Segmentation of a high-resolution urban scene using texture operators

Conners, Richard W.; Trivedi, Mohan M.; Harlow, Charles A.

doi:10.1016/0734-189x(84)90197-x

Cited by 332 publications

(170 citation statements)

References 29 publications

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“…[10] Image texture, as defined by the cooccurrence matrix, was among the first local image properties to be developed capable of segmenting image areas that appear visually distinct to a human observer [Haralick et al, 1973;Haralick, 1979;Conners et al, 1984]. It is therefore reasonable to hypothesize that image texture may allow for grain size determination since patches of different grain sizes appear distinct to a human observer provided image resolution is sufficient.…”

Section: Mapping Grain Size From Airborne Imagery: Theoretical Considmentioning

confidence: 99%

Catchment‐scale mapping of surface grain size in gravel bed rivers using airborne digital imagery

Carbonneau

Lane

Bergeron

2004

Water Resources Research

182

255

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[1] This study develops and assesses two methods for estimating median surface grain sizes using digital image processing from centimeter-resolution airborne imagery. Digital images with ground resolutions of 3 cm and 10 cm were combined with field calibration measurements to establish predictive relationships for grain size as a function of both local image texture and local image semivariance. Independently acquired grain size data were then used to assess the algorithm performance. Results showed that for the 3 cm imagery both local image semivariance and texture are highly sensitive to median grain size, with semivariance being a better predictor than image texture. However, in the case of 10 cm imagery, sensitivity of image semivariance and texture to grain size was poor, and this scale of imagery was found to be unsuitable for grain size estimation. This study therefore demonstrates that local image properties in very high resolution digital imagery allow for automated grain size measurement using image processing and remote sensing methods.

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Section: Mapping Grain Size From Airborne Imagery: Theoretical Considmentioning

confidence: 99%

Catchment‐scale mapping of surface grain size in gravel bed rivers using airborne digital imagery

Carbonneau

Lane

Bergeron

2004

Water Resources Research

182

255

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“…Fifteen features proposed by [51] and three features from [52] are derived. This selection fits with their function availability in the Google Earth Engine (GEE) [53].…”

Section: Vegetation Index (Vi)mentioning

confidence: 99%

“…Table A1 lists textural features from [51] with their corresponding formulas; in these, we have used the following notational conventions: p(i, j) is the (i,j)th entry in a normalized gray tone matrix, p x (i) = ∑ N g j = 1 P(i, j) is the ith entry in the marginal-probability matrix computed by summing the rows of p(i, j) , for fixed i, p x (j) = ∑ N g j = 1 P(i, j), is the jth entry in the marginal-probability matrix computed by summing the columns of p(i, j) , for fixed j, N g , is the number of distinct gray levels in the quantized image, p x+y (k) = ∑ N g i = 1 ∑ N g j = 1 p(i, j) i+j = k , and p x−y (k) = ∑ N g i = 1 ∑ N g j = 1 p(i, j) |i−j| = k Table A2 specifies names of the textural features proposed by [52], and their formulas, in which the following notation is used: s(i, j, δ, T) is the (i,j)th entry in a normalized gray level matrix, equivalent to p(i,j), T represents the region and shape used to estimate the second order probabilities, and δ = (∆x, ∆y) is the displacement vector. Table A1.…”

Section: Conflicts Of Interestmentioning

confidence: 99%

A Cloud-Based Multi-Temporal Ensemble Classifier to Map Smallholder Farming Systems

