Wavelet-based texture measures for object-based classification of aerial images

Toscani, Philipp; Immitzer, Markus; Atzberger, Clement

doi:10.1127/1432-8364/2013/0162

Cited by 15 publications

(12 citation statements)

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“…Interestingly, the two most important features were generated from the GR index. The importance and advantages of indices is well described by the literature [59,61,63,78], while the significance of texture matches the observations of Toscani et al [76] and Koger et al [112].…”

Section: Random Forestsupporting

confidence: 70%

“…When performing OBIA, texture can add valuable information, thus increasing the accuracy [76][77][78][79]. For the purpose of our study, such features were generated based on the coiflet wavelet transformation [80].…”

Section: Spectral and Textural Featuresmentioning

confidence: 99%

“…MDA is automatically generated within RF by running the model and systematically testing which features impact most (in a negative sense) the Out-Of-Bag (OOB) accuracy of the classification, if left out. The MDA values were then used for feature ranking and selection following approaches described in [76,78,89]. By performing the feature selection the model was optimized and the accuracy increased.…”

Section: =mentioning

confidence: 99%

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Assessing the Potential of Sentinel-2 and Pléiades Data for the Detection of Prosopis and Vachellia spp. in Kenya

Rima

Einzmann

et al. 2017

Remote Sensing

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Abstract:Prosopis was introduced to Baringo, Kenya in the early 1980s for provision of fuelwood and for controlling desertification through the Fuelwood Afforestation Extension Project (FAEP). Since then, Prosopis has hybridized and spread throughout the region. Prosopis has negative ecological impacts on biodiversity and socio-economic effects on livelihoods. Vachellia tortilis, on the other hand, is the dominant indigenous tree species in Baringo and is an important natural resource, mostly preferred for wood, fodder and charcoal production. High utilization due to anthropogenic pressure is affecting the Vachellia populations, whereas the well adapted Prosopis-competing for nutrients and water-has the potential to replace the native Vachellia vegetation. It is vital that both species are mapped in detail to inform stakeholders and for designing management strategies for controlling the Prosopis invasion. For the Baringo area, few remote sensing studies have been carried out. We propose a detailed and robust object-based Random Forest (RF) classification on high spatial resolution Sentinel-2 (ten meter) and Pléiades (two meter) data to detect Prosopis and Vachellia spp. for Marigat sub-county, Baringo, Kenya. In situ reference data were collected to train a RF classifier. Classification results were validated by comparing the outputs to independent reference data of test sites from the "Woody Weeds" project and the Out-Of-Bag (OOB) confusion matrix generated in RF. Our results indicate that both datasets are suitable for object-based Prosopis and Vachellia classification. Higher accuracies were obtained by using the higher spatial resolution Pléiades data (OOB accuracy 0.83 and independent reference accuracy 0.87-0.91) compared to the Sentinel-2 data (OOB accuracy 0.79 and independent reference accuracy 0.80-0.96). We conclude that it is possible to separate Prosopis and Vachellia with good accuracy using the Random Forest classifier. Given the cost of Pléiades, the free of charge Sentinel-2 data provide a viable alternative as the increased spectral resolution compensates for the lack of spatial resolution. With global revisit times of five days from next year onwards, Sentinel-2 based classifications can probably be further improved by using temporal information in addition to the spectral signatures.

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Section: Random Forestsupporting

confidence: 70%

Section: Spectral and Textural Featuresmentioning

confidence: 99%

Section: =mentioning

confidence: 99%

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Assessing the Potential of Sentinel-2 and Pléiades Data for the Detection of Prosopis and Vachellia spp. in Kenya

Rima

Einzmann

et al. 2017

Remote Sensing

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“…One advantage of the bootstrapping is that it yields relatively unbiased 'out-of-bag' (OOB) results, as long as representative reference data are provided [42]. Another benefit is the computation of importance measures which can be used for the evaluation of the input data and subsequent feature reduction.…”

