Quantized ternary pattern and singular value decomposition for the efficient mining of sequences in SRSI images
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Quantized ternary pattern and singular value decomposition for the efficient mining of sequences in SRSI images R. Angelin Preethi1 · G. Anandharaj2 Received: 1 February 2020 / Accepted: 31 August 2020 / Published online: 14 September 2020 © Springer Nature Switzerland AG 2020
Abstract 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. Keywords Serial remote sensing images · Quantized ternary pattern · Singular value decomposition · Run length coding · Geo-spatial image processing
1 Introduction The growth of satellite and space technology has led to the revolution in science and technology. The satellite images obtained from the space have several insights regarding climate change, town planning, change in urban development pattern, traffic flow pattern, migration of wild, environmental change, vegetation index fragmentation and so on [1]. The geospatial images contain spatial and temporal patterns, which require highly efficient mining techniques to derive insights. Among the satellite images, the Serial Remote Sensing Images (SRSI) has more potential to obtain effective results for earth observation. The SRSI images are captured from a particular area at different time periods that help to identify
the habitat changes [2, 3]. The events that occur at certain time intervals such as seasonal changes, deforestation of forests, growth of building in metropolitans, and change in vegetation patterns during different seasons can be easily explored using SRSI. The images are explored based on the mining of frequent patterns that has a higher count than the threshold value. The change of events can be identified using the pixels [4]. The SRSI has the sequential patterns of spatial data using which more knowledge can be mined. In general, a sequential pattern mining algorithm is used to explore the frequent items from conventional databases. In the case of geo spatial data, the data has complex characteristics such as geospatial features, large volume, which hinders the traditional sequential patter
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