Floods are one of the most common natural disasters, causing fatalities and severe economic and environmental impacts, directly affecting agriculture, urban infrastructure, and transportation networks. Hence, it is of utmost importance that flooded areas are efficiently and effectively identified in the aftermath. Synthetic aperture radar (SAR) images are invaluable to this end, since the amount of microwave energy reflected from water is less than that from land, due to its low surface roughness and lack of apparent texture. In this study, we explore the combination of histograms with deep neural networks for the purpose of flood mapping. The proposed histogram extraction layers, specifically designed for SAR content, are integrated into deep segmentation neural networks and are tested on two real SAR datasets. Experimental results have shown that histogram layers integrated into deep segmentation neural networks improve the performance up to 6% in terms of intersection over union (IoU) with a negligible increase in the number of learnable parameters. The code of the work will be available athttps://github.com/ilterturkmenli/HistSegNet.

Domain adaptation is one of the prominent strategies for handling both the scarcity of pixel-level ground truth and the domain shift, that is widely encountered in large-scale land use/cover map calculation. Studies focusing on adversarial domain adaptation via re-styling source domain samples, commonly through generative adversarial networks (GANs), have reported varying levels of success, yet they suffer from semantic inconsistencies, visual corruptions, and often require a large number of target domain samples. In this letter, we propose a new lightweight unsupervised domain adaptation (UDA) method for the semantic segmentation of very high-resolution remote sensing images, based on an image-to-image translation (I2IT) approach, via an encoder–decoder strategy where latent content representations are mixed across domains, and a perceptual network module and loss function enforce visual semantic consistency. We show through cross-domain comparative experiments that it: 1) leads to semantically consistent images; 2) can operate with a single target domain sample (i.e., one-shot); and 3) at a fraction of the number of parameters required from the state-of-the-art methods, while still outperforming them. Code is available at github.com/Sarmadfismael/RSOS_I2I.

We propose a completely unsupervised pixel-wise anomaly detection (AD) method for hyperspectral images (HSIs). The proposed method consists of three steps called data preparation, reconstruction, and detection. In the data preparation step, we apply a background purification to train the deep network in an unsupervised manner. In the reconstruction step, we propose to use three different deep autoencoding adversarial network (AEAN) models including 1-D-AEAN, 2-D-AEAN, and 3-D-AEAN which are developed for working on spectral, spatial, and joint spectral–spatial domains, respectively. The goal of the AEAN models is to generate synthesized HSIs which are close to real ones. A reconstruction error map (REM) is calculated between the original and the synthesized image pixels. In the detection step, we propose to use a weighted RX (WRX) -based detector in which the pixel weights are obtained according to REM. We compare our proposed method with the classical Reed–Xiaoli (RX), WRX, support vector data description (SVDD)-based, collaborative representation-based detector (CRD), adaptive weight deep belief network (AW-DBN) detector, and deep autoencoder AD (DAEAD) method on real hyperspectral data sets. The experimental results show that the proposed approach outperforms other detectors in the benchmark.

We propose multivariate skewed${t}$-distribution (MVSkt) for hyperspectral anomaly detection (AD). The proposed distribution model is able to increase the detection performance of autoencoder (AE)-based anomaly detectors. In the proposed method, the reconstruction error of a deep AE is modeled with a skewed${t}$-distribution. The deep AE network is trained based on adversarial learning strategy by feeding its input with the hyperspectral data cubes. The parameters of the${t}$-distribution model are estimated using variational Bayesian approach. We define an MVSkt-based detection rule for pixel-wise AD. We compare our proposed method with those based on the multivariate normal (MVN) distribution and the robust MVN variance–mean mixture distributions on real hyperspectral datasets. The experimental results show that the proposed approach outperforms other detectors in the benchmark.

We propose a new learning rule for sparse multinomial logistic regression (SMLR). The new rule is the generalization of the one proposed in the pioneering work by Krishnapuram et al. In our proposed method, the parameters of SMLR are iteratively estimated from log-posterior by using some approximations. The proposed update rule provides a faster convergence compared to the state-of the-art methods used for SMLR parameter estimation. The estimated parameters are tested on the pixel-based classification of hyperspectral images. The experimental results on real hyperspectral images show that the classification accuracy of proposed method is also better than those of the state-of-the-art methods.

Attribute profiles (APs) are among the most prominent spectral-spatial pixel description tools, widely employed with optical, and hyperspectral images in particular. Their use, however, with the SAR data has been very limited. In this letter, we investigate the use of the recently developed feature attribute profiles (FPs) for the pixel-based classification of SAR images. Compared to low-level features such as intensity and amplitude, FPs are shown to provide high-level features incorporating successfully the spatial information of the pixels. Based on the classification results obtained on real TerraSAR-X images, it is shown that the FPs are capable of more accurately classifying the pixels compared to conventional and widely used SAR feature extraction methods such as gray-level co-occurrence matrices, histograms of oriented gradients, local binary patterns, and Gabor filters.

We propose a mixture-based superpixel segmentation method for synthetic aperture radar (SAR) images. The method uses SAR image amplitudes and pixel coordinates as features. The feature vectors are modeled statistically by taking into account the SAR image statistics. We resort to finite mixture models to cluster the pixels into superpixels. After superpixel segmentation, we classify different land covers such as urban, land, and lake using the features extracted from each superpixel. Based on the classification results obtained on real TerraSAR-X images, it is shown that the results obtained by the proposed superpixel method are capable of achieving a more accurate classification compared with those obtained by state-of-the-art superpixel segmentation methods such as quick-shift, turbo pixels, simple linear iterative clustering, and pixel intensity and location similarity.

