Semantic change detection (SCD) is a crucial research topic in remote sensing. To achieve high-precision semantic segmentation results, Segment Anything Model-Guided Feature Enhancement (SAM-FE) is proposed. SAM-FE utilizes Mobile-SAM to extract features from bi-temporal remote sensing images (RSIs). In addition, the cross-temporal feature aggregation module (CTFA), the multiscale contextual information fusion module (MCIF), and the change feature enhancement module (CFE) are utilized to enhance the general features and the representation of the change information of the RSIs, thus improving the accuracy of change detection. Experimental results indicate that SAM-FE significantly outperforms the existing methods in both the Second datasets and MusSCD, with F1 of 0.6241 and 0.8316, respectively. Meanwhile, SAM-FE maintains lower parameters, demonstrating its superiority and practicality.

With the rapid advancement of remote sensing technology, change detection using bitemporal remote sensing images has significant applications in land use planning and environmental monitoring. The emergence of convolutional neural networks (CNNs) has accelerated the development of deep learning-based change detection. However, existing deep learning algorithms exhibit limitations in understanding bitemporal feature relationships and accurately identifying change region boundaries. Moreover, they inadequately explore feature interactions between bitemporal images before extracting differential features. To address these issues, this article proposes a novel interactive and supervised dual-mode attention network (ISDANet). In the feature encoding stage, we employ the lightweight MobileNetV2 as the backbone to extract bitemporal features. Additionally, we design the neighbor feature aggregation module (NFAM) to aggregate semantic features from adjacent scales within the dual-branch backbone, enhancing the representation of temporal features. We further introduce the interactive attention enhancement module (IAEM), which effectively integrates self-attention and cross-attention mechanisms. This establishes deep interactions between bitemporal features, suppresses irrelevant noise, and ensures precise focus on true change regions. In the feature decoding stage, the supervised attention module (SAM) reweights differential features and leverages supervisory signals to guide the learning of attention mechanisms, significantly improving boundary detection accuracy. SAM dynamically aggregates multilevel features, balancing high-level semantics and low-level details to capture subtle changes in complex scenes. The proposed model achieves F1 scores that are 0.28%, 1.6%, and 0.76% higher than the best comparative method, spatiotemporal enhancement and interlevel fusion network (SEIFNet), on three CD datasets [LEVIR-CD, Guangzhou dataset (GZ-CD), and Sun Yat-sen University dataset (SYSU-CD)], respectively, while maintaining a lightweight design with only 6.93 M parameters and 3.46G floating-point operations (FLOPs). The code is available athttps://github.com/RenHongjin6/ISDANet.

Deep learning has significantly reshaped the landscape of remote sensing change detection (RSCD). However, detecting subtle changes remains a formidable challenge due to the simultaneous presence of three critical issues: domain shift from seasonal and illumination variations, severe class imbalance between change and background areas, and the need for high deployment efficiency. To address these multifaceted problems in a unified manner, this paper introduces SDTNet, a comprehensive framework featuring three key innovations. First, to counter domain shift, we propose the Style-Unified Temporal Difference Contrastive Learning Strategy (STDCL), a novel, fine-grained contrastive learning method that guides the model to decouple real from pseudo changes without incurring additional inference costs. Second, to mitigate severe class imbalance, we design the Adaptive Dice Focal Loss (ADFLoss), which, unlike static loss functions, introduces two novel adaptive factors to dynamically balance sample weights and implement a curriculum learning strategy. Third, for efficient feature enhancement, we present the Dual-Branch Temporal Difference Transformer (DTDFormer), an efficiency-aware architecture that adapts the differential attention mechanism for bi-temporal inputs and incorporates a sparse attention module to reduce computational complexity. Experimental results demonstrate that our integrated SDTNet framework achieves state-of-the-art (SOTA) performance across five large-scale change detection datasets. The code is publicly available at https://github.com/Baobaon/SDTNet.