et al. 2018

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Smallholder farmers cultivate more than 80% of the cropland area available in Africa. The intrinsic characteristics of such farms include complex crop-planting patterns, and small fields that are vaguely delineated. These characteristics pose challenges to mapping crops and fields from space. In this study, we evaluate the use of a cloud-based multi-temporal ensemble classifier to map smallholder farming systems in a case study for southern Mali. The ensemble combines a selection of spatial and spectral features derived from multi-spectral Worldview-2 images, field data, and five machine learning classifiers to produce a map of the most prevalent crops in our study area. Different ensemble sizes were evaluated using two combination rules, namely majority voting and weighted majority voting. Both strategies outperform any of the tested single classifiers. The ensemble based on the weighted majority voting strategy obtained the higher overall accuracy (75.9%). This means an accuracy improvement of 4.65% in comparison with the average overall accuracy of the best individual classifier tested in this study. The maximum ensemble accuracy is reached with 75 classifiers in the ensemble. This indicates that the addition of more classifiers does not help to continuously improve classification results. Our results demonstrate the potential of ensemble classifiers to map crops grown by West African smallholders. The use of ensembles demands high computational capability, but the increasing availability of cloud computing solutions allows their efficient implementation and even opens the door to the data processing needs of local organizations.

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“…First-order statistics refer to the marginal grey level distribution, second-order statistics to the joint grey level distribution of pairs of pixels, and higher-order statistics to the joint grey level distribution of three or more pixels. The basic computation of 14 features was introduced by Haralick et al (1973) and later complemented by others (Conners et al, 1984;Parkkinen et al, 1990).…”

Section: Introductionmentioning

confidence: 99%

Computerised image analysis of biocrystallograms originating from agricultural products

Andersen¹,

Henriksen²,

Laursen³

et al. 1999

Computers and Electronics in Agriculture

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Procedures are presented for computerised image analysis of biocrystallogram images, originating from biocrystallization investigations of agricultural products. The biocrystallization method is based on the crystallographic phenomenon that when adding biological substances, such as plant extracts, to aqueous solutions of dihydrate CuCl 2 , biocrystallograms with reproducible dendritic crystal structures are formed during crystallisation. The morphological features found in the structures are traditionally applied for visual ranking or classification, e.g. in comparative studies of the effects of farming systems on crop quality. The circular structures contain predominantly a single centre from where ramifications expand in a zonal structure. In previous studies primarily texture analysis was applied, and the images analysed and classified by means of a circular region-of-interest (ROI), i.e. the region specified for analysis. In the present study the objective was to examine how the discriminative information relevant for classification purposes is distributed over the zonal structure, and how the information is affected by the varying location of the crystallisation centre. The texture analysis procedures were applied to a so-called degradation series of 33 images, including seven groups representing discrete 'treatment levels'. The biocrystallograms were produced over seven consecutive days, on the basis of a single carrot extract degrading while stored at 6°C. This degradation is known to induce systematic changes in morpholog-* Corresponding author. Tel.: +45-35-283520; fax: +45-35-282175; e-mail: joa@kvl.dk. (1999) 51-69 52 ical features over a number of successive days. The biocrystallograms were scanned at 600 dpi, with 256 grey levels. Eight first-order statistical parameters were calculated for four resolution scales, and 15 second-order parameters for five scales, giving a total of 107 observations for each image. Classification of an individual image was performed by means of stepwise discriminant analysis. Four main types, and several subtypes and sizes of ROI were examined. The 33 images as well as a subset of 21 images were examined. When imposing a restriction on the centre location in the subset, thereby reducing the within-group variance, the scores were markedly improved. Classifications of the total set and the subset showed scores up to 84.8 and 100%, respectively. A number of parameters showed a monotonic relationship with degradation day number. Multiple linear regressions based on up to eight parameters indicated strong relationships, with R 2 up to 0.98. It is concluded that the procedures were able to discriminate the seven groups of images, and are applicable for biocrystallization investigations of agricultural products. Perspectives for the application of image analysis are briefly mentioned.

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Segmentation of a high-resolution urban scene using texture operators

Cited by 332 publications

References 29 publications

Catchment‐scale mapping of surface grain size in gravel bed rivers using airborne digital imagery

Catchment‐scale mapping of surface grain size in gravel bed rivers using airborne digital imagery

A Cloud-Based Multi-Temporal Ensemble Classifier to Map Smallholder Farming Systems

Computerised image analysis of biocrystallograms originating from agricultural products

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