Evidential Deep Learning with Spectral-Spatial Uncertainty Disentanglement for Open-Set Hyperspectral Domain Generalization

Open-set domain generalization(OSDG) for hyperspectral image classification presents significant challenges due to the presence of unknown classes in target domains and the need for models to generalize across multiple unseen domains without target-specific adaptation. Existing domain adaptation methods assume access to target domain data during training and fail to address the fundamental issue of domain shift when unknown classes are present, leading to negative transfer and reduced classification performance. To address these limitations, we propose a novel open-set domain generalization framework that combines four key components: Spectrum-Invariant Frequency Disentanglement (SIFD) for domain-agnostic feature extraction, Dual-Channel Residual Network (DCRN) for robust spectral-spatial feature learning, Evidential Deep Learning (EDL) for uncertainty quantification, and Spectral-Spatial Uncertainty Disentanglement (SSUD) for reliable open-set classification. The SIFD module extracts domain-invariant spectral features in the frequency domain through attention-weighted frequency analysis and domain-agnostic regularization, while DCRN captures complementary spectral and spatial information via parallel pathways with adaptive fusion. EDL provides principled uncertainty estimation using Dirichlet distributions, enabling the SSUD module to make reliable open-set decisions through uncertainty-aware pathway weighting and adaptive rejection thresholding. Experimental results on three cross-scene hyperspectral classification tasks show that our approach achieves performance comparable to state-of-the-art domain adaptation methods while requiring no access to the target domain during training. The implementation will be made available at https://github.com/amir-khb/SSUDOSDG upon acceptance.

Distance Transform Guided Mixup for Alzheimer's Detection

Alzheimer's detection efforts aim to develop accurate models for early disease diagnosis. Significant advances have been achieved with convolutional neural networks and vision transformer based approaches. However, medical datasets suffer heavily from class imbalance, variations in imaging protocols, and limited dataset diversity, which hinder model generalization. To overcome these challenges, this study focuses on single-domain generalization by extending the well-known mixup method. The key idea is to compute the distance transform of MRI scans, separate them spatially into multiple layers and then combine layers stemming from distinct samples to produce augmented images. The proposed approach generates diverse data while preserving the brain's structure. Experimental results show generalization performance improvement across both ADNI and AIBL datasets.

Single Domain Generalization for Alzheimer's Detection from 3D MRIs with Pseudo-Morphological Augmentations and Contrastive Learning

Although Alzheimer's disease detection via MRIs has advanced significantly thanks to contemporary deep learning models, challenges such as class imbalance, protocol variations, and limited dataset diversity often hinder their generalization capacity. To address this issue, this article focuses on the single domain generalization setting, where given the data of one domain, a model is designed and developed with maximal performance w.r.t. an unseen domain of distinct distribution. Since brain morphology is known to play a crucial role in Alzheimer's diagnosis, we propose the use of learnable pseudo-morphological modules aimed at producing shape-aware, anatomically meaningful class-specific augmentations in combination with a supervised contrastive learning module to extract robust class-specific representations. Experiments conducted across three datasets show improved performance and generalization capacity, especially under class imbalance and imaging protocol variations. The source code will be made available upon acceptance at https://github.com/zobia111/SDG-Alzheimer.

Change State Space Models for Remote Sensing Change Detection

Despite their frequent use for change detection, both ConvNets and Vision transformers (ViT) exhibit well-known limitations, namely the former struggle to model long-range dependencies while the latter are computationally inefficient, rendering them challenging to train on large-scale datasets. Vision Mamba, an architecture based on State Space Models has emerged as an alternative addressing the aforementioned deficiencies and has been already applied to remote sensing change detection, though mostly as a feature extracting backbone. In this article the Change State Space Model is introduced, that has been specifically designed for change detection by focusing on the relevant changes between bi-temporal images, effectively filtering out irrelevant information. By concentrating solely on the changed features, the number of network parameters is reduced, enhancing significantly computational efficiency while maintaining high detection performance and robustness against input degradation. The proposed model has been evaluated via three benchmark datasets, where it outperformed ConvNets, ViTs, and Mamba-based counterparts at a fraction of their computational complexity. The implementation will be made available at https://github.com/Elman295/CSSM upon acceptance.