We propose a spatially dependent mixture model for contextual classification of synthetic aperture radar (SAR) images. The proposed mixture model is based on the local image histograms modeled by multinomial densities. The contextual information is included into the mixture model both in the pixel and the class label domain by using local histograms and autologistic regression, respectively. Based on the classification results obtained on real TerraSAR-X images, it is shown that the proposed model is capable of more accurately classifying the pixels particularly in the heterogeneous regions, such as urban areas, compared with the conventional mixture model.

We propose a local histogram based mixture model for synthetic aperture radar image classification. In this model, local histograms are modeled by multinomial densities. The model has also a spatially dependent class label prior to achieve a smooth contextual classification. Based on the classification results obtained on real TerraSAR-X image, it is shown that the proposed model is capable of more accurately classifying pixels especially in the heterogeneous regions, like urban areas, compared to conventional mixture model.

We propose a novel image prior for the non-parametric Bayesian mixture model based unsupervised classification of SAR images. We modified the Normalized Gamma Process prior that constitutes a more general form of the Dirichlet Process prior in order to enclose the contribution of the adjacent pixels into the classification scheme. This yields an image classification prior embedded in a mixture model that allows infinite number of clusters and enables reaching to smoothed classification maps. Based on the classification results obtained on synthetic and real TerraSAR-X images, it is shown that the proposed model is capable of accurately classifying the pixels. It applies a simple iterative update scheme at a single run without performing a hierarchical clustering strategy as used in the previously proposed methods. It is also demonstrated that the model order estimation accuracy of the proposed method outperforms the conventional finite mixture models.

In this paper, we combine amplitude and texture statistics of the synthetic aperture radar images for the purpose of model-based classification. In a finite mixture model, we bring together the Nakagami densities to model the class amplitudes and a 2-D auto-regressive texture model with t-distributed regression error to model the textures of the classes. A non-stationary multinomial logistic latent class label model is used as a mixture density to obtain spatially smooth class segments. The classification expectation-maximization algorithm is performed to estimate the class parameters and to classify the pixels. We resort to integrated classification likelihood criterion to determine the number of classes in the model. We present our results on the classification of the land covers obtained in both supervised and unsupervised cases processing TerraSAR-X, as well as COSMO-SkyMed data.

We compare the performance of the texture and the amplitude based mixture density models for urban area extraction from high resolution Synthetic Aperture Radar (SAR) images. We use an Auto-Regressive (AR) model with t-distribution error for the textures and a Nakagami density for the amplitudes. We exploit a Multinomial Logistic (MnL) latent class label model as a mixture density to obtain spatially smooth class segments. We combine the Classification EM (CEM) algorithm with the hierarchical agglomeration strategy and a model order selection criterion called Integrated Completed Likelihood (ICL).We test our algorithm on TerraSAR-X data provided by DLR/DFD.

In this paper we review a maximum a posteriori (MAP) approach detection method in a Bayesian scheme which incorporates prior information about the source flux distribution, the locations and the number of sources of extragalactic point sources in images of the Cosmic Microwave Background. This new technique allows us to obtain fast solutions and to fix the number of detected sources in a non-arbitrary way. The performance of the method is superior to that of the standard frequentist approach based on the matched filter.

We combine both amplitude and texture statistics of the Synthetic Aperture Radar (SAR) images using Products of Experts (PoE) approach for classification purpose. We use Nak-agami density to model the class amplitudes. To model the textures of the classes, we exploit a non-Gaussian Markov Random Field (MRF) texture model with t-distributed regression error. Non-stationary Multinomial Logistic (MnL) latent class label model is used as a mixture density to obtain spatially smooth class segments. We perform the Classification Expectation-Maximization (CEM) algorithm to estimate the class parameters and classify the pixels. We obtained some classification results of water, land and urban areas in both supervised and semi-supervised cases on TerraSAR-X data.

We propose to model the image differentials of astrophysical source maps by Student's t-distribution and to use them in the Bayesian source separation method as priors. We introduce an efficient Markov Chain Monte Carlo (MCMC) sampling scheme to unmix the astrophysical sources and describe the derivation details. In this scheme, we use the Langevin stochastic equation for transitions, which enables parallel drawing of random samples from the posterior, and reduces the computation time significantly (by two orders of magnitude). In addition, Student's t-distribution parameters are updated throughout the iterations. The results on astrophysical source separation are assessed with two performance criteria defined in the pixel and the frequency domains.

We propose an adaptive Monte Carlo Markov Chain (MCMC) simulation for the Bayesian source separation problem and apply it to the unmixing of astrophysical components. In this method, we use the Langevin stochastic equation for transitions, which enables parallel drawing of random samples from the posterior, and which reduces the computation time significantly (by two orders of magnitude). In addition to this, the parameters of the Markov Random Field (MRF) model are updated via Maximum Likelihood (ML) throughout the iterations.

We investigate the source separation problem of random fields within a Bayesian framework. The Bayesian formulation enables the incorporation of prior image models in the estimation of sources. Due to the intractability of the analytical solution, we resort to numerical methods for the joint maximization of the a posteriori distribution of the unknown variables and parameters. We construct the prior densities of pixels using Markov random fields based on a statistical model of the gradient image, and we use a fully Bayesian method with modified-Gibbs sampling. We contrast our work to approximate Bayesian solutions such as Iterated Conditional Modes (ICM) and to non-Bayesian solutions of ICA variety. The performance of the method is tested on synthetic mixtures of texture images and astrophysical images under various noise scenarios. The proposed method is shown to outperform significantly both its approximate Bayesian and non-Bayesian competitors.