Remote sensing (RS) image scene classification has wide applications in the field of RS. Although existing methods have achieved remarkable performance, there are still limitations in feature extraction and lightweight design. Current multi-branch models, although performing well, have large parameter counts and high computational costs, making them difficult to deploy on resource-constrained edge devices such as unmanned aerial vehicles (UAVs). On the other hand, lightweight models like StarNet, having less parameter, but rely on element-wise multiplication to generate features and lack the capture of explicit long-range spatial feature, resulting in insufficient classification accuracy. To address these issues, this letter proposes a lightweight mamba-based hybrid network, namely LMHMamba, whose core is an innovative lightweight multi-feature hybrid mamba (LMHM) module. This module combines the advantage of StarNet in implicitly generating high-dimensional nonlinear features, introduces a lightweight state space module to enhance spatial feature learning capabilities, and then uses local and global attention modules to emphasize local and global features. This enables effective multi-dimensional feature fusion while maintaining low parameter. We validate the performance of LMHMamba model on three remote sensing scene classification datasets and compare it with mainstream lightweight models and the latest methods. Experimental results show that LMHMamba achieves advanced levels in both classification accuracy and computational efficiency, significantly outperforming existing lightweight models, providing an efficient solution for edge deployment. Code is available at https://github.com/yizhilanmaodhh/LMHMamba.

Remote sensing image change detection (CD), as a pivotal technology for monitoring Earth’s surface dynamics, plays a crucial role in urban planning, resource management, and disaster assessment. Despite the success of deep learning-based methods, they still suffer from two significant limitations. Firstly, the inadequate exploitation of frequency-domain information restricts their ability to capture subtle structural and edge changes. Secondly, the suboptimal interaction strategies that predominantly rely on attention mechanisms or direct feature exchange that fails to fully model complex semantic differences between bi-temporal images. To address these challenges, we propose a Wavelet Attention-based Group Interaction Network (WAGINet), which leverages wavelet attention for joint frequency-spatial domain feature learning and uses a group-wise feature exchange mechanism to optimize bi-temporal interaction. The wavelet attention module decomposes features into high-low frequency components and emphasize important ones to improve edge-aware feature extraction. In the meanwhile, the group interaction strategy enables both channel-group and spatial-group feature exchange to capture the correlation between bi-temporal features while better protecting structural integrity, so that promotes more discriminative change representation. Experimental results on public LEVIR-CD and WHU-CD datasets show that WAGINet outperforms existing state-of-the-art methods, providing an effective solution for high-precision remote sensing image CD in complex scenarios. Code available at https://github.com/yizhilanmaodhh/WAGINet.

Automatically counting the number of pedestrians in an image is a challenging task, but is useful in many practical applications, including intelligent transport systems and congestion alerts. One of the challenges associated with this task is labeling, which is onerous as it requires annotating each pedestrian in the training images. To alleviate the labeling burden, one promising method is Active Learning (AL). Under a limited annotation budget, AL selects the most informative regions for annotation, based on the fact that visually similar regions often exist. Hence, the core of the AL strategy is the region-selection approach. Unlike in previous studies, where the approach aims to select the region representative of the entire image, we argue that the region with highly inconsistent predictions is more informative. To support this argument, we propose a new AL approach: Inter-Scale Bilateral Inconsistency (ISBI). The ISBI measures the prediction divergence between the original image and its scaled images, which allows identification of the regions with the highest inconsistencies. Furthermore, to fully leverage the abundant unlabeled regions, we implement a pseudo labeling strategy for semi-supervised learning. Extensive experiments on mainstream datasets demonstrate that our approach can outperform other state-of-the-art approaches in most cases.

Change detection from synthetic aperture radar (SAR) imagery is critical in remote sensing research. Existing methods have made significant progress in the application of convolutional neural networks (CNNs) and attention mechanisms. However, traditional CNNs suffer the limitations in feature representation due to their depth and width constraints, and struggle to effectively capture complex interactions between image features. To address these issues, we propose a novel dynamic bilinear fusion network (DBFNet) for change detection in SAR imagery. First, to compensate for the lack of traditional convolutional representation capability, we design a dynamic shift convolution module that adaptively aggregates multiple convolution kernels and shifts pixels, enabling richer and more detailed features to be extracted. Second, a bilinear fusion module (BFM) is designed to generate the bilinear joint representation between parallel features by computing a matrix outer product of feature maps. The parallel features include both intraimage and interimage features, thereby effectively modeling the complex interactions and capturing the dependence relationship between spatiotemporal features. The experimental results on three real SAR datasets demonstrate the superior performance of DBFNet compared to existing state-of-the-art methods. The codes are available athttps://github.com/yizhilanmaodhh/DBFNet.