Adapting the Biological SSVEP Response to Artificial Neural Networks

Neuron importance assessment is crucial for understanding the inner workings of artificial neural networks (ANNs) and improving their interpretability and efficiency. This paper introduces a novel approach to neuron significance assessment inspired by frequency tagging, a technique from neuroscience. By applying sinusoidal contrast modulation to image inputs and analyzing resulting neuron activations, this method enables fine-grained analysis of a network's decision-making processes. Experiments conducted with a convolutional neural network for image classification reveal notable harmonics and intermodulations in neuron-specific responses under part-based frequency tagging. These findings suggest that ANNs exhibit behavior akin to biological brains in tuning to flickering frequencies, thereby opening avenues for neuron/filter importance assessment through frequency tagging. The proposed method holds promise for applications in network pruning, and model interpretability, contributing to the advancement of explainable artificial intelligence and addressing the lack of transparency in neural networks. Future research directions include developing novel loss functions to encourage biologically plausible behavior in ANNs.

Explainable AI for Earth Observation: Current Methods, Open Challenges, and Opportunities

Deep learning has taken by storm all fields involved in data analysis, including remote sensing for Earth observation. However, despite significant advances in terms of performance, its lack of explainability and interpretability, inherent to neural networks in general since their inception, remains a major source of criticism. Hence it comes as no surprise that the expansion of deep learning methods in remote sensing is being accompanied by increasingly intensive efforts oriented towards addressing this drawback through the exploration of a wide spectrum of Explainable Artificial Intelligence techniques. This chapter, organized according to prominent Earth observation application fields, presents a panorama of the state-of-the-art in explainable remote sensing image analysis.

ADRMX: Additive Disentanglement of Domain Features with Remix Loss

The common assumption that train and test sets follow similar distributions is often violated in deployment settings. Given multiple source domains, domain generalization aims to create robust models capable of generalizing to new unseen domains. To this end, most of existing studies focus on extracting domain invariant features across the available source domains in order to mitigate the effects of inter-domain distributional changes. However, this approach may limit the model's generalization capacity by relying solely on finding common features among the source domains. It overlooks the potential presence of domain-specific characteristics that could be prevalent in a subset of domains, potentially containing valuable information. In this work, a novel architecture named Additive Disentanglement of Domain Features with Remix Loss (ADRMX) is presented, which addresses this limitation by incorporating domain variant features together with the domain invariant ones using an original additive disentanglement strategy. Moreover, a new data augmentation technique is introduced to further support the generalization capacity of ADRMX, where samples from different domains are mixed within the latent space. Through extensive experiments conducted on DomainBed under fair conditions, ADRMX is shown to achieve state-of-the-art performance. Code will be made available at GitHub after the revision process.

Unsupervised Domain Adaptation for Semantic Segmentation using One-shot Image-to-Image Translation via Latent Representation Mixing

Domain adaptation is one of the prominent strategies for handling both domain shift, that is widely encountered in large-scale land use/land cover map calculation, and the scarcity of pixel-level ground truth that is crucial for supervised semantic segmentation. Studies focusing on adversarial domain adaptation via re-styling source domain samples, commonly through generative adversarial networks, 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 unsupervised domain adaptation method for the semantic segmentation of very high resolution images, that i) leads to semantically consistent and noise-free images, ii) operates with a single target domain sample (i.e. one-shot) and iii) at a fraction of the number of parameters required from state-of-the-art methods. More specifically an image-to-image translation paradigm is proposed, based on an encoder-decoder principle where latent content representations are mixed across domains, and a perceptual network module and loss function is further introduced to enforce semantic consistency. Cross-city comparative experiments have shown that the proposed method outperforms state-of-the-art domain adaptation methods. Our source code will be available at \url{https://github.com/Sarmadfismael/LRM_I2I}.

Domain Generalisation for Object Detection

Domain generalisation aims to promote the learning of domain-invariant features while suppressing domain specific features, so that a model can generalise well on previously unseen target domains. This paper studies domain generalisation in the object detection setting. We propose new terms for handling both the bounding box detector and domain belonging, and incorporate them with consistency regularisation. This allows us to learn a domain agnostic feature representation for object detection, applicable to the problem of domain generalisation. The proposed approach is evaluated using four standard object detection datasets with available domain metadata, namely GWHD, Cityscapes, BDD100K, Sim10K and exhibits consistently superior generalisation performance over baselines.

Classification of remote sensing images using attribute profiles and feature profiles from different trees: a comparative study

The motivation of this paper is to conduct a comparative study on remote sensing image classification using the morphological attribute profiles (APs) and feature profiles (FPs) generated from different types of tree structures. Over the past few years, APs have been among the most effective methods to model the image's spatial and contextual information. Recently, a novel extension of APs called FPs has been proposed by replacing pixel gray-levels with some statistical and geometrical features when forming the output profiles. FPs have been proved to be more efficient than the standard APs when generated from component trees (max-tree and min-tree). In this work, we investigate their performance on the inclusion tree (tree of shapes) and partition trees (alpha tree and omega tree). Experimental results from both panchromatic and hyperspectral images again confirm the efficiency of FPs compared to APs.