scholarly journals A Cluster Graph Approach to Land Cover Classification Boosting

Data ◽  
2019 ◽  
Vol 4 (1) ◽  
pp. 10
Author(s):  
Lloyd Hughes ◽  
Simon Streicher ◽  
Ekaterina Chuprikova ◽  
Johan Du Preez
Keyword(s):  
Land Cover ◽  
Graphical Model ◽  
Land Cover Classification ◽  
Probabilistic Graphical Model ◽  
Observation Data ◽  
Computationally Efficient ◽  
Efficient Manner ◽  
Uncertainty Estimates ◽  
Spatio Temporal ◽  
Cluster Graph

When it comes to land cover classification, the process of deriving the land classes is complex due to possible errors in algorithms, spatio-temporal heterogeneity of the Earth observation data, variation in availability and quality of reference data, or a combination of these. This article proposes a probabilistic graphical model approach, in the form of a cluster graph, to boost geospatial classifications and produce a more accurate and robust classification and uncertainty product. Cluster graphs can be characterized as a means of reasoning about geospatial data such as land cover classifications by considering the effects of spatial distribution, and inter-class dependencies in a computationally efficient manner. To assess the capabilities of our proposed cluster graph boosting approach, we apply it to the field of land cover classification. We make use of existing land cover products (GlobeLand30, CORINE Land Cover) along with data from Volunteered Geographic Information (VGI), namely OpenStreetMap (OSM), to generate a boosted land cover classification and the respective uncertainty estimates. Our approach combines qualitative and quantitative components through the application of our probabilistic graphical model and subjective expert judgments. Evaluating our approach on a test region in Garmisch-Partenkirchen, Germany, our approach was able to boost the overall land cover classification accuracy by 1.4% when compared to an independent reference land cover dataset. Our approach was shown to be robust and was able to produce a diverse, feasible and spatially consistent land cover classification in areas of incomplete and conflicting evidence. On an independent validation scene, we demonstrated that our cluster graph boosting approach was generalizable even when initialized with poor prior assumptions.

scholarly journals Uncertainty Analysis of Object-Based Land-Cover Classification Using Sentinel-2 Time-Series Data

2020 ◽  
Vol 12 (22) ◽  
pp. 3798
Author(s):  
Lei Ma ◽  
Michael Schmitt ◽  
Xiaoxiang Zhu
Keyword(s):  
Time Series ◽  
Land Cover ◽  
Time Series Data ◽  
Land Cover Classification ◽  
Series Data ◽  
Spectral Bands ◽  
Object Based ◽  
Spatio Temporal ◽  
Optical Satellite Images ◽  
The Impact

Recently, time-series from optical satellite data have been frequently used in object-based land-cover classification. This poses a significant challenge to object-based image analysis (OBIA) owing to the presence of complex spatio-temporal information in the time-series data. This study evaluates object-based land-cover classification in the northern suburbs of Munich using time-series from optical Sentinel data. Using a random forest classifier as the backbone, experiments were designed to analyze the impact of the segmentation scale, features (including spectral and temporal features), categories, frequency, and acquisition timing of optical satellite images. Based on our analyses, the following findings are reported: (1) Optical Sentinel images acquired over four seasons can make a significant contribution to the classification of agricultural areas, even though this contribution varies between spectral bands for the same period. (2) The use of time-series data alleviates the issue of identifying the “optimal” segmentation scale. The finding of this study can provide a more comprehensive understanding of the effects of classification uncertainty on object-based dense multi-temporal image classification.

scholarly journals Integer Programming on the Junction Tree Polytope for Influence Diagrams

2020 ◽  
Vol 2 (3) ◽  
pp. 209-228
Author(s):  
Axel Parmentier ◽  
Victor Cohen ◽  
Vincent Leclère ◽  
Guillaume Obozinski ◽  
Joseph Salmon
Keyword(s):  
Graphical Model ◽  
Optimization Problems ◽  
Valid Inequalities ◽  
Random Variables ◽  
Mixed Integer ◽  
Influence Diagrams ◽  
Probabilistic Graphical Model ◽  
Computationally Efficient ◽  
Junction Tree ◽  
Acyclic Digraph

Influence diagrams (ID) and limited memory influence diagrams (LIMID) are flexible tools to represent discrete stochastic optimization problems, with the Markov decision process (MDP) and partially observable MDP as standard examples. More precisely, given random variables considered as vertices of an acyclic digraph, a probabilistic graphical model defines a joint distribution via the conditional distributions of vertices given their parents. In an ID, the random variables are represented by a probabilistic graphical model whose vertices are partitioned into three types: chance, decision, and utility vertices. The user chooses the distribution of the decision vertices conditionally to their parents in order to maximize the expected utility. Leveraging the notion of rooted junction tree, we present a mixed integer linear formulation for solving an ID, as well as valid inequalities, which lead to a computationally efficient algorithm. We also show that the linear relaxation yields an optimal integer solution for instances that can be solved by the “single policy update,” the default algorithm for addressing IDs.

scholarly journals Graphical-model framework for automated annotation of cell identities in dense cellular images

eLife ◽  
2021 ◽  
Vol 10 ◽  
Author(s):  
Shivesh Chaudhary ◽  
Sol Ah Lee ◽  
Yueyi Li ◽  
Dhaval S Patel ◽  
Hang Lu
Keyword(s):  
Graphical Model ◽  
Conditional Random Fields ◽  
Ground Truth ◽  
Computationally Efficient ◽  
Model Framework ◽  
Efficient Manner ◽  
Whole Brain ◽  
C Elegans ◽  
Automated Annotation ◽  
Brain Data

