| 1. |
- Börlin, Niclas
(författare)
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Comparison of resection : intersection algorithms and projection geometries in radiostereometry
- 2002
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Ingår i: ISPRS journal of photogrammetry and remote sensing (Print). - : Elsevier. - 0924-2716 .- 1872-8235. ; 56:5-6, s. 390-400
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Tidskriftsartikel (refereegranskat)abstract
- Three resection-intersection algorithms were applied to simulated projections and clinical data from radiostereometric patients. On simulated data, the more advanced bundle-adjustment-based algorithms outperformed the classical Selvik algorithm, even if the error reductions were small for some parameters. On clinical data, the results were inconclusive. The two different projection geometries had a much larger influence on the error size and distribution. For the biplanar configuration, the position and motion errors were small and almost isotropic. For the uniplanar configuration, the position errors were comparably high and anisotropic, but still resulted in a high accuracy for some motion parameters at the expense of others. The simplified resection-intersection algorithm by Selvik may still be considered a good and robust algorithm for radiostereometry. More studies will have to be performed to find out how the theoretical advantages of the bundle methods can be utilized in clinical radiostereometry. (C) 2002 Elsevier Science B.V. All rights reserved.
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| 2. |
- Anwer, Rao Muhammad, et al.
(författare)
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Binary patterns encoded convolutional neural networks for texture recognition and remote sensing scene classification
- 2018
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Ingår i: ISPRS journal of photogrammetry and remote sensing (Print). - : ELSEVIER SCIENCE BV. - 0924-2716 .- 1872-8235. ; 138, s. 74-85
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Tidskriftsartikel (refereegranskat)abstract
- Designing discriminative powerful texture features robust to realistic imaging conditions is a challenging computer vision problem with many applications, including material recognition and analysis of satellite or aerial imagery. In the past, most texture description approaches were based on dense orderless statistical distribution of local features. However, most recent approaches to texture recognition and remote sensing scene classification are based on Convolutional Neural Networks (CNNs). The de facto practice when learning these CNN models is to use RGB patches as input with training performed on large amounts of labeled data (ImageNet). In this paper, we show that Local Binary Patterns (LBP) encoded CNN models, codenamed TEX-Nets, trained using mapped coded images with explicit LBP based texture information provide complementary information to the standard RGB deep models. Additionally, two deep architectures, namely early and late fusion, are investigated to combine the texture and color information. To the best of our knowledge, we are the first to investigate Binary Patterns encoded CNNs and different deep network fusion architectures for texture recognition and remote sensing scene classification. We perform comprehensive experiments on four texture recognition datasets and four remote sensing scene classification benchmarks: UC-Merced with 21 scene categories, WHU-RS19 with 19 scene classes, RSSCN7 with 7 categories and the recently introduced large scale aerial image dataset (AID) with 30 aerial scene types. We demonstrate that TEX-Nets provide complementary information to standard RGB deep model of the same network architecture. Our late fusion TEX-Net architecture always improves the overall performance compared to the standard RGB network on both recognition problems. Furthermore, our final combination leads to consistent improvement over the state-of-the-art for remote sensing scene classification. (C) 2018 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS). Published by Elsevier B.V. All rights reserved.
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| 3. |
- Ban, Yifang, et al.
(författare)
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Spaceborne SAR Data for Global Urban Mapping at 30m Resolution Utilizing a Robust Urban Extractor
- 2015
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Ingår i: ISPRS journal of photogrammetry and remote sensing (Print). - : Elsevier BV. - 0924-2716 .- 1872-8235. ; 103
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Tidskriftsartikel (refereegranskat)abstract
- With more than half of the world population now living in cities and 1.4 billion more people expected to move into cities by 2030, urban areas pose significant challenges on local, regional and global environment. Timely and accurate information on spatial distributions and temporal changes of urban areas are therefore needed to support sustainable development and environmental change research. The objective of this research is to evaluate spaceborne SAR data for improved global urban mapping using a robust processing chain, the KTH-Pavia Urban Extractor. The proposed processing chain includes urban extraction based on spatial indices and Grey Level Co-occurrence Matrix (GLCM) textures, an existing method and several improvements i.e., SAR data preprocessing, enhancement, and post-processing. ENVISAT Advanced Synthetic Aperture Radar (ASAR) C-VV data at 30m resolution were selected over 10 global cities and a rural area from six continents to demonstrated robustness of the improved method. The results show that the KTH-Pavia Urban Extractor is effective in extracting urban areas and small towns from ENVISAT ASAR data and built-up areas can be mapped at 30m resolution with very good accuracy using only one or two SAR images. These findings indicate that operational global urban mapping is possible with spaceborne SAR data, especially with the launch of Sentinel-1 that provides SAR data with global coverage, operational reliability and quick data delivery.
