The inherent speckle noise in synthetic aperture radar (SAR) images limits the accuracy of SAR image change detection. As a crucial step in unsupervised change detection, existing difference map generation methods primarily utilise neighbourhood information to counteract the interference caused by speckle noise. However, pixels within the neighbourhood can themselves be affected by heterogeneous pixels and noise. Therefore, this paper proposes a difference map generation method, partial neighbourhood ratio (PNR), which relies on high-confidence homogeneous pixels within the neighbourhood for difference calculation. Specifically, under the assumption that the local neighbourhood of SAR images follows a normal distribution, we develop a method for selecting high-confidence homogeneous pixels. This method quantifies inter-neighborhood dissimilarity by leveraging the statistical features of predominantly homogeneous pixel clusters within an adaptive framework, thereby reducing the impact of noise and enhancing the accuracy of difference expression. Experimental results demonstrate the superior performance of the proposed PNR. The change detection results, obtained by applying both manual trial-and-error and dual-domain network on three SAR datasets, have validated the effectiveness of the proposed algorithm.

The utilisation of synthetic aperture radar (SAR) imagery for change detection can effectively circumvents the stringent limitations imposed by weather and lighting conditions, and is finding widespread applications in fields such as disaster monitoring and urban research. To address issues of edge blurring, severe noise interference and sample imbalance, an automated SAR change detection framework is proposed based on enhanced edge information and prototype constrained clustering. Firstly, a gradient-based neighbourhood ratio is designed to reinforce the edge information of the difference map, facilitating robust differential information representations. Subsequently, to obtain accurate samples in an unsupervised manner, we have developed prototype constrained hierarchical clustering for pre-classification. The quantity and quality of selected samples can be precisely guaranteed through the utilisation of histogram analysis and prototype constraints. In the sample learning and prediction phases, a class-balanced noise-tolerant change detection network is proposed that combines focal loss and mean absolute error loss, further tackling the sample imbalance issue, strengthening noise resistance and improving change detection accuracy. Comprehensive experimental results and analysis conducted on five benchmark datasets have validated the effectiveness and robustness of the proposed method.

Characterising spatial information as reinforcement of spectral signatures can largely assist the performance in hyperspectral image (HSI) classification. Markov random fields (MRFs) are probabilistic image texture models, and capable of encoding contextual dependencies through charactering local conditional probabilities. As a representative standardised measure of dispersion of image probability distributions, coefficient of variation (CoV) can be a useful tool for characterising spatial heterogeneity. Their parameter derivation processes also share strong compatibility with convolutional neural networks that specifies spatial correlations in local neighbourhoods. In this work, we propose an MRF and CoV based spectral-spatial convolutional network (MRF-CoV-CNN) for HSI classification. MRF models and CoVs are characterised as measures of spatial distributions and further combined with spectral information. Then the proposed MRF-CoV-CNN takes the fused features as input and produces reliable classification results. Comprehensive experiments have been conducted on the Pavia university dataset and the Salinas dataset to evaluate the proposed method both visually and quantitatively.

This paper presents a visual perception based framework for semantic change detection between multi-temporal SAR images. Gabor texture features have been incorporated into accurately and effectively extracting differences for generating saliency difference image. Then we have proposed to build a visual perception based multi-class change indicator to describe various types of changes. In final classification stage, samples with high confidences have been selected and fed into PCANet to predict pixel classes for semantic SAR change detection. Comprehensive experiments have been conducted on the area of Nanhaizi park. Comparisons with other change detection methods have been reported to verify the usefulness of the proposed method in accurately detecting and describing various types of changes.

In hyperspectral image (HSI) classification task, effectively deriving and incorporating spatial information into spectral features is one of a key focus as it can largely influence the performance. Markov random fields (MRFs) are generative and flexible image texture models, and capable of effectively extracting spatial neighbourhood information along multiple spectral wavebands in an unsupervised way. Its parameter estimation process also shares strong compatibility with deep architecture, especially the convolutional neural networks. In this work, we propose an MRF based spectral-spatial fusion network (SSFNet) for HSI classification. Spatial features are extracted using MRF models and further fused with spectral information. Then the proposed SSFNet takes the fused features as input and produces reliable classification results. Comprehensive experiments conducted on the Indian pines and the Pavia university datasets are reported to verify the proposed method.

Facial expression synthesis has drawn increasing attention in computer vision, graphics and animation. Recently, generative adversarial nets (GANs) have become a new perspective for face synthesis and have had remarkable success in generating photorealistic images and image‐to‐image translation. In this study, the authors present an appearance‐based facial expression synthesis framework, ApprGAN, by combining shape and texture and introducing cycle consistency and identity mapping into the adversarial learning. Specifically, given an input face image, a pair of shape and texture generators are trained for synthetic shape deformation and expression detail generation, respectively. Extensive experiments on expression synthesis and cross‐database synthesis were conducted, together with comparisons with the existing methods. Results of expression synthesis and quantitative verification on various databases show the effectiveness of ApprGAN in synthesising photorealistic and identity‐preserving expressions and its marked improvement over the existing methods.

We present here an unsupervised approach to learning suitable features for a deep learning framework applied to image classification. PCANet was introduced as a simple and efficient baseline for deep learning approaches which used cascaded principle component analysis (PCA) derived filter banks, as well as other simple image processing elements such as binary hashing and blockwise histograms. This was followed by DCTNet which used discrete cosine transform (DCT) filter banks as a learning-free alternative. In this paper we propose SOMNet which uses self-organizing map (SOM) based filters offering a non-orthogonal alternative to PCANet providing comparable performance. It is well established that SOM is a non-linear version of PCA but does not suffer from the same constraints. We also show that through the use of a simple trick in the binarization process results in a dramatic reduction in the dimension of the final feature vector, thus allowing the utilization of more filters which could lead to deeper and more complex structures in further work. We also demonstrate the results of a hybrid methodology that clusters generative Markov random fields (MRF) as filters which provides more diverse features in a data driven approach to deep learning.