Object-based image analysis through nonlinear scale-space filtering

Tzotsos, Angelos; Καράντζαλος, Κωνσταντίνος; Argialas, Demetre P.

doi:10.1016/j.isprsjprs.2010.07.001

Cited by 59 publications

(62 citation statements)

References 33 publications

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“…Motivated by the successful application of object-based image analysis (OBIA) in very high resolution satellite data [3,4,9,47,48,54,55], the developed methodology was based on OBIA principles, while it was designed to address vineyard detection, vine canopy extraction and vine variety discrimination. In particular, for every processing step, several experiments were performed based on features employed from the literature on similar crop identification/detection studies (e.g., [8,9,16,[22][23][24]27,30]), while the optimal ones for all datasets were selected from a larger pool, through several experiments, feature analysis tools (assessing how each feature contributes to the discrimination task) and a trial and error procedure for fine tuning their parameters.…”

Section: Methodsmentioning

confidence: 99%

“…By minimizing the average heterogeneity and maximizing their respective homogeneity, this algorithm consecutively merges pixels or existing image objects. The procedure was based on certain user-defined parameters, like scale, color/shape and smoothness/compactness, that were weighted together to define a homogeneity criterion [48,56]. The classification scheme was based on two segmentation levels, i.e., Level 1 and Level 2 (Figure 2), while the core classification was conducted at Level 2, and then, the final classification maps were derived from the finer scale Level 1.…”

Section: Vineyard Detectionmentioning

confidence: 99%

“…We have employed an object-based classification framework [47,48] and developed an efficient set of features and rules that are able to effectively identify vineyards in high resolution multispectral pan-sharpened data in different viticulture regions. Moreover, the developed framework was validated for the extraction of the vine canopy and the discrimination of vine varieties through numerous experiments towards the separation of three and up to six different vine varieties/clones.…”

Section: Introductionmentioning

confidence: 99%

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Vineyard Detection and Vine Variety Discrimination from Very High Resolution Satellite Data

2016

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Abstract:In order to exploit remote sensing data operationally for precision agriculture applications, efficient and automated methods are required for the accurate detection of vegetation, crops and different crop varieties. To this end, we have designed, developed and evaluated an object-based classification framework towards the detection of vineyards, the vine canopy extraction and the vine variety discrimination from very high resolution multispectral data. A novel set of spectral, spatial and textural features, as well as rules, segmentation scales and a set of parameters are proposed based on object-based image analysis. The validation of the developed methodology was carried out on multitemporal WorldView-2 satellite data at four different viticulture regions in Greece. Concurrent in situ canopy reflectance observations were acquired from a portable spectroradiometer during the field campaigns. The performed quantitative evaluation indicated that the developed approach managed in all cases to detect vineyards with high completeness and correctness detection rates, i.e., over 89%. The vine canopy extraction methodology was validated with overall accuracy (OA) rates of above 96%. The quantitative evaluation regarding the vine variety discrimination task, including experiments with up to six different varieties, reached OA rates above 85% at the parcel level. The combined analysis of the experimental results with the spectral signatures from the in situ reflectance data indicated that certain vine varieties (e.g., Merlot) presented distinct spectral patterns across the VNIR spectrum.

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Section: Methodsmentioning

confidence: 99%

Section: Vineyard Detectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Vineyard Detection and Vine Variety Discrimination from Very High Resolution Satellite Data

2016

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“…Likewise, new research has been carried out to take advantage of the use of multiple scales of data [6,8,21,22].…”

Section: A Region-based Classification Methodsmentioning

confidence: 99%

Interactive Multiscale Classification of High-Resolution Remote Sensing Images

Santos

Gosselin²,

Philipp‐Foliguet³

et al. 2013

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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The use of remote sensing images (RSIs) as a source of information in agribusiness applications is very common. In those applications, it is fundamental to identify and understand trends and patterns in space occupation.However, the identification and recognition of crop regions in remote sensing images are not trivial tasks yet. In high resolution image analysis and recognition, many of the problems are related to the representation scale of the data, and to both the size and the representativeness of the training set. In this paper, we propose a method for interactive classification of remote sensing images considering multiscale segmentation. Our aim is to improve the selection of training samples using the features from the most appropriate scales of representation. We use a boosting-based active learning strategy to select regions at various scales for user's relevance feedback. The idea is to select the regions that are closer to the border that separates both target classes: relevant and non-relevant regions.The experimental results showed that the combination of scales produces better results than isolated scales in a relevance feedback process. Furthermore, the interactive method achieved good results with few user interactions.The proposed method needs only a small portion of the training set to build classifiers that are as strong as the ones generated by a supervised method that uses the whole training set.

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“…An advantage of OBIA is to create objects on the image through segmentation using neighbour and spectral similarity pixels, contrary to pixel-based classification. OBIA offers advantages for multi-scale image and hierarchical object representation, especially in high spatial resolution image (Tzotsos et al, 2011;Kim et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

A Region-Based Multi-Scale Approach for Object-Based Image Analysis

Kavzoğlu

Erdemir

Tonbul

2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Commission VII, WG VII/4KEY WORDS: Object Based Image Analysis, Region-based scale, Classification, Multiresolution Segmentation, Estimation of Scale Parameter (ESP). ABSTRACT:Within the last two decades, object-based image analysis (OBIA) considering objects (i.e. groups of pixels) instead of pixels has gained popularity and attracted increasing interest. The most important stage of the OBIA is image segmentation that groups spectrally similar adjacent pixels considering not only the spectral features but also spatial and textural features.Although there are several parameters (scale, shape, compactness and band weights) to be set by the analyst, scale parameter stands out the most important parameter in segmentation process. Estimating optimal scale parameter is crucially important to increase the classification accuracy that depends on image resolution, image object size and characteristics of the study area. In this study, two scale-selection strategies were implemented in the image segmentation process using pan-sharped Qickbird-2 image. The first strategy estimates optimal scale parameters for the eight sub-regions. For this purpose, the local variance/rate of change (LV-RoC) graphs produced by the ESP-2 tool were analysed to determine fine, moderate and coarse scales for each region. In the second strategy, the image was segmented using the three candidate scale values (fine, moderate, coarse) determined from the LV-RoC graph calculated for whole image. The nearest neighbour classifier was applied in all segmentation experiments and equal number of pixels was randomly selected to calculate accuracy metrics (overall accuracy and kappa coefficient). Comparison of region-based and image-based segmentation was carried out on the classified images and found that region-based multi-scale OBIA produced significantly more accurate results than imagebased single-scale OBIA. The difference in classification accuracy reached to 10% in terms of overall accuracy.

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Object-based image analysis through nonlinear scale-space filtering

Cited by 59 publications

References 33 publications

Vineyard Detection and Vine Variety Discrimination from Very High Resolution Satellite Data

Vineyard Detection and Vine Variety Discrimination from Very High Resolution Satellite Data

Interactive Multiscale Classification of High-Resolution Remote Sensing Images

A Region-Based Multi-Scale Approach for Object-Based Image Analysis

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