Land-cover evolution class analysis in Image Time Series of Landsat and Sentinel-2 based on Latent Dirichlet Allocation

Espinoza-Molina, Daniela; Bahmanyar, Reza; Datcu, Mihai; Dı́az-Delgado, Ricardo; Bustamante, Javier

doi:10.1109/multi-temp.2017.8035261

Cited by 7 publications

(6 citation statements)

References 8 publications

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“…If the aim is comparison of before-after outcomes, then it is typical to have a small number of remote sensing datasets corresponding to a small number of time periods. For these types of data, common approaches are to analyse the data for each time period using the methods described in Section 7; for example [12], take the difference in pixel or object values between the satellite imagery data for two periods of interest and analyse the differences using methods described in Section 7, and another example [41] is to include the time period as a covariate in the methods, as described in Section 7 [42].…”

Section: Statistical Machine Learning Methods For Time Series Datamentioning

confidence: 99%

Statistical Machine Learning Methods and Remote Sensing for Sustainable Development Goals: A Review

2018

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Interest in statistical analysis of remote sensing data to produce measurements of environment, agriculture, and sustainable development is established and continues to increase, and this is leading to a growing interaction between the earth science and statistical domains. With this in mind, we reviewed the literature on statistical machine learning methods commonly applied to remote sensing data. We focus particularly on applications related to the United Nations World Bank Sustainable Development Goals, including agriculture (food security), forests (life on land), and water (water quality). We provide a review of useful statistical machine learning methods, how they work in a remote sensing context, and examples of their application to these types of data in the literature. Rather than prescribing particular methods for specific applications, we provide guidance, examples, and case studies from the literature for the remote sensing practitioner and applied statistician. In the supplementary material, we also describe the necessary steps pre and post analysis for remote sensing data; the pre-processing and evaluation steps.

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Section: Statistical Machine Learning Methods For Time Series Datamentioning

confidence: 99%

Statistical Machine Learning Methods and Remote Sensing for Sustainable Development Goals: A Review

2018

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“…The pixel values in these micropatches were taken as the local descriptors. A k-means clustering was applied to these local descriptors to obtain a 50-word dictionary [8]. After the word assignment, we computed the histograms of words for each macropatch, thereby generating the bag-of-words representation of the dataset.…”

Section: Experimental Settingmentioning

confidence: 99%

“…LDA has been used on panchromatic Quickbird images to annotate large satellite images using semantic concepts, where examples of each semantic concept are given by the user [7]. LDA has also been used for measuring changes in multispectral image time series [8]. LDA was also applied to high-level scene understanding and content extraction [9] which aim to discover latent semantic classes containing pairs of objects characterized by a certain spatial positioning.…”

Section: Introductionmentioning

confidence: 99%

Feature-Free Explainable Data Mining in SAR Images Using Latent Dirichlet Allocation

Karmakar

Dumitru

Schwarz

et al. 2021

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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In this paper, we propose a promising approach for the application-oriented content classification of space-borne radar imagery that presents an interesting alternative to popular current machine learning algorithms. In the following, we consider the problem of unsupervised feature-free satellite image classification with already known classes as an explainable data mining problem for regions with no prior information. Three important issues are addressed here: explainability, feature independence, and unsupervision. There is an increasing demand towards explainable machine learning models as they strive to meet the "right to explanation". The importance of feature-free classification stems from the problem that different classification outcomes are obtained from using different features and the complexity of computing sophisticated image primitive features. Developing unsupervised discovery techniques helps overcome the limitations in object discovery due to the lack of labelled data and the dependence on features. In this paper, we demonstrate the applicability of a Latent Dirichlet Allocation (LDA) model, one of the most established unsupervised probabilistic methods, in discovering the latent structure of synthetic aperture radar (SAR) data. The idea is to use LDA as an explainable data mining tool to discover scientifically explainable semantic relations. The suitability of the approach as an explainable model is discussed and interpretable topic representation maps are produced which practically demonstrate the idea of "interpretability" in an explainable machine learning paradigm. LDA discovers the latent structures in the data as a set of topics. We create the interpretable visualizations of the data utilizing these topics and compute the topic distributions for each land-cover class. Our results show that each class has a distinct topic distribution that represents that particular class. Then these classes can be grouped based on their similarity of topic composition. Both the topic composition and grouping are explainable by domain experts.

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“…(From left to right, top): A quick-look view of visible false-color bands (B4, B3, and B2), false-colour visible/infrared bands (B8, B4, and B3), false-color infrared bands (B12, B11, and B8A), and all bands (B1, B2, B3, B4, B5, B6, B7, B8, B8A, B9, B10, B11, and B12). (From left to right, bottom): An example that shows four combination of bands and the information that can be extracted (Espinoza-Molina, Bahmanyar, Datcu, Díaz-Delgado, & Bustamante, 2017).…”

Section: Data Availability Statementmentioning

confidence: 99%

Understanding satellite images: a data mining module for Sentinel images

et al. 2020

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The increased number of free and open Sentinel satellite images has led to new applications of these data. Among them is the systematic classification of land cover/use types based on patterns of settlements or agriculture recorded by these images, in particular, the identification and quantification of their temporal changes. In this paper, we will present guidelines and practical examples of how to obtain rapid and reliable image patch labelling results and their validation based on data mining techniques for detecting these temporal changes, and presenting these as classification maps and/or statistical analytics. This represents a new systematic validation approach for semantic image content verification. We will focus on a number of different scenarios proposed by the user community using Sentinel data. From a large number of potential use cases, we selected three main cases, namely forest monitoring, flood monitoring, and macro-economics/urban monitoring.

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Land-cover evolution class analysis in Image Time Series of Landsat and Sentinel-2 based on Latent Dirichlet Allocation

Cited by 7 publications

References 8 publications

Statistical Machine Learning Methods and Remote Sensing for Sustainable Development Goals: A Review

Statistical Machine Learning Methods and Remote Sensing for Sustainable Development Goals: A Review

Feature-Free Explainable Data Mining in SAR Images Using Latent Dirichlet Allocation

Understanding satellite images: a data mining module for Sentinel images

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