Change detection is an important research area in remote sensing. To achieve accurate results, it is essential to extract multi-scale spatial information from images while filtering out noise. However, existing models lack this capability. Therefore, Multi-Scale Feature Gathering Network (MSFGNet) is proposed. Within MSFGNet, Bi-Temporal Image Multi-Level Fusion Module (BMF) is utilized to fuse bi-temporal remote sensing images. Additionally, Multi-Receptive Field Features Extraction Module (MRFE) is utilized to extract deep features. Within MRFE, Large Receptive Field Features Extraction Module (LRFE) and Multi-Scale Information Fusion Module (MSIF) are designed, which use large kernel convolution and dilated convolution respectively to capture spatial information with large receptive fields. Furthermore, Cross-Dimension Feature Sifting Fusion Module (CDFSF) is designed to sift noise from various dimensions, fusing valuable information. Across multiple public datasets, MSFGNet consistently achieves the best experimental results. The code can be accessed at https://github.com/juncyan/msfgnet.git.

In recent years, deep learning methods have achieved great success in various fields due to their strong performance in practical applications. In this paper, we present a light-weight neural network for Parkinson's disease diagnostics, in which a series of hand-drawn data are collected to distinguish Parkinson's disease patients from healthy control subjects. The proposed model consists of a convolution neural network (CNN) cascading to long-short-term memory (LSTM) to adapt the characteristics of collected time-series signals. To make full use of their advantages, a multilayered LSTM model is firstly used to enrich features which are then concatenated with raw data and fed into a shallow one-dimensional (1D) CNN model for efficient classification. Experimental results show that the proposed model achieves a high-quality diagnostic result over multiple evaluation metrics with much fewer parameters and operations, outperforming conventional methods such as support vector machine (SVM), random forest (RF), lightgbm (LGB) and CNN-based methods.

This article presents a novel intrinsic image transfer (IIT) algorithm for image illumination manipulation, which creates a local image translation between two illumination surfaces. This model is built on an optimization-based framework composed of illumination, reflectance and content photo-realistic losses, respectively. Each loss is first defined on the corresponding sub-layers factorized by an intrinsic image decomposition and then reduced under the well-known spatial-varying illumination illumination-invariant reflectance prior knowledge. We illustrate that all losses, with the aid of an "exemplar" image, can be directly defined on images without the necessity of taking an intrinsic image decomposition, thereby giving a closed-form solution to image illumination manipulation. We also demonstrate its versatility and benefits to several illumination-related tasks: illumination compensation, image enhancement and tone mapping, and high dynamic range (HDR) image compression, and show their high-quality results on natural image datasets.

In this paper, we propose a semi-sparsity smoothing method based on a new sparsity-induced minimization scheme. The model is derived from the observations that semi-sparsity prior knowledge is universally applicable in situations where sparsity is not fully admitted such as in the polynomial-smoothing surfaces. We illustrate that such priors can be identified into a generalized $L_{0}$ -norm minimization problem in higher-order gradient domains, giving rise to a new "feature-aware" filter with a powerful simultaneous-fitting ability in both sparse singularities (corners and salient edges) and polynomial-smoothing surfaces. Notice that a direct solver to the proposed model is not available due to the non-convexity and combinatorial nature of $L_{0}$ -norm minimization. Instead, we propose to solve it approximately based on an efficient half-quadratic splitting technique. We demonstrate its versatility and many benefits to a series of signal/image processing and computer vision applications.

A fundamental problem in computer vision is establishing correspondence between features in two images of the same scene. The computational burden in this problem is solving for the optimal mapping and transformation between the two scenes. In this paper we present a sieve algorithm for efficiently estimating the transformation and correspondence. A sieve algorithm uses approximations to generate a sequence of increasingly accurate estimates of the correspondence. Initially, the approximations are computationally inexpensive and are designed to quickly sieve through the space of possible solutions. As the space of possible solutions shrinks, greater accuracy is required and the complexity of the approximations increases.