A hierarchical Markovian model for multiscale region-based classification of vector-valued images

Katartzis, Antonis; Vanhamel, Iris; Sahli, Hichem

doi:10.1109/tgrs.2004.842405

Cited by 32 publications

(33 citation statements)

References 27 publications

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“…More recently, many studies [11][12][13] have considered information encoded in regions (group of pixels) for RSI classification tasks. The growth of classification approaches based on regions has been analyzed in [38].…”

Section: A Region-based Classification Methodsmentioning

confidence: 99%

“…These new trends have encouraged research studies that compare techniques based on pixels and/or regions [2,12,14,15], and propose new segmentation techniques that support the classification of regions in RSIs [20,[39][40][41]. 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%

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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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Section: A Region-based Classification Methodsmentioning

confidence: 99%

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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“…Namely, the proposed MSRAG approach of Section 2.2 and a scale-space-based hierarchy, denoted MRAT (multiscale region adjacency tree), as described in [3]. The creation of the MRAT is based on the creation of the multi-scale tower using the PDE approach of Section 2.1.…”

Section: Segmentation and Msrag Generation Analysismentioning

confidence: 99%

“…Spatial patterns provide valuable contextual information for achieving consistent labeling. Moreover, since segmentation could provide very small (or too big) ambiguous regions, multi-scale analysis allows one to determine the objects' class more accurately [1,2] in the most appropriate scale within a range (multi-scale) [3].…”

Section: Introductionmentioning

confidence: 99%

Efficient Learning of Spatial Patterns with Multi-Scale Conditional Random Fields for Region-Based Classification

Alioscha-Pérez

Sahli

2014

Remote Sensing

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Automatic image classification is of major importance for a wide range of applications and is supported by a complex process that usually requires the identification of individual regions and spatial patterns (contextual information) among neighboring regions within images. Hierarchical conditional random fields (CRF) consider both multi-scale and contextual information in a unified discriminative probabilistic framework, yet they suffer from two main drawbacks. On the one hand, their current classification performance still leaves space for improvement, mostly due to the use of very simple or inappropriate pairwise energy expressions to model complex spatial patterns; on the other hand, their training remains complex, particularly for multi-class problems. In this work, we investigated alternative pairwise energy expressions to better account for class transitions and developed an efficient parameters learning strategy for the resultant expression. We propose: (i) a multi-scale CRF model with novel energies that involves information related to the multi-scale image structure; and (ii) an efficient maximum margin parameters learning procedure where the complex learning problem is decomposed into simpler individual multi-class sub-problems. During experiments conducted on several well-known satellite image data sets, the suggested multi-scale CRF exhibited between a 1% and 15% accuracy improvement compared to other works. We also found that, on different multi-scale decompositions, the total number of regions and their average size have a direct impact on the classification results.Remote Sens. 2014, 6 6728

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“…For example, Ju et al (2005), referring to the example of land-cover classification, contend that in a complex image, a single scale may not adequately represent all the classes of interest. An appropriate alternative to single-scale observations may be to use multi-scale analysis (Coops and Waring 2001, Andrefouet et al 2003, Li et al 2003, Katartzis et al 2005. In simple terms, multi-scale remote sensing analysis involves the observation of the Earth's surface at more than one spatial scale.…”

Section: Spatial Variabilitymentioning

confidence: 99%

On scales and dynamics in observing the environment

Aplin

2006

International Journal of Remote Sensing

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Natural and anthropogenic processes at the Earth's surface operate at a range of spatial and temporal scales. Different scales of observation are required to match the spatial scales of the processes under observation. At the same time, the temporal sampling rate of the observing systems must be reconciled with the dynamics of the processes observed. Bringing together these issues requires insight, innovation and, inevitably, compromise. This paper reviews spatial and temporal considerations in remote sensing and introduces the papers in this Special Issue on 'Scales and Dynamics in Observing the Environment'. The review comprises three main sections. The first section focuses on spatial variability in remote sensing, while the second section focuses on temporal variability in remote sensing. The third section links these two issues, focusing on the interplay of space and time in remote sensing. The review is primarily theoretical, explaining spatial and temporal properties of remote sensing and remotely sensed phenomena. Where appropriate, however, practical examples are included to demonstrate how remote sensing is used in environmental applications. Following the review, the papers included in the Special Issue are introduced, outlining their significance in the context of 'Scales and Dynamics in Observing the Environment'.

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A hierarchical Markovian model for multiscale region-based classification of vector-valued images

Cited by 32 publications

References 27 publications

Interactive Multiscale Classification of High-Resolution Remote Sensing Images

Interactive Multiscale Classification of High-Resolution Remote Sensing Images

Efficient Learning of Spatial Patterns with Multi-Scale Conditional Random Fields for Region-Based Classification

On scales and dynamics in observing the environment

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