Section: Random Forest Classification Approachmentioning

confidence: 99%

Optimal Input Features for Tree Species Classification in Central Europe Based on Multi-Temporal Sentinel-2 Data

et al. 2019

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Detailed knowledge about tree species composition is of great importance for forest management. The two identical European Space Agency (ESA) Sentinel-2 (S2) satellites provide data with unprecedented spectral, spatial and temporal resolution. Here, we investigated the potential benefits of using high temporal resolution data for classification of five coniferous and seven broadleaved tree species in a diverse Central European Forest. To run the classification, 18 cloud-free S2 acquisitions were analyzed in a two-step approach. The available scenes were first used to stratify the study area into six broad land-cover classes. Subsequently, additional classification models were created separately for the coniferous and the broadleaved forest strata. To permit a deeper analytical insight in the benefits of multi-temporal datasets for species identification, classification models were developed taking into account all 262,143 possible permutations of the 18 S2 scenes. Each model was fine-tuned using a stepwise recursive feature reduction. The additional use of vegetation indices improved the model performances by around 5 percentage points. Individual mono-temporal tree species accuracies range from 48.1% (January 2017) to 78.6% (June 2017). Compared to the best mono-temporal results, the multi-temporal analysis approach improves the out-of-bag overall accuracy from 72.9% to 85.7% for the broadleaved and from 83.8% to 95.3% for the coniferous tree species, respectively. Remarkably, a combination of six–seven scenes achieves a model quality equally high as the model based on all data; images from April until August proved most important. The classes European Beech and European Larch attain the highest user’s accuracies of 96.3% and 95.9%, respectively. The most important spectral variables to distinguish between tree species are located in the Red (coniferous) and short wave infrared (SWIR) bands (broadleaved), respectively. Overall, the study highlights the high potential of multi-temporal S2 data for species-level classifications in Central European forests.

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“…Based on the authors' own and published experience, we exploited a widely used ensemble classifi er called "random forest" (RF) which often yields good and robust classifi cation results (Gislason et al 2006 ;RodriguezGaliano et al 2012 ;Toscani et al 2013 ). RF uses bootstrap aggregation to create different training subsets, to produce a diversity of classifi cation trees, each providing a unique classifi cation result.…”

Section: Land-use and Land-cover Classifi Cation Using Object-based Amentioning

confidence: 99%

Targeting Landscapes to Identify Mitigation Options in Smallholder Agriculture

Rufino

Atzberger

Baldi

et al. 2016

Methods for Measuring Greenhouse Gas Balances and Evaluating Mitigation Options in Smallholder Agriculture

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This chapter presents a method for targeting landscapes with the objective of assessing mitigation options for smallholder agriculture. It presents alternatives in terms of the degree of detail and complexity of the analysis, to match the requirement of research and development initiatives. We address heterogeneity in land-use decisions that is linked to the agroecological characteristics of the landscape and to the social and economic profi les of the land users. We believe that as projects implement this approach, and more data become available, the method will be refi ned to reduce costs and increase the effi ciency and effectiveness of mitigation in smallholder agriculture. The approach is based on the assumption that landscape classifi cations refl ect differences in land productivity and greenhouse gas (GHG) emissions, and can be used to scale up point or fi eld-level measurements. At local level, the diversity of soils and land management can be meaningfully summarized using a suitable typology. Field types refl ecting small-scale fertility gradients are correlated to land

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Wavelet-based texture measures for object-based classification of aerial images

Cited by 15 publications

References 0 publications

Assessing the Potential of Sentinel-2 and Pléiades Data for the Detection of Prosopis and Vachellia spp. in Kenya

Assessing the Potential of Sentinel-2 and Pléiades Data for the Detection of Prosopis and Vachellia spp. in Kenya

Optimal Input Features for Tree Species Classification in Central Europe Based on Multi-Temporal Sentinel-2 Data

Targeting Landscapes to Identify Mitigation Options in Smallholder Agriculture

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