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Supervised classification of hyperspectral images is a challenging task due to the relatively low ratio between the number of training samples and the number of spectral channels. Subspace-based classification methods deal with this difficulty by assuming that feature vectors lie in a low-dimensional subspace. Based on the fact that a class in a hyperspectral image may be composed of a number of different...
Performance of hyperspectral image classification depends on feature extraction. Compared with conventional hand-crafted feature extraction, deep learning can learn feature with more discriminative information. In this paper, a two-channel deep convolutional neural network (Two-CNN) is proposed to learn jointly spectral-spatial feature from hyperspectral image. The proposed model is composed of two...
We propose a convolutional neural network (CNN) model for remote sensing image classification. Using CNNs provides us with a means of learning contextual features for large-scale image labeling. Our network consists of four stacked convolutional layers that downsample the image and extract relevant features. On top of these, a deconvolutional layer upsamples the data back to the initial resolution,...
Satellite image classification is a key task used in remote sensing for the automatic interpretation of a large amount of information. Today there exist many types of classification algorithms using advanced image processing methods enhancing the classification accuracy rate. One of the best state-of-the-art methods which improves significantly the classification of complex scenes relies on Self-Dual...
In this paper the Supervised Locally Linear Embedding (SLLE) algorithm is introduced into polarimetric SAR (PolSAR) feature dimensionality reduction (DR) and land cover classification. SLLE technique, as a supervised nonlinear manifold learning method, can obtain a low-dimensional embedding space which preserves both the local geometric property of high-dimensional data and discriminative information...
This paper proposes a custom convolutional deep belief network for polarimetric synthetic aperture radar (PolSAR) data feature extraction. The proposed architecture stands out through the interesting features it shows, starting with the fact that it is adapted to fully polarimetric SAR data. Then, the multilayer approach allows the stepwise discovery of higher-level features. The convolutional approach...
This paper investigates the utilization of game theory models for automated analysis of hyperspectral imagery fused with other remotely sensed and/or in situ data. The author analyzes two approaches to using strategic, competitive game theory models for groundcover classification, including the application of game theory models to (i) feature-level fusion and (ii) decision fusion for hypertemporal-hyperspectral...
The aim of this study is to build a model suitable to classify grassland management practices using satellite image time series with high spatial resolution. The study site is located in southern France where 52 parcels with three management types were selected. The NDVI computed from a Formosat-2 intra-annual time series of 17 images was used. To work at the parcel scale while accounting for the...
New high resolution Satellite Image Time Series (SITS) are becoming crucial to land cover mapping over large areas. Their high temporal resolution will allow to better depict scene dynamics. However, it will also increase the amount of data to process. The classification of these data involves therefore new challenges such as: (1) selecting the best feature set to use as input data, (2) dealing with...
Polarimetric synthetic aperture radar (PolSAR) images are widely applied in terrain and ground cover classification. Feature extraction and classifier design are both important in Pol- SAR image classification. In this paper, various target decompositions are applied to obtain different polarimetric features. Since that neighboring pixels usually belong to the same species, they can be simultaneously...
Traditional joint sparse representation based hyperspectral classification methods define a local region for each pixel. Through representing the pixels within the local region simultaneously, the class of the central pixel is able to be decided. A common limitation of this kind of methods is that only local pixels are considered in such methods, and thus, non-local information will be ignored. In...
In this paper we address the problem of urban optical imagery classification by developing a convolutional neural network (CNN) approach. We design a custom CNN that operates on local patches in order to produce dense pixel-level classification map. In this work we focus on a comprehensive dataset of 2.5-meter SPOT-5 imagery acquired at different dates and sites. The performance of the proposed model...
Earthen levees protect large areas of populated and cultivated land from flooding. The potential loss of life and property associated with the catastrophic failure of levees can be great. One type of problem that occurs along these levees which can lead to complete failure during a high water event is slough slides [1]. Using Entropy (H), Anisotropy (A), and alpha (α) parameters, we implemented Wishart...
In this article, a clustering-based band selection method is proposed to tackle the dimension reduction problem of hyperspectral data. The method is essentially based on low-rank doubly stochastic matrix decomposition, which is more stable than current low-rank approximation clustering methods. Experimental results show that the selected band subsets perform well in hyperspectral data classification...
In classification, a large number of features often make it difficult to select appropriate classification features. In such situations, feature selection or dimensionality reduction methods play an important role in classification. ReliefF algorithm is one of the most successful filtering feature selection methods. In this paper, some shortcomings of the ReliefF algorithm are improved, on the problem...
In this paper, a novel generic framework has been designed, developed and validated for addressing simultaneously the tasks of image registration, segmentation and change detection from multisensor, multiresolution, multitemporal satellite image pairs. Our approach models the inter-dependencies of variables through a higher order graph. The proposed formulation is modular with respect to the nature...
Nowadays, hyperspectral images have been an attractive subject for many researches in remote sensing area since they provide abundant information due to their wide range of spectral bands. On the one hand, classification plays a significant role in extraction of information for different applications. On the other hand, providing a huge amount of data by hyperspectral images may lead to complexity...
Almost concurrent imagery from Landsat-5 and Radarsat-2 are examined separately and in combination to maximize the accuracy of a simple classification of a typical multi-use grassland region in western Canada. Almost all classifications were of sufficient accuracy to be used in an operational sense. Landsat seven band classification was the most accurate, but was deemed less likely to be useful as...
Although fully polarimetric analysis techniques have been applied to remote sensing radar for charactering different surface scattering properties using, these techniques have not yet been widely adopted for Ground Penetrating Radar (GPR) applications. In 2015 we applied the “H-alpha decomposition” technique to polarimetric GPR data for classifying buried metallic targets such as wire branches, a...
In statistical classification, such mixture models allow a formal approach to unsupervised clustering. Fitting mixture distributions can be handled by a wide variety of techniques. A standard method to fit finite mixture models to observed data is the Expectation-Maximization (EM) algorithm which is an iterative procedure which converges to a (local) maximum of the marginal a posteriori probability...
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