo-spatial image processing; Run length coding; Singular value decomposition
• ISSN: 2523-3963; 2523-3971; 2523-3971
• DOI: 10.1007/s42452-020-03474-8

The growth and development of particular region over time can be witnessed by remote sensing images. Although such raw images have less possibility to derive the insights, Serial Remote Sensing Images (SRSI) has the large potential to discover the patterns. The evolution of spatial patterns in various areas including urban development, expansion of vegetation cover and agriculture is the evidence for the utilization of SRSI accumulation. The application of conventional sequential pattern-mining algorithms on the SRSI images results in high computational complexity. This issue can be resolved by grouping the pixels and mining sequence patterns. A one-pass framework is introduced to compress and hide the data in the marked stream without any loss. In this paper, we proposed a Quantized ternary pattern based pixel grouping and Singular Value Decomposition—Run Length Coding based pattern mining. The algorithms are experimented using a dataset, namely, the Cropland data layer dataset. The proposed algorithm is efficient in terms of mining time and sequence pattern generation.