The deployment of federated learning in real-world IoT ecosystems presents intrinsic challenges stemming from hardware asymmetry and sample scarcity, the existing related approaches generally homogenize model architectures and assume abundant labeled data, resulting in an inability to achieve fast generalization on devices with varying computational capabilities and dynamic task conditions. To address the aforementioned challenges, we propose a novel federated cross-device heterogeneous few-shot adaptation (Fed-CHFSA) method for IoT systems. In Fed-CHFSA, collaborating with other devices, each edge device obtains a personalized model that can not only adapt well to the category distribution of respective local data but also recognize unseen categories without data leakage. Specifically, we designed a fine-grained personalized aggregation (FPA) module and an information entropy-driven adaptive feature constraint (EAFC) module for the devices possessing a small amount of labeled data in the model aggregation and training phases of Fed-CHFSA, respectively. In each round of global communication, the edge device performs a certain epoch of personalized training locally under the normalization of EAFC in the feature space. Subsequently, the central server follows the FPA to finely aggregate the received model updates parameter-wise, and redistribute the updated global model to participating devices. After multiple rounds of global communication, every edge device acquires an optimal model more adaptable to local data and more generalized to unseen categories. Compared with existing FL and PFL algorithms on three benchmark few-shot learning (FSL) datasets, the proposed Fed-CHFSA framework achieves the best performance. The effectiveness of FPA and EAFC is also demonstrated by extensive ablation experiments.

With the rapid advancements in computer vision, high-resolution remote sensing imagery has become a crucial data source for object detection. Nevertheless, effectively utilizing limited computational resources and reducing the burden on satellite edge devices remains a significant challenge. To effectively reduce model complexity while maintaining its representational capacity, this article proposes a scale-aware pruning framework (SAPF) to enhance remote sensing object detection ability. First, this article classifies the convolutional layers in object detection models into two categories: layers with a single-scale feature representation and layers with a multiscale feature representation. For convolutional layers with single-scale features, we utilize singular value decomposition (SVD) to quantify feature importance and assess filter redundancy to enhance model efficiency. By removing less critical filters, this pruning criteria aims to reduce the model size and computational load without compromising performance. However, convolutional layers with multiscale features are crucial for optimizing feature extraction and balancing information capture across various scales. To address this, this article evaluates the similarity between convolutional layers with different scales to determine the contribution of various scale features in multiscale fusion. Surprisingly, the SAPF can reduce the FLOPs and parameters, as well as ensure the representational ability obviously when the YOLO v5s and Faster-RCNN are adopted to classify the NWPU VHR-10, RSOD, and SIMD datasets. This means we can save the training computation resources for the model. Additionally, SAPF can significantly improve the efficiency of the model in object detection to ensure its real-time performance.

Remote sensing (RS) scenarios usually involve sensitive geographic information on national security and regional development. In the commonly used centralized machine-learning paradigm, data dispersed in various locations are concentrated and processed on a single server, which is prone to privacy leakage and data security concerns. Besides, it is difficult to solve the high heterogeneity of RS images by simply applying federated learning (FL) algorithms to scene classification. In this article, we formulate a federated remote sensing scene classification (FedSC) framework, and design a customized neural architecture search (CNAS) to achieve both global generality for multiparty collaborative distributed training and local specificity for personalized RS scene customization. The proposed FedSC is generalizable to be implemented in any manually designed networks, network pruning strategies, or NAS methods related to remote sensing scene classification (RSSC). While the designed CNAS not only achieves collaborative distributed training in protecting participant data privacy to obtain a generalized global model, but also provides a customized local model for each participant that is more in line with the characteristics of private RS scenarios. Overall, the proposed FedSC$_{\textrm {CNAS}}$provides a novel federated collaborative training paradigm for RSSC in terms of data privacy, data heterogeneity, and personalized customization. Extensive analytical and comparative experiments on three benchmark RSSC datasets validate the versatility and effectiveness of our methods, and the proposed FedSC$_{\textrm {CNAS}}$exhibits superior competitiveness compared to state-of-the-art methods.

The advancement of Convolutional Neural Networks (CNNs) has enhanced remote sensing scene classification on satellites. However, the increased computational complexity of CNNs will impose a substantial burden on satellite hardware, thereby hindering the practical application. Although various pruning techniques have been developed to reduce the scale of CNNs by assessing the importance of model parameters, these weight-based methods often lead to a degradation in model performance when the original model fails to provide meaningful parameters at under-trained conditions. In this paper, we introduce a novel Layer-interaction Adaptive Pruning (LiAP) method designed to streamline under-trained models. Unlike conventional approaches, LiAP evaluates the importance of neurons based on the distribution of eigenvalues rather than individual parameters. Specifically, this is achieved by projecting the weight matrix of each convolutional layer into the eigenspace, where the eigenvalues are utilized to assess the redundancy of filters. Then each space will be assigned a fined score based on the eigenvalue distribution to provide a robust measure of filter importance. Compared to weight-based pruning methods, LiAP maintains consistent evaluation accuracy between well-trained and under-trained models, as the eigenspace is inherently robust to variations in the weight parameter space. We conducted extensive experiments on VGG-16 and ResNet-50 architectures using datasets such as AID, NWPU-RESISC45, PatternNet, and WHU-RS19 over various data partitions. Notably, our method achieved State-of-the-Art (SOTA) reductions in both FLOPs and parameters for VGG-16 across the aforementioned datasets. These results underscore the efficacy of LiAP in enhancing the efficiency and applicability of CNNs in satellite-based remote sensing tasks.

