Hidden Markov Models for crop recognition in remote sensing image sequences

Leite, Paula Beatriz Cerqueira; Feitosa, Raul Queiroz; Formaggio, Antônio Roberto; Costa, Gilson Alexandre Ostwald Pedro da; Pakzad, Kian; Sanches, I. D.

doi:10.1016/j.patrec.2010.02.008

Cited by 53 publications

(39 citation statements)

References 10 publications

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“…The Landsat archive constitutes the longest record of multi-spectral data available at medium spatial resolution, and has been used for crop mapping purposes at regional scale [10][11][12], using either spectral response and/or vegetation indices [13][14][15][16]. The opening of the Landsat archives in 2008 has pushed forward the implementation of data analysis and image classification techniques based on multi-temporal features and time series analysis [17].…”

Section: Introductionmentioning

confidence: 99%

In-Season Mapping of Crop Type with Optical and X-Band SAR Data: A Classification Tree Approach Using Synoptic Seasonal Features

et al. 2015

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Abstract:The work focuses on developing a classification tree approach for in-season crop mapping during early summer, by integrating optical (Landsat 8 OLI) and X-band SAR (COSMO-SkyMed) data acquired over a test site in Northern Italy. The approach is based on a classification tree scheme fed with a set of synoptic seasonal features (minimum, maximum and average, computed over the multi-temporal datasets) derived from vegetation and soil condition proxies for optical (three spectral indices) and X-band SAR (backscatter) data. Best performing input features were selected based on crop type separability and preliminary classification tests. The final outputs are crop maps identifying seven crop types, delivered during the early growing season (mid-July). Validation was carried out for two seasons (2013 and 2014), achieving overall accuracy greater than 86%. Results highlighted the contribution of the X-band backscatter (σ°) in improving mapping accuracy and promoting the transferability of the algorithm over a different year, when compared to using only optical features.

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Section: Introductionmentioning

confidence: 99%

In-Season Mapping of Crop Type with Optical and X-Band SAR Data: A Classification Tree Approach Using Synoptic Seasonal Features

et al. 2015

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“…Then, a Gaussian smoothing and the Sobel operator were applied (c.f. Leite et al, 2011). Finally, the Watershed segmentation algorithm was employed to generate segments with consistent borders across all images.…”

Section: Datasetmentioning

confidence: 99%

“…The second one is based on modelling temporal dependencies by rules (Simonneaux et al, 2008), or adaptive strategies to select the relevance of features over time for specific crops (Müller et al, 2010). The last one incorporates temporal dependencies into statistical models (Melgani and Serpico, 2004) (Leite et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

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A Method to Estimate Temporal Interaction in a Conditional Random Field Based Approach for Crop Recognition

Diaz¹,

Feitosa²,

Sanches³

et al. 2016

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

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ABSTRACT:This paper presents a method to estimate the temporal interaction in a Conditional Random Field (CRF) based approach for crop recognition from multitemporal remote sensing image sequences. This approach models the phenology of different crop types as a CRF. Interaction potentials are assumed to depend only on the class labels of an image site at two consecutive epochs. In the proposed method, the estimation of temporal interaction parameters is considered as an optimization problem, whose goal is to find the transition matrix that maximizes the CRF performance, upon a set of labelled data. The objective functions underlying the optimization procedure can be formulated in terms of different accuracy metrics, such as overall and average class accuracy per crop or phenological stages. To validate the proposed approach, experiments were carried out upon a dataset consisting of 12 co-registered LANDSAT images of a region in southeast of Brazil. Pattern Search was used as the optimization algorithm. The experimental results demonstrated that the proposed method was able to substantially outperform estimates related to joint or conditional class transition probabilities, which rely on training samples.

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“…Studies like Leite et al (2011);Siachalou et al (2015) incorporate crop phenology via Hidden Markov Models (HMM). They used HMM to model temporal context among crop phenology stages but lack a proper spatial context framework.…”

Section: Introductionmentioning

confidence: 99%

Crop Type Mapping From a Sequence of Terrasar-X Images With Dynamic Conditional Random Fields

Kenduiywo¹,

Bargiel²,

Soergel³

2016

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Crop phenology is dynamic as it changes with times of the year. Such biophysical processes also look spectrally different to remote sensing satellites. Some crops may depict similar spectral properties if their phenology coincide, but differ later when their phenology diverge. Thus, conventional approaches that select only images from phenological stages where crops are distinguishable for classification, have low discrimination. In contrast, stacking images within a cropping season limits discrimination to a single feature space that can suffer from overlapping classes. Since crop backscatter varies with time, it can aid discrimination. Therefore, our main objective is to develop a crop sequence classification method using multitemporal TerraSAR-X images. We adopt first order markov assumption in undirected temporal graph sequence. This property is exploited to implement Dynamic Conditional Random Fields (DCRFs). Our DCRFs model has a repeated structure of temporally connected Conditional Random Fields (CRFs). Each node in the sequence is connected to its predecessor via conditional probability matrix. The matrix is computed using posterior class probabilities from association potential. This way, there is a mutual temporal exchange of phenological information observed in TerraSAR-X images. When compared to independent epoch classification, the designed DCRF model improved crop discrimination at each epoch in the sequence. However, government, insurers, agricultural market traders and other stakeholders are interested in the quantity of a certain crop in a season. Therefore, we further develop a DCRF ensemble classifier. The ensemble produces an optimal crop map by maximizing over posterior class probabilities selected from the sequence based on maximum F1-score and weighted by correctness. Our ensemble technique is compared to standard approach of stacking all images as bands for classification using Maximum Likelihood Classifier (MLC) and standard CRFs. It outperforms MLC and CRFs by 7.70% and 6.42% in overall accuracy, respectively.

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Hidden Markov Models for crop recognition in remote sensing image sequences

Cited by 53 publications

References 10 publications

In-Season Mapping of Crop Type with Optical and X-Band SAR Data: A Classification Tree Approach Using Synoptic Seasonal Features

In-Season Mapping of Crop Type with Optical and X-Band SAR Data: A Classification Tree Approach Using Synoptic Seasonal Features

A Method to Estimate Temporal Interaction in a Conditional Random Field Based Approach for Crop Recognition

Crop Type Mapping From a Sequence of Terrasar-X Images With Dynamic Conditional Random Fields

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