Sequential pattern mining of land cover dynamics based on time-series remote sensing images

2016 ◽  
Vol 76 (21) ◽  
pp. 22919-22942 ◽  
Author(s):  
Huichan Liu ◽  
Guojin He ◽  
Weili Jiao ◽  
Guizhou Wang ◽  
Yan Peng ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Land Cover ◽  
Pattern Mining ◽  
Sequential Pattern Mining ◽  
Sequential Pattern ◽  
Remote Sensing Images ◽  
Land Cover Dynamics

scholarly journals Spatiotemporal Mining of Time-Series Remote Sensing Images Based on Sequential Pattern Mining

2015 ◽  
Vol II-4/W2 ◽  
pp. 111-118
Author(s):  
H. C. Liu ◽  
G. J. He ◽  
X. M. Zhang ◽  
W. Jiang ◽  
S. G. Ling
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Land Cover ◽  
Land Cover Change ◽  
Pattern Mining ◽  
Sequential Pattern Mining ◽  
Sequential Pattern ◽  
Landscape Patterns ◽  
Mining Method ◽  
Study Region

With the continuous development of satellite techniques, it is now possible to acquire a regular series of images concerning a given geographical zone with both high accuracy and low cost. Research on how best to effectively process huge volumes of observational data obtained on different dates for a specific geographical zone, and to exploit the valuable information regarding land cover contained in these images has received increasing interest from the remote sensing community. In contrast to traditional land cover change measures using pair-wise comparisons that emphasize the compositional or configurational changes between dates, this research focuses on the analysis of the temporal sequence of land cover dynamics, which refers to the succession of land cover types for a given area over more than two observational periods. Using a time series of classified Landsat images, ranging from 2006 to 2011, a sequential pattern mining method was extended to this spatiotemporal context to extract sets of connected pixels sharing similar temporal evolutions. The resultant sequential patterns could be selected (or not) based on the range of support values. These selected patterns were used to explore the spatial compositions and temporal evolutions of land cover change within the study region. Experimental results showed that continuous patterns that represent consistent land cover over time appeared as quite homogeneous zones, which agreed with our domain knowledge. Discontinuous patterns that represent land cover change trajectories were dominated by the transition from vegetation to bare land, especially during 2009–2010. This approach quantified land cover changes in terms of the percentage area affected and mapped the spatial distribution of these changes. Sequential pattern mining has been used for string mining or itemset mining in transactions analysis. The expected novel significance of this study is the generalization of the application of the sequential pattern mining method for capturing the spatial variability of landscape patterns, and their trajectories of change, to reveal information regarding process regularities with satellite imagery.

scholarly journals Sequential Pattern Mining Algorithm Based on Text Data: Taking the Fault Text Records as an Example

2018 ◽  
Vol 10 (11) ◽  
pp. 4330 ◽  
Author(s):  
Xinglong Yuan ◽  
Wenbing Chang ◽  
Shenghan Zhou ◽  
Yang Cheng
Keyword(s):  
Time Series ◽  
Pattern Mining ◽  
Sequential Pattern Mining ◽  
Sequential Pattern ◽  
Fault Classification ◽  
Sequential Patterns ◽  
Series Data ◽  
Similarity Measurement ◽  
Text Similarity ◽  
Text Data

Sequential pattern mining (SPM) is an effective and important method for analyzing time series. This paper proposed a SPM algorithm to mine fault sequential patterns in text data. Because the structure of text data is poor and there are many different forms of text expression for the same concept, the traditional SPM algorithm cannot be directly applied to text data. The proposed algorithm is designed to solve this problem. First, this study measured the similarity of fault text data and classified similar faults into one class. Next, this paper proposed a new text similarity measurement model based on the word embedding distance. Compared with the classic text similarity measurement method, this model can achieve good results in short text classification. Then, on the basis of fault classification, this paper proposed the SPM algorithm with an event window, which is a time soft constraint for obtaining a certain number of sequential patterns according to needs. Finally, this study used the fault text records of a certain aircraft as experimental data for mining fault sequential patterns. Experiment showed that this algorithm can effectively mine sequential patterns in text data. The proposed algorithm can be widely applied to text time series data in many fields such as industry, business, finance and so on.

