Geospatial image segmentation made easy
These are some publications and references to BIS.
Clinton, Nicholas, Ashley Holt, James Scarborough, Li Yan, and Peng Gong. "Accuracy assessment measures for object-based image segmentation goodness." Photogrammetric Engineering and remote sensing 76, no. 3 (2010): 289-299.
Abstract: To select an image segmentation from sets of segmentation results, measures for ranking the segmentations relative to a set of reference objects are needed. We review selected vector-based measures designed to compare the results of object-based image segmentation with sets of training objects extracted from the image of interest. We describe and compare area-based and location-based measures that measure the shape similarity between segments and training objects. By implementing the measures in two object-based image processing software packages, we illustrate their use in terms of automatically identifying parsimonious parameter combinations from arbitrarily large sets of segmentation results. The results show that the measures have divergent performance in terms of the identification of parameter combinations. Clustering of the results in measure space narrows the search. We illustrate combination schemes for the measures for generating rankings of segmentation results. The ranked segmentation results are illustrated and described.
Please cite this article when using BIS
ISPRS Geospatial Week 2015
Mas, J.-F. and González, R.: CHANGE DETECTION AND LAND USE / LAND COVER DATABASE UPDATING USING IMAGE SEGMENTATION, GIS ANALYSIS AND VISUAL INTERPRETATION, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-3/W3, 61-65, doi:10.5194/isprsarchives-XL-3-W3-61-2015, 2015.
Abstract: This article presents a hybrid method that combines image segmentation, GIS analysis, and visual interpretation in order to detect discrepancies between an existing land use/cover map and satellite images, and assess land use/cover changes. It was applied to the elaboration of a multidate land use/cover database of the State of Michoacán, Mexico using SPOT and Landsat imagery. The method was first applied to improve the resolution of an existing 1:250,000 land use/cover map produced through the visual interpretation of 2007 SPOT images. A segmentation of the 2007 SPOT images was carried out to create spectrally homogeneous objects with a minimum area of two hectares. Through an overlay operation with the outdated map, each segment receives the “majority” category from the map. Furthermore, spectral indices of the SPOT image were calculated for each band and each segment; therefore, each segment was characterized from the images (spectral indices) and the map (class label). In order to detect uncertain areas which present discrepancy between spectral response and class label, a multivariate trimming, which consists in truncating a distribution from its least likely values, was applied. The segments that behave like outliers were detected and labeled as “uncertain” and a probable alternative category was determined by means of a digital classification using a decision tree classification algorithm. Then, the segments were visually inspected in the SPOT image and high resolution imagery to assign a final category. The same procedure was applied to update the map to 2014 using Landsat imagery. As a final step, an accuracy assessment was carried out using verification sites selected from a stratified random sampling and visually interpreted using high resolution imagery and ground truth.
Keywords: Land cover database, Updating, Uncertainty, Image segmentation, Visual interpretation
Remote Sensing 2014
Gebhardt, Steffen, Thilo Wehrmann, Miguel Angel Muñoz Ruiz, Pedro Maeda, Jesse Bishop, Matthias Schramm, Rene Kopeinig et al. "MAD-MEX: Automatic Wall-to-Wall Land Cover Monitoring for the Mexican REDD-MRV Program Using All Landsat Data." Remote Sensing 6, no. 5 (2014): 3923-3943.
Abstract: Estimating forest area at a national scale within the United Nations program of Reducing Emissions from Deforestation and Forest Degradation (REDD) is primarily based on land cover information using remote sensing technologies. Timely delivery for a country of a size like Mexico can only be achieved in a standardized and cost-effective manner by automatic image classification. This paper describes the operational land cover monitoring system for Mexico. It utilizes national-scale cartographic reference data, all available Landsat satellite imagery, and field inventory data for validation. Seven annual national land cover maps between 1993 and 2008 were produced. The classification scheme defined 9 and 12 classes at two hierarchical levels. Overall accuracies achieved were up to 76%. Tropical and temperate forest was classified with accuracy up to 78% and 82%, respectively. Although specifically designed for the needs of Mexico, the general process is suitable for other participating countries in the REDD+ program to comply with guidelines on standardization and transparency of methods and to assure comparability. However, reporting of change is ill-advised based on the annual land cover products and a combination of annual land cover and change detection algorithms is suggested.
Keywords: REDD+; MRV; activity data; land cover; baseline; monitoring; Landsat; Mexico.
"We presented the MAD-MEX system supporting automatic wall-to-wall land cover classification using the full Landsat data archive. The system is developed against open source technology with the Berkeley Image Segmentation being the only commercial product. The system makes use of existing cluster infrastructure [...] that supports parallel processing. It is stable and fast as it can process a national land cover map for Mexico with an area of two million square kilometers in a few days."
Huang, Huabing, Peng Gong, Xiao Cheng, Nick Clinton, and Zengyuan Li. "Improving measurement of forest structural parameters by co-registering of high resolution aerial imagery and low density LiDAR data." Sensors 9, no. 3 (2009): 1541-1558.
Abstract: Forest structural parameters, such as tree height and crown width, are indispensable for evaluating forest biomass or forest volume. LiDAR is a revolutionary technology for measurement of forest structural parameters, however, the accuracy of crown width extraction is not satisfactory when using a low density LiDAR, especially in high canopy cover forest. We used high resolution aerial imagery with a low density LiDAR system to overcome this shortcoming. A morphological filtering was used to generate a DEM (Digital Elevation Model) and a CHM (Canopy Height Model) from LiDAR data. The LiDAR camera image is matched to the aerial image with an automated keypoints search algorithm. As a result, a high registration accuracy of 0.5 pixels was obtained. A local maximum filter, watershed segmentation, and object-oriented image segmentation are used to obtain tree height and crown width. Results indicate that the camera data collected by the integrated LiDAR system plays an important role in registration with aerial imagery. The synthesis with aerial imagery increases the accuracy of forest structural parameter extraction when compared to only using the low density LiDAR data.
Keywords: LiDAR; Aerial image; Forest structural parameters extraction.
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