Contextual PolSAR image classification using fractal dimension and support vector machines

Aghababaee, Hossein; Amini, Jalal; Tzeng, Y.C.

doi:10.5721/eujrs20134618

Cited by 16 publications

(11 citation statements)

References 26 publications

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“…In addition, several features can be selected and fused into the framework easily. For the future, the classification framework can be improved by: 1) using other information and dissimilarity measures may be more suitable or complementary, such as more complicated statistical models [Vasile et al, 2010], more components of reduced features, scattering mechanisms, texture and features like fractal information in the image [Aghababaee et al, 2013]; 2) Considering the methods that employ the edge information as well as the covariance matrix information are very interesting for over-segment generation. Moreover, In order to create a fully unsupervised classification framework, the clusters that obtain from the game theory can be merged without user intervention.…”

Section: Resultsmentioning

confidence: 99%

“…In this way, the over-segments are generated using the adapted k-means and region merging are performed using these three algorithms. The ground truth of the San Francisco image gleaned from [Aghababaee et al, 2013], which contains 4040 urban pixels, 4897 ocean pixels and 3514 vegetation pixels. In the preprocessing operation, this PolSAR image is processed with the IDAN refined filter [Vasile et al, 2006].…”

Section: Experiments Using Airsar Imagementioning

confidence: 99%

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Game Theoretic Classification of Polarimetric SAR images

Aghababaee

Sahebi

2015

European Journal of Remote Sensing

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This paper reports on a region based classification of polarimetric synthetic aperture radar (PolSAR) images using the concept of game theory. The proposed method mainly contains the following steps. Firstly, the PolSAR image is partitioned into over-segments using an adaptation of k-means approach. Then, in order to compute the similarity between two distinct oversegments, a measure from polarimetric features and region size is defined. Finally, the regions or over-segments are merged into the meaningful clusters using a game theory based approach. In the game theory way, region merging problem is transformed into an iterative figure/ground separation state. In other words, considering the similarity measure, over-segments that belong to the figure compete with others through the game and obtain a considerable advantage in comparison with others. Accordingly, these privileged over-segments can be merged as an individual cluster. For clustering of the remaining over-segments, the procedure should be repeated. The performance of the proposed classification framework on simulated and real data sets is presented and analyzed; and the experimental results show that the framework provides a promising solution for classification of PolSAR images.

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Section: Resultsmentioning

confidence: 99%

Section: Experiments Using Airsar Imagementioning

confidence: 99%

Game Theoretic Classification of Polarimetric SAR images

Aghababaee

Sahebi

2015

European Journal of Remote Sensing

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“…Furthermore, the CNN is good at dealing with color images, so the Pauli RGB image is suitable. For these reasons, many classification algorithms use the Pauli RGB image as their input [16,17], and so does our method.…”

Section: Generating Pauli Rgb Image To Obtain the Spatial Featurementioning

confidence: 99%

Dual-Branch Deep Convolution Neural Network for Polarimetric SAR Image Classification

et al. 2017

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Abstract:The deep convolution neural network (CNN), which has prominent advantages in feature learning, can learn and extract features from data automatically. Existing polarimetric synthetic aperture radar (PolSAR) image classification methods based on the CNN only consider the polarization information of the image, instead of incorporating the image's spatial information. In this paper, a novel method based on a dual-branch deep convolution neural network (Dual-CNN) is proposed to realize the classification of PolSAR images. The proposed method is built on two deep CNNs: one is used to extract the polarization features from the 6-channel real matrix (6Ch) which is derived from the complex coherency matrix. The other is utilized to extract the spatial features of a Pauli RGB (Red Green Blue) image. These extracted features are first combined into a fully connected layer sharing the polarization and spatial property. Then, the Softmax classifier is employed to classify these features. The experiments are conducted on the Airborne Synthetic Aperture Radar (AIRSAR) data of Flevoland and the results show that the classification accuracy on 14 types of land cover is up to 98.56%. Such results are promising in comparison with other state-of-the-art methods.

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“…Support vector machines (SVMs) are superior image classification techniques in airborne and satellite imagery, applying a set of machine learning algorithms and having their roots in statistical learning theory (Erfanifard et al 2014). Due to its ability to handle the nonlinear classifier problem, SVM is able to go beyond the limitations of linear learning machines by implementation of the kernel function, which paves the way to find a nonlinear decision function (Aghababaee et al 2013). User accuracies and producer accuracies of all of classifications are presented in Tables 1, 2 and 3.…”

Section: Data Preparationmentioning

confidence: 99%

Urban sprawl assessment and modeling using landsat images and GIS

Mohammady

Delavar

2016

Model. Earth Syst. Environ.

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Nowadays, urban sprawl phenomenon have been seen in many of cities in the developing and developed countries. Urban sprawl is considered as a particular kind of urban growth which comes up with a lot of negative effects. Thus, monitoring, analyzing and modeling of this phenomenon seem to be unavoidable. This paper assess urban sprawl in Tehran Metropolis as the capital of Iran and models urban sprawl in this mega city utilizing artificial neural networks and adaptive neuro-based fuzzy inference system methods with remote sensing data and geospatial information systems spatial analyses and modeling capabilities to simulate Tehran urban growth. The results confirm that this city has experienced sprawl and sprawl has an increasing rate. Three Landsat imageries from TM and ETM? sensors taken in 1988, 1999 and 2010 and seven predictor variables include distance to road, distance to green space, slope, elevation, distance to fault, distance to developed area and number of urban cells in 3 by 3 neighborhoods have been used for urban sprawl assessment and modeling. Relative operating characteristics (ROC) and sensitivity analyses have been used to evaluate simulation results. In this research, two evaluation steps have been implemented using ROC and on the both of them, ANFIS presented the best performance vs two different proposed ANN structures.

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Contextual PolSAR image classification using fractal dimension and support vector machines

Cited by 16 publications

References 26 publications

Game Theoretic Classification of Polarimetric SAR images

Game Theoretic Classification of Polarimetric SAR images

Dual-Branch Deep Convolution Neural Network for Polarimetric SAR Image Classification

Urban sprawl assessment and modeling using landsat images and GIS

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