Although identifying cell names in dense image stacks is critical in analyzing functional whole-brain data enabling comparison across experiments, unbiased identification is very difficult, and relies heavily on researchers' experiences. Here we present a probabilistic-graphical-model framework, CRF_ID, based on Conditional Random Fields, for unbiased and automated cell identification. CRF_ID focuses on maximizing intrinsic similarity between shapes. Compared to existing methods, CRF_ID achieves higher accuracy on simulated and ground-truth experimental datasets, and better robustness against challenging noise conditions common in experimental data. CRF_ID can further boost accuracy by building atlases from annotated data in highly computationally efficient manner, and by easily adding new features (e.g. from new strains). We demonstrate cell annotation in C. elegans images across strains, animal orientations, and tasks including gene-expression localization, multi-cellular and whole-brain functional imaging experiments. Together, these successes demonstrate that unbiased cell annotation can facilitate biological discovery, and this approach may be valuable to annotation tasks for other systems.

A Review on Unsupervised Band Selection Techniques : Land Cover Classification for Hyperspectral Earth Observation Data

2021 ◽  
pp. 0-0
Author(s):  
Ram Narayan Patro ◽  
Subhashree Subudhi ◽  
Pradyut Kumar Biswal ◽  
Fabio Dell'Acqua
Keyword(s):  
Land Cover ◽  
Land Cover Classification ◽  
Earth Observation ◽  
Band Selection ◽  
Observation Data ◽  
Earth Observation Data

Semi-Supervised Land Cover Classification Using Pi-SAR2 Observation Data

2020 ◽  
Author(s):  
Yuya Arima ◽  
Shoichiro Kojima ◽  
Jyunpei Uemoto ◽  
Tomohiko Konno
Keyword(s):  
Land Cover ◽  
Land Cover Classification ◽  
Observation Data

scholarly journals Deep Learning for Land Use and Land Cover Classification Based on Hyperspectral and Multispectral Earth Observation Data: A Review

2020 ◽  
Vol 12 (15) ◽  
pp. 2495 ◽  
Author(s):  
Ava Vali ◽  
Sara Comai ◽  
Matteo Matteucci
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Deep Learning ◽  
Land Cover ◽  
Ground Truth ◽  
Land Cover Classification ◽  
Global Scale ◽  
Hyperspectral Images ◽  
Machine Learning Techniques ◽  
Observation Data

Lately, with deep learning outpacing the other machine learning techniques in classifying images, we have witnessed a growing interest of the remote sensing community in employing these techniques for the land use and land cover classification based on multispectral and hyperspectral images; the number of related publications almost doubling each year since 2015 is an attest to that. The advances in remote sensing technologies, hence the fast-growing volume of timely data available at the global scale, offer new opportunities for a variety of applications. Deep learning being significantly successful in dealing with Big Data, seems to be a great candidate for exploiting the potentials of such complex massive data. However, there are some challenges related to the ground-truth, resolution, and the nature of data that strongly impact the performance of classification. In this paper, we review the use of deep learning in land use and land cover classification based on multispectral and hyperspectral images and we introduce the available data sources and datasets used by literature studies; we provide the readers with a framework to interpret the-state-of-the-art of deep learning in this context and offer a platform to approach methodologies, data, and challenges of the field.

scholarly journals FASTENER Feature Selection for Inference from Earth Observation Data

Entropy ◽  
2020 ◽  
Vol 22 (11) ◽  
pp. 1198
Author(s):  
Filip Koprivec ◽  
Klemen Kenda ◽  
Beno Šircelj
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Land Cover ◽  
State Of The Art ◽  
Land Cover Classification ◽  
Earth Observation ◽  
Supervised Machine Learning ◽  
Observation Data ◽  
Multi Objective ◽  
Earth Observation Data

In this paper, a novel feature selection algorithm for inference from high-dimensional data (FASTENER) is presented. With its multi-objective approach, the algorithm tries to maximize the accuracy of a machine learning algorithm with as few features as possible. The algorithm exploits entropy-based measures, such as mutual information in the crossover phase of the iterative genetic approach. FASTENER converges to a (near) optimal subset of features faster than other multi-objective wrapper methods, such as POSS, DT-forward and FS-SDS, and achieves better classification accuracy than similarity and information theory-based methods currently utilized in earth observation scenarios. The approach was primarily evaluated using the earth observation data set for land-cover classification from ESA’s Sentinel-2 mission, the digital elevation model and the ground truth data of the Land Parcel Identification System from Slovenia. For land cover classification, the algorithm gives state-of-the-art results. Additionally, FASTENER was tested on open feature selection data sets and compared to the state-of-the-art methods. With fewer model evaluations, the algorithm yields comparable results to DT-forward and is superior to FS-SDS. FASTENER can be used in any supervised machine learning scenario.

Multisource Earth Observation Data for Land-Cover Classification Using Random Forest

2018 ◽  
Vol 15 (5) ◽  
pp. 789-793 ◽  
Author(s):  
Zhigang Xu ◽  
Jike Chen ◽  
Junshi Xia ◽  
Peijun Du ◽  
Hongrui Zheng ◽  
...  
Keyword(s):  
Random Forest ◽  
Land Cover ◽  
Land Cover Classification ◽  
Earth Observation ◽  
Observation Data ◽  
Earth Observation Data
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