Scale Matters: Spatially Partitioned Unsupervised Segmentation Parameter Optimization for Large and Heterogeneous Satellite Images

Georganos, Stefanos; Grippa, Taïs; Lennert, Moritz; Vanhuysse, Sabine; Johnson, Brian Alan; Wolff, Éléonore

doi:10.3390/rs10091440

Cited by 47 publications

(48 citation statements)

References 67 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…An important advantage of the proposed method is its potential for automation with particular merit for large, heterogeneous areas. As Grippa et al [47] and Georganos et al [48] pointed out, in heterogeneous urban scenes a single set of segmentation parameters is inadequate and less efficient than spatially partitioning the study area into several subsets and optimizing each separately. Similar studies for semi-rural and agricultural environments have demonstrated the efficacy of local optimization through the use of F-measure and ESP methods [25,27].…”

Section: Discussionmentioning

confidence: 99%

Normalization in Unsupervised Segmentation Parameter Optimization: A Solution Based on Local Regression Trend Analysis

et al. 2018

Self Cite

View full text Add to dashboard Cite

In object-based image analysis (OBIA), the appropriate parametrization of segmentation algorithms is crucial for obtaining satisfactory image classification results. One of the ways this can be done is by unsupervised segmentation parameter optimization (USPO). A popular USPO method does this through the optimization of a "global score" (GS), which minimizes intrasegment heterogeneity and maximizes intersegment heterogeneity. However, the calculated GS values are sensitive to the minimum and maximum ranges of the candidate segmentations. Previous research proposed the use of fixed minimum/maximum threshold values for the intrasegment/intersegment heterogeneity measures to deal with the sensitivity of user-defined ranges, but the performance of this approach has not been investigated in detail. In the context of a remote sensing very-high-resolution urban application, we show the limitations of the fixed threshold approach, both in a theoretical and applied manner, and instead propose a novel solution to identify the range of candidate segmentations using local regression trend analysis. We found that the proposed approach showed significant improvements over the use of fixed minimum/maximum values, is less subjective than user-defined threshold values and, thus, can be of merit for a fully automated procedure and big data applications.

show abstract

Section: Discussionmentioning

confidence: 99%

Normalization in Unsupervised Segmentation Parameter Optimization: A Solution Based on Local Regression Trend Analysis

et al. 2018

Self Cite

View full text Add to dashboard Cite

show abstract

“…Moreover, to properly assess the performance of the tested machine learning algorithms (CNN and RF), the significance of the overall accuracy between classifications was calculated using the McNemar statistical test [65,66]. Several studies have reported the use of the McNemar test to compare between two classification approaches [54,67,68]. In this test, a null hypothesis that there is no significant difference between OA values of the two compared classifications is proposed.…”

Section: Inter-comparison and Quality Assessmentmentioning

confidence: 99%

Mapping Irrigated Areas Using Sentinel-1 Time Series in Catalonia, Spain

et al. 2019

View full text Add to dashboard Cite

Mapping irrigated plots is essential for better water resource management. Today, the free and open access Sentinel-1 (S1) and Sentinel-2 (S2) data with high revisit time offers a powerful tool for irrigation mapping at plot scale. Up to date, few studies have used S1 and S2 data to provide approaches for mapping irrigated plots. This study proposes a method to map irrigated plots using S1 SAR (synthetic aperture radar) time series. First, a dense temporal series of S1 backscattering coefficients were obtained at plot scale in VV (Vertical-Vertical) and VH (Vertical-Horizontal) polarizations over a study site located in Catalonia, Spain. In order to remove the ambiguity between rainfall and irrigation events, the S1 signal obtained at plot scale was used conjointly to S1 signal obtained at a grid scale (10 km × 10 km). Later, two mathematical transformations, including the principal component analysis (PCA) and the wavelet transformation (WT), were applied to the several SAR temporal series obtained in both VV and VH polarization. Irrigated areas were then classified using the principal component (PC) dimensions and the WT coefficients in two different random forest (RF) classifiers. Another classification approach using one dimensional convolutional neural network (CNN) was also performed on the obtained S1 temporal series. The results derived from the RF classifiers with S1 data show high overall accuracy using the PC values (90.7%) and the WT coefficients (89.1%). By applying the CNN approach on SAR data, a significant overall accuracy of 94.1% was obtained. The potential of optical images to map irrigated areas by the mean of a normalized differential vegetation index (NDVI) temporal series was also tested in this study in both the RF and the CNN approaches. The overall accuracy obtained using the NDVI in RF classifier reached 89.5% while that in the CNN reached 91.6%. The combined use of optical and radar data slightly enhanced the classification in the RF classifier but did not significantly change the accuracy obtained in the CNN approach using S1 data.

show abstract

“…However, even in state-of-the-art methods, the segmentation parameters are generally optimized for whole scenes, which is not effective for citywide mapping that involves large images with a high degree of heterogeneity. Therefore, a local segmentation optimization was developed (spatially partitioned unsupervised segmentation parameter optimization [101]) that outperforms global approaches, both in terms of thematic and geometric accuracy [102].…”

Section: The Potential Of Obia For Generating Land Cover Information mentioning

confidence: 99%

The Role of Earth Observation in an Integrated Deprived Area Mapping “System” for Low-to-Middle Income Countries

et al. 2020

Self Cite

View full text Add to dashboard Cite

Urbanization in the global South has been accompanied by the proliferation of vast informal and marginalized urban areas that lack access to essential services and infrastructure. UN-Habitat estimates that close to a billion people currently live in these deprived and informal urban settlements, generally grouped under the term of urban slums. Two major knowledge gaps undermine the efforts to monitor progress towards the corresponding sustainable development goal (i.e., SDG 11—Sustainable Cities and Communities). First, the data available for cities worldwide is patchy and insufficient to differentiate between the diversity of urban areas with respect to their access to essential services and their specific infrastructure needs. Second, existing approaches used to map deprived areas (i.e., aggregated household data, Earth observation (EO), and community-driven data collection) are mostly siloed, and, individually, they often lack transferability and scalability and fail to include the opinions of different interest groups. In particular, EO-based-deprived area mapping approaches are mostly top-down, with very little attention given to ground information and interaction with urban communities and stakeholders. Existing top-down methods should be complemented with bottom-up approaches to produce routinely updated, accurate, and timely deprived area maps. In this review, we first assess the strengths and limitations of existing deprived area mapping methods. We then propose an Integrated Deprived Area Mapping System (IDeAMapS) framework that leverages the strengths of EO- and community-based approaches. The proposed framework offers a way forward to map deprived areas globally, routinely, and with maximum accuracy to support SDG 11 monitoring and the needs of different interest groups.

show abstract

Scale Matters: Spatially Partitioned Unsupervised Segmentation Parameter Optimization for Large and Heterogeneous Satellite Images

Cited by 47 publications

References 67 publications

Normalization in Unsupervised Segmentation Parameter Optimization: A Solution Based on Local Regression Trend Analysis

Normalization in Unsupervised Segmentation Parameter Optimization: A Solution Based on Local Regression Trend Analysis

Mapping Irrigated Areas Using Sentinel-1 Time Series in Catalonia, Spain

The Role of Earth Observation in an Integrated Deprived Area Mapping “System” for Low-to-Middle Income Countries

Contact Info

Product

Resources

About