Industrial recommender systems usually hold data from multiple business scenarios and are expected to provide recommendation services for these scenarios simultaneously. In the retrieval step, the topK high-quality items selected from a large number of corpus usually need to be various for multiple scenarios. Take Alibaba display advertising system for example, not only because the behavior patterns of Taobao users are diverse, but also differentiated scenarios' bid prices assigned by advertisers vary significantly. Traditional methods either train models for each scenario separately, ignoring the cross-domain overlapping of user groups and items, or simply mix all samples and maintain a shared model which makes it difficult to capture significant diversities between scenarios. In this paper, we present Adaptive Domain Interest Network(ADIN) that adaptively handles the commonalities and diversities across scenarios, making full use of multi-scenarios data during training. Then the proposed method is able to improve the performance of each business domain by giving various topK candidates for different scenarios during online inference. Specifically, our proposed ADIN models the commonalities and diversities for different domains by shared networks and domain-specific networks, respectively. In addition, we apply the domain-specific batch normalization and design the domain interest adaptation layer for feature-level domain adaptation. A self training strategy is also incorporated to capture label-level connections across domains.ADIN has been deployed in the display advertising system of Alibaba, and obtains 1.8% improvement on advertising revenue.

Convolutional neural networks (CNN) can extract shift-invariant features, and have been widely applied in change detection task. However, common CNN lacks noise robustness and needs supervised data, to alleviate these problems, in this paper, we propose a novel deep shearlet network (ShearNet) for change detection in SAR images. In the network, a shearlet denoising layer (SDL) is designed to enhance the representation ability of common CNN. In SDL, feature maps are decomposed into subband coefficients by shearlet transform (ST). Due to optimal sparse representation property and highly direction sensitivity of ST, the network can capture important geometric information. Then, hard-threshold shrinkage is applied to high frequency subbands to drop small coefficients that are most likely to be noise, so that reduce the effect of noise. Finally, ShearNet is trained by introducing a noise-robust loss with noisy labels. The noisy labels are obtained by deep clustering that shows more robustness than existing preclassification methods. This fine-tuning process novelly follows the paradigm of learning from noisy labels to aside the difficulty of precisely labeling samples. Our experimental results on multiple real SAR datasets show that ShearNet can boost accuracy, and have better applicability for change detection in SAR images. The source code is available at https://github.com/yizhilanmaodhh/ShearNet.

Synthetic aperture radar (SAR) image change detection (CD) is an important application in the field of remote sensing. Due to the lack of labeled data especially in the pixelwise task, it is urgent to develop unsupervised techniques to effectively detect changes. In this article, we propose a novel unsupervised representation learning framework for CD in SAR images, called multiscale self-attention (SA) deep clustering based on octave convolution. The main motivation is that a convolutional neural network (CNN) has the ability to extract significant feature hidden in input images, but it relies heavily on annotated data. Clustering is typically free from supervision; however, SAR images always suffer from speckle noise, which is unfriendly for clustering. Thus, we integrate unsupervised clustering with CNN to learn clustering-friendly feature representations. In the unified framework, CNN feature learning and clustering can be optimized end-to-end without supervision. To better suppress speckle noise and boost the joint optimization for distinguishing changes and unchanges, we use the K-means++ algorithm that is robust to noise as the clustering algorithm. In the meanwhile, we introduce the octave convolution and SA mechanism into the network to fully mine important spatial structure information for enhancing noise resistance of the network. Moreover, multiscale fusion modules are proposed to fuse multiscale input into a complementary feature representation that contains more context and semantic information around each pixel so that it refines the difference feature extraction while reducing speckle noise. Experiments on challenging SAR data sets demonstrate the effectiveness and potential of the proposed model compared with the current state-of-the-art algorithms.