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| 4. |
- Brandtberg, Tomas
(författare)
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Classifying individual tree species under leaf-off and leaf-on conditions using airborne lidar
- 2007
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Ingår i: ISPRS journal of photogrammetry and remote sensing (Print). - : Elsevier BV. - 0924-2716 .- 1872-8235. ; 61:5, s. 325-340
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Tidskriftsartikel (refereegranskat)abstract
- In this paper, a methodology for individual tree-based species classification using high sampling density and small footprint lidar data is clarified, corrected and improved. For this purpose, a well-defined directed graph (digraph) is introduced and it plays a fundamental role in the approach. It is argued that there exists one and only one such unique digraph that describes all four pure events and resulting disjoint sets of laser points associated with a single tree in data from a two-return lidar system. However, the digraph is extendable so that it fits an n-return lidar system (n>2) with higher logical resolution. Furthermore, a mathematical notation for different types of groupings of the laser points is defined, and a new terminology for various types of individual tree-based concepts defined by the digraph is proposed. A novel calibration technique for estimating individual tree heights is evaluated. The approach replaces the unreliable maximum single laser point height of each tree with a more reliable prediction based on shape characteristics of a marginal height distribution of the whole first-return point cloud of each tree. The result shows a reduction of the RMSE of the tree heights of about 20% (stddev=1.1 m reduced to stddev=0.92 m). The method improves the species classification accuracy markedly, but it could also be used for reducing the sampling density at the time of data acquisition. Using the calibrated tree heights, a scale-invariant rescaled space for the universal set of points for each tree is defined, in which all individual tree-based geometric measurements are conducted. With the corrected and improved classification methodology the total accuracy raises from 60% to 64% for classifying three leaf-off individual tree deciduous species (N=200 each) in West Virginia, USA: oaks (Quercus spp.), red maple (Acer ruhrum), and yellow poplar (Liriodendron tuliperifera).
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| 5. |
- Dao, P. D., et al.
(författare)
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Improving hyperspectral image segmentation by applying inverse noise weighting and outlier removal for optimal scale selection
- 2021
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Ingår i: ISPRS journal of photogrammetry and remote sensing (Print). - : Elsevier B.V.. - 0924-2716 .- 1872-8235. ; 171, s. 348-366
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Tidskriftsartikel (refereegranskat)abstract
- Optimal scale selection for image segmentation is an essential component of the Object-Based Image Analysis (OBIA) and interpretation. An optimal segmentation scale is a scale at which image objects, overall, best represent real-world ground objects and features across the entire image. At this scale, the intra-object variance is ideally lowest and the inter-object spatial autocorrelation is ideally highest, and a change in the scale could cause an abrupt change in these measures. Unsupervised parameter optimization methods typically use global measures of spatial and spectral properties calculated from all image objects in all bands as the target criteria to determine the optimal segmentation scale. However, no studies consider the effect of noise in image spectral bands on the segmentation assessment and scale selection. Furthermore, these global measures could be affected by outliers or extreme values from a small number of objects. These issues may lead to incorrect assessment and selection of optimal scales and cause the uncertainties in subsequent segmentation and classification results. These issues become more pronounced when segmenting hyperspectral data with large spectral variability across the spectrum. In this study, we propose an enhanced method that 1) incorporates the band's inverse noise weighting in the segmentation and 2) detects and removes outliers before determining segmentation scale parameters. The proposed method is evaluated on three well-established segmentation approaches – k-means, mean-shift, and watershed. The generated segments are validated by comparing them with reference polygons using normalized over-segmentation (OS), under-segmentation (US), and the Euclidean Distance (ED) indices. The results demonstrate that this proposed scale selection method produces more accurate and reliable segmentation results. The approach can be applied to other segmentation selection criteria and are useful for automatic multi-parameter tuning and optimal scale parameter selections in OBIA methods in remote sensing. © 2020 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS)
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| 6. |
- de Paula Pires, Raul, et al.