Pruning techniques have been utilized widely for convolutional neural networks (CNNs) to reduce the computation resources in remote sensing scene image classification. However, conventional pruning techniques are weight-based, which can not balance the pruning ratio and representation ability appropriately. In this paper, we propose a Data Customization-based Multiobjective Optimization Pruning (DCMOP) framework for the pruning in remote sensing scene image classification, which can not only trade-off between pruning ratio and capability for CNNs, but also speed up the evolutionary process for the pruning. We adopt the multiobjective evolutionary algorithms (MOEAs) to search for a trade-off between the pruning ratio and capability for CNNs. However, a big concern of pruning for networks via MOEAs is that the evaluation of sub-networks is time-costing. This originates that the slimmed sub-networks require a lot of retraining operation, which will burden the hardware. In order to alleviate this limitation, we design a Data Customization-based Proxy Mechanism (DCPM) to reduce the size of the input dataset in terms of the structure of the slimmed sub-network to accelerate significantly the evolutionary process for the pruning. According to this, our proposed DCMOP achieves the pruning with higher efficiency and performance by cooperating with MOEAs and DCPM. Experimental results based on four datasets of AID, NWPURESISC45, PatternNet, and WHU-RS19 show that the proposed DCMOP can achieve a balance between model performance and pruning rate, while obviously reducing the time cost of the pruning.

Remote sensing scene classification (RSSC) aims to understand and analyze the semantic information at the scene level with complex geographical properties. Despite the profound success of advanced deep models in automatically capturing hierarchical embedding representations and the gradual dominant trend in RSSC, it still remains a great challenge to precisely focus on targets at variable scales that are considered highly relevant to the corresponding scene and separated from the background. Motivated by this recognition, in this article, we present the gradient-guided multiscale focal attention network (GMFANet) for RSSC to adaptively localize the representative multiscale semantic representation for complex scenes. In particular, a lightweight parameterized hierarchical multiscale attention (HMA) mechanism is proposed, which constitutes the main aim of adaptively enhancing physical detail and high-level semantic information at different layers, rather than regarding each scale set with equivalent insight, while eliminating redundant information inherent in conventional attention mechanisms. Subsequently, a gradient-guided spatial focused attention (GSFA) module is specifically designed to accurately localize critical regions at multiple scales, with the dynamic combination of gradient-activated reference attention map and prediction attention map from supervised information-based learning. In addition, a curriculum-driven dynamic attention fusion (CDAF) strategy is tailored to fuse the spatial attention above from easy to hard for avoiding from poor local optimum and decreasing the early learning ambiguity. Our extensive comparative experiments and ablation analyses implemented on real-world public RSSC datasets indicate that our approach achieves the state-of-the-art performance exactly. The code is available athttps://github.com/bling2beyond/GMFANet.

Convolutional neural networks (CNNs) have been adopted to classify the remote sensing scene image. However, the application of these complicated networks on the satellite platform is difficult because of the limited computation resources. Therefore, we propose an energy-based filter pruning framework (EFPF) to reduce the size of the original model. The energy can be obtained through the eigenvalues of each weight tensor by singular value decomposition (SVD). Specifically, we calculate the energy of each layer by the ratio of eigenvalues that are lower than a specified truncation parameter and then remove filters from the original layer in light of the degree of energy. The EFPF is reliable because SVD techniques can capture the covariance among all filters from the original weight tensor, and therefore, the energy from the eigenvalues can reflect the redundancy of the filters. (i.e., if the distribution of eigenvalues is sharp, then the energy among filters will be lower, and the redundancy will be higher.) Surprisingly, the EFPF can reduce the FLOPs and parameters, as well as improve the top1 accuracy obviously when the VGG-16 and ResNet-50 are adopted to classify the AID, NWPU45, PatternNet, and WHU19 datasets. Additionally, the EFPF can achieve similar pruning results when the original model is fully-trained (converge) and under-trained (In-converge), which means we can save the training computation resources for the original model.

In this paper, we propose a new change detection method of multi-temporal synthetic aperture radar (SAR) images. Due to the ability of extracting key feature of images and robustness to noise, stacked denoising auto encoder (SDAE) has been widely used in remote sensing. However, the single SDAE stills has some limitations to handle with the speckle noise of SAR images. Therefore, we propose a new structure Bi-hierarchical SDAE for feature extraction. The first level of SDAE denoises the original image and reconstructs the difference map, and the second level extracts the superpixel-based difference features for classification. Besides, Bayesian optimization effectively improves the classification performance of feature classifier. The experimental results of the datasets in this paper show that the Bi-hierarchical SDAE and Bayesian optimization framework has high accuracy and proves its effectiveness.