Urban monitoring using multi-temporal SAR and multi-spectral data

Gómez-Chova, L.; Fernàndez–Prieto, Diego; Calpe, J.; Soria, Emilio; Vila, José M.; Camps‐Valls, Gustau

doi:10.1016/j.patrec.2005.08.004

Cited by 68 publications

(42 citation statements)

References 17 publications

(17 reference statements)

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“…The available features were the seven Landsat bands, two SAR backscattering intensities (0-35 days), and the SAR interferometric coherence. We used all seven Landsat TM spectral bands and appended two SAR features: the computed coherence Co and a spatially filtered version of the coherence F Co, which was specially designed to increase the urban-area discrimination [17]. 2) FC1.…”

Section: Data Collectionmentioning

confidence: 99%

Remote Sensing Feature Selection by Kernel Dependence Measures

Camps‐Valls

Mooij

Schölkopf

2010

IEEE Geosci. Remote Sensing Lett.

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Abstract-This letter introduces a nonlinear measure of independence between random variables for remote sensing supervised feature selection. The so-called Hilbert-Schmidt independence criterion (HSIC) is a kernel method for evaluating statistical dependence and it is based on computing the Hilbert-Schmidt norm of the cross-covariance operator of mapped samples in the corresponding Hilbert spaces. The HSIC empirical estimator is easy to compute and has good theoretical and practical properties. Rather than using this estimate for maximizing the dependence between the selected features and the class labels, we propose the more sensitive criterion of minimizing the associated HSIC p-value. Results in multispectral, hyperspectral, and SAR data feature selection for classification show the good performance of the proposed approach.

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Section: Data Collectionmentioning

confidence: 99%

Remote Sensing Feature Selection by Kernel Dependence Measures

Camps‐Valls

Mooij

Schölkopf

2010

IEEE Geosci. Remote Sensing Lett.

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“…For OC-SVM, we take the class 'urban' as the target class. The images used are from ERS2 SAR and Landsat TM sensors acquired in 1999 over the area of Naples, Italy [34]. The dataset have seven Landsat bands, two SAR backscattering intensities (0-35 days), and the SAR interferometric coherence.…”

Section: Experimental Results For Supervised Classificationmentioning

confidence: 99%

“…This section presents the experimental results of semisupervised methods in the same urban monitoring application presented in the previous section [34]. However, different sets of labeled and unlabeled training samples were used in order to test the performance of the SSL methods.…”

Section: Experimental Results For Semisupervised Classificationmentioning

confidence: 99%

A Review of Kernel Methods in Remote Sensing Data Analysis

Gómez-Chova

Muñoz-Marí

Laparra

et al. 2011

Optical Remote Sensing

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Summary. Kernel methods have proven effective in the analysis of images of the Earth acquired by airborne and satellite sensors. Kernel methods provide a consistent and well-founded theoretical framework for developing nonlinear techniques and have useful properties when dealing with low number of (potentially high dimensional) training samples, the presence of heterogenous multimodalities, and different noise sources in the data. These properties are particularly appropriate for remote sensing data analysis. In fact, kernel methods have improved results of parametric linear methods and neural networks in applications such as natural resource control, detection and monitoring of anthropic infrastructures, agriculture inventorying, disaster prevention and damage assessment, anomaly and target detection, biophysical parameter estimation, band selection, and feature extraction.This chapter provides a survey of applications and recent theoretical developments of kernel methods in the context of remote sensing data analysis. The specific methods developed in the fields of supervised classification, semisupervised classification, target detection, model inversion, and nonlinear feature extraction are revised both theoretically and through experimental (illustrative) examples. The emergent fields of transfer, active, and structured learning, along with efficient parallel implementations of kernel machines, are also revised.

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“…Such trend favours development of novel image classification methods that can handle temporal data (Jianya et al, 2008). This is especially true for radar sensors which overcome limitations of optical sensors: their signals can penetrate clouds and are independent of daylight (Gomez-Chova et al, 2006;Tupin, 2010). Incorporating crop growing degree day information into multitemporal radar images from TerraSAR-X is likely to improve crop classification.…”

Section: Introductionmentioning

confidence: 99%

Spatial-Temporal Conditional Random Fields Crop Classification From Terrasar-X Images

Kenduiywo

Bargiel

Soergel

2015

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

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ABSTRACT:The rapid increase in population in the world has propelled pressure on arable land. Consequently, the food basket has continuously declined while global demand for food has grown twofold. There is need to monitor and update agriculture land-cover to support food security measures. This study develops a spatial-temporal approach using conditional random fields (CRF) to classify co-registered images acquired in two epochs. We adopt random forest (RF) as CRF association potential and introduce a temporal potential for mutual crop phenology information exchange between spatially corresponding sites in two epochs. An important component of temporal potential is a transitional matrix that bears intra-and inter-class changes between considered epochs. Conventionally, one matrix has been used in the entire image thereby enforcing stationary transition probabilities in all sites. We introduce a site dependent transition matrix to incorporate phenology information from images. In our study, images are acquired within a vegetation season, thus perceived spectral changes are due to crop phenology. To exploit this phenomena, we develop a novel approach to determine site-wise transition matrix using conditional probabilities computed from two corresponding temporal sites. Conditional probability determines transitions between classes in different epochs and thus we used it to propagate crop phenology information. Classification results show that our approach improved crop discrimination in all epochs compared to state-of-the-art mono-temporal approaches (RF and CRF monotemporal) and existing multi-temporal markov random fields approach by Liu et al. (2008).

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Urban monitoring using multi-temporal SAR and multi-spectral data

Cited by 68 publications

References 17 publications

Remote Sensing Feature Selection by Kernel Dependence Measures

Remote Sensing Feature Selection by Kernel Dependence Measures

A Review of Kernel Methods in Remote Sensing Data Analysis

Spatial-Temporal Conditional Random Fields Crop Classification From Terrasar-X Images

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