(författare)
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Individual tree detection and estimation of stem attributes with mobile laser scanning along boreal forest roads
- 2022
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Ingår i: ISPRS Journal of Photogrammetry and Remote Sensing. - : Elsevier BV. - 0924-2716. ; 187, s. 211-224
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Tidskriftsartikel (refereegranskat)abstract
- The collection of field-reference data is a key task in remote sensing-based forest inventories. However, traditional methods of collection demand extensive personnel resources. Thus, field-reference data collection would benefit from more automated methods. In this study, we proposed a method for individual tree detection (ITD) and stem attribute estimation based on a car-mounted mobile laser scanner (MLS) operating along forest roads. We assessed its performance in six ranges with increasing mean distance from the roadside. We used a Riegl VUX1LR sensor operating with high repetition rate, thus providing detailed cross sections of the stems. The algorithm we propose was designed for this sensor configuration, identifying the cross sections (or arcs) in the point cloud and aggregating those into single trees. Furthermore, we estimated diameter at breast height (DBH), stem profiles, and stem volume for each detected tree. The accuracy of ITD, DBH, and stem volume estimates varied with the trees' distance from the road. In general, the proximity to the sensor of branches 0-10 m from the road caused commission errors in ITD and over estimation of stem attributes in this zone. At 50-60 m from roadside, stems were often occluded by branches, causing omissions and underestimation of stem attributes in this area. ITD's precision and sensitivity varied from 82.8% to 100% and 62.7% to 96.7%, respectively. The RMSE of DBH estimates ranged from 1.81 cm (6.38%) to 4.84 cm (16.9%). Stem volume estimates had RMSEs ranging from 0.0800 m(3) (10.1%) to 0.190 m(3) (25.7%), depending on the distance to the sensor. The average proportion of detected reference volume was highly affected by the performance of ITD in the different zones. This proportion was highest from 0 to 10 m (113%), a zone that concentrated most ITD commission errors, and lowest from 50 to 60 m (66.6%), mostly due to the omission errors in this area. In the other zones, the RMSE ranged from 87.5% to 98.5%. These accuracies are in line with those obtained by other state-of-the-art MLS and terrestrial laser scanner (TLS) methods. The car-mounted MLS system used has the potential to collect data efficiently in large-scale inventories, being able to scan approximately 80 ha of forests per day depending on the survey setup. This data collection method could be used to increase the amount of field-reference data available in remote sensing based forest inventories, improve models for area-based estimations, and support precision forestry development.
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| 7. |
- Fan, Hongchao, et al.
(författare)
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An automatic approach for the typification of façade structures
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Ingår i: ISPRS journal of photogrammetry and remote sensing (Print). - 0924-2716 .- 1872-8235.
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Tidskriftsartikel (övrigt vetenskapligt/konstnärligt)abstract
- Typification is a well-established operator of map generalization. Although it is widely used in many existing research fields, less discussion has been devoted to the quality of typification. This paper presents a user survey for the evaluation of different typification results of façade structures under different constraints. The survey shows that preservation of the shape of the features is the most important constraint for a reasonable typification process, which has also been quantitatively verified by calculating the similarities between the typified façades and the original façade using attributed relational graph (ARG) and nested earth mover’s distance (NEMD) algorithms. Based on that, an algorithm is developed to generate perceivably reasonable representation from the original facade with decreasing map scale. The algorithm is implemented and tested on a number of façades. Experiments reveal that the typification can be automatically conducted and can create results which are well associated with the original façades.
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| 8. |
- Forsman, Mona, et al.