scholarly journals Constraint based Sequential Pattern Mining in Time Series Databases - A Two Way Approach

2013 ◽  
Vol 4 ◽  
pp. 313-318 ◽  
Author(s):  
Vangipuram Radhakrishna ◽  
Chintakindi Srinivas ◽  
C.V. Guru Rao
Keyword(s):  
Time Series ◽  
Pattern Mining ◽  
Sequential Pattern Mining ◽  
Sequential Pattern

Mapping and quantifying land cover dynamics using dense remote sensing time series with the user-friendly pyNITA software

2021 ◽  
pp. 105179
Author(s):  
Michael Alonzo ◽  
Jamon Van Den Hoek ◽  
Paulo J. Murillo-Sandoval ◽  
Cara E. Steger ◽  
John Aloysius Zinda
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Land Cover ◽  
Land Cover Dynamics ◽  
User Friendly ◽  
Sensing Time

scholarly journals Semi-Supervised Convolutional Long Short-Term Memory Neural Networks for Time Series Land Cover Classification

2021 ◽  
Vol 13 (17) ◽  
pp. 3504
Author(s):  
Jing Shen ◽  
Chao Tao ◽  
Ji Qi ◽  
Hao Wang
Keyword(s):  
Remote Sensing ◽  
Neural Networks ◽  
Time Series ◽  
Land Cover ◽  
Classification Accuracy ◽  
Short Term Memory ◽  
Remote Sensing Images ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory

Time series images with temporal features are beneficial to improve the classification accuracy. For abstract temporal and spatial contextual information, deep neural networks have become an effective method. However, there is usually a lack of sufficient samples in network training: one is the loss of images or the discontinuous distribution of time series data because of the inevitable cloud cover, and the other is the lack of known labeled data. In this paper, we proposed a Semi-supervised convolutional Long Short-Term Memory neural network (SemiLSTM) for time series remote sensing images, which was validated on three data sets with different time distributions. It achieves an accurate and automated land cover classification via a small number of labeled samples and a large number of unlabeled samples. Besides, it is a robust classification algorithm for time series optical images with cloud coverage, which reduces the requirements for cloudless remote sensing images and can be widely used in areas that are often obscured by clouds, such as subtropical areas. In conclusion, this method makes full advantage of spectral-spatial-temporal characteristics under the condition of limited training samples, especially expanding time context information to enhance classification accuracy.

scholarly journals Toward a Yearly Country-Scale CORINE Land-Cover Map without Using Images: A Map Translation Approach

2021 ◽  
Vol 13 (6) ◽  
pp. 1060
Author(s):  
Luc Baudoux ◽  
Jordi Inglada ◽  
Clément Mallet
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Time Frame ◽  
Superior Performance ◽  
Land Cover Mapping ◽  
Visual Interpretation ◽  
Remote Sensing Images ◽  
Corine Land Cover ◽  
Land Cover Map ◽  
By Products

CORINE Land-Cover (CLC) and its by-products are considered as a reference baseline for land-cover mapping over Europe and subsequent applications. CLC is currently tediously produced each six years from both the visual interpretation and the automatic analysis of a large amount of remote sensing images. Observing that various European countries regularly produce in parallel their own land-cover country-scaled maps with their own specifications, we propose to directly infer CORINE Land-Cover from an existing map, therefore steadily decreasing the updating time-frame. No additional remote sensing image is required. In this paper, we focus more specifically on translating a country-scale remote sensed map, OSO (France), into CORINE Land Cover, in a supervised way. OSO and CLC not only differ in nomenclature but also in spatial resolution. We jointly harmonize both dimensions using a contextual and asymmetrical Convolution Neural Network with positional encoding. We show for various use cases that our method achieves a superior performance than the traditional semantic-based translation approach, achieving an 81% accuracy over all of France, close to the targeted 85% accuracy of CLC.

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