(författare)
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Bias of cylinder diameter estimation from ground-based laser scanners with different beam widths : a simulation study
- 2018
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Ingår i: ISPRS journal of photogrammetry and remote sensing (Print). - Amsterdam : Elsevier. - 0924-2716 .- 1872-8235. ; 135, s. 84-92
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Tidskriftsartikel (refereegranskat)abstract
- In this study we have investigated why diameters of tree stems, which are approximately cylindrical, are often overestimated by mobile laser scanning. This paper analyzes the physical processes when using ground-based laser scanning that may contribute to a bias when estimating cylinder diameters using circle-fit methods. A laser scanner simulator was implemented and used to evaluate various properties, such as distance, cylinder diameter, and beam width of a laser scanner-cylinder system to find critical conditions. The simulation results suggest that a positive bias of the diameter estimation is expected. Furthermore, the bias follows a quadratic function of one parameter - the relative footprint, i.e., the fraction of the cylinder width illuminated by the laser beam. The quadratic signature opens up a possibility to construct a compensation model for the bias.
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| 9. |
- Gomez-Gallego, Mireia
(författare)
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Using hyperspectral plant traits linked to photosynthetic efficiency to assess N and P partition
- 2020
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Ingår i: ISPRS Journal of Photogrammetry and Remote Sensing. - : Elsevier BV. - 0924-2716. ; 169, s. 406-420
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Tidskriftsartikel (refereegranskat)abstract
- Spatial prediction of photosynthesis requires an understanding of how foliage nitrogen (N) and phosphorus (P) regulate this process and the relationship between these elements and scalable spectral proxies. Hyperspectral imagery has been used to predict important photosynthetic variables such as the maximum rate of carboxylation (V-cmax) and electron transport (J(max)). However, our understanding of how generally applicable these relationships are for plants that are limited by N and P, characterised by respective mass based ratios of N/P <= 10 and N/P > 10, is still incomplete as most studies assume N and P co-limit photosynthesis.Hyperspectral imagery and measurements of photosynthesis were obtained from one-year old Pinus radiata D. Don, grown under a factorial combination of N and P treatments. Using these data, the objectives of this study were to (i) identify whether trees were co-limited or independently limited by N and P, and then use hyperspectral imagery to (ii) partition N and P limited trees, (iii) build models of N and P from a range of hyperspectral indices and (iv) explore links between key plant traits and both V-cmax and J(max).Compared to the use of all data, which assumes co-limitation, markedly stronger relationships between N and P and photosynthetic capacity were obtained through splitting data at N/P = 10 (independent limitation) for both V-cmax (R-2 = 0.40 vs. 0.59) and J(max) (R-2 = 0.38 vs. 0.64). A random forest model was used to accurately partition N from P limited trees and the two main variables used within this model were Photochemical Reflectance Index (PRI) and Solar-Induced Chlorophyll Fluorescence (SIF). Using data from the P limiting phase, the most precise models of P were created using PRI (R-2 = 0.75) and SIF (R-2 = 0.52). Indices that were proxies for chlorophyll were the most precise predictors of N within the N limiting phase but strong positive relationships were also evident between N and both PRI (R-2 = 0.83) and SIF (R-2 = 0.57). Through their correlations with N and P, there were strong positive relationships between both SIF, PRI and V-cmax (R-2 = 0.78 and 0.83, respectively) and J(max) (R-2 = 0.80 and 0.83, respectively) that were generalisable across both N and P limiting ranges. These results suggest that quantified SIF and PRI from hyperspectral images may have greater precision and generality for predicting both foliage nutrition and biochemical limitations to photosynthesis than other widely used hyperspectral indices.
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| 10. |
- Harrie, Lars, et al.
(författare)
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An evaluation of measures for quantifying map information
- 2010
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Ingår i: ISPRS Journal of Photogrammetry and Remote Sensing. - : Elsevier BV. - 0924-2716. ; 65:3, s. 266-274
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Tidskriftsartikel (refereegranskat)abstract
- A real-time map must not contain too much information. Therefore, we need measures of map information that could guideline the selection of data layers and the real-time generalisation process. In this paper we evaluate measures of amount of information and distribution of information. The evaluation is performed by (1) defining measures, (2) implementing the measures, (3) computing the measures for some test maps, and finally (4) comparing the values of the measures with human judgement of the map information. For amount of information, we found that the measures number of objects, number of points and object line length had better correspondence with human judgement than object area. We also found that measures based on the size of Voronoi regions (of objects respective points) can be used for identifying distribution of information. The results are based on the testing of only building objects. Future work should extend the test, using all object types.
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