The field of remote sensing change detection (RSCD) has seen significant advancements recently, focusing on the precise identification and analysis of temporal changes in remote sensing images. Existing deep learning-based RSCD methods primarily rely on concatenation or subtraction to integrate features of bi-temporal images and reconstruct change features through a feature pyramid network decoding architecture. However, these methods face challenges related to inadequate spatio-temporal change representation and insufficient aggregation of multilevel semantic information, resulting in pseudo-changes and poor completeness of detected change objects. In this paper, we propose an innovative RSCD framework via spatio-temporal feature fusion and guide aggregation (STFF-GA) to address the aforementioned challenges. The architecture of this network comprises two key components: the STFF module and the GA module. The STFF module is designed as a low-parameter and low-computation structure, effectively enhancing the representation of spatio-temporal change information through split, interaction, and fusion strategies. The GA module uses deep feature guidance (DFG) mapping as prior information to guide the aggregation of multi-level semantic information, thereby correcting the positional information of change objects and filtering out pseudo-changes and other noise interference. Additionally, it utilizes convolution kernels of various scales to extract fine-grained features, facilitating the complete reconstruction of change objects. Extensive experiments conducted on three benchmark change detection datasets demonstrate that the proposed STFF-GA consistently outperforms other state-of-the-art detectors. The code is available at https://github.com/NjustHGWei/STFF-GA.

Winter wheat is a significant global food crop, and it is crucial to monitor its distribution for better agricultural management, land planning, and environmental sustainability. However, the distribution style of winter wheat planting fields is not consistent due to different terrain conditions. In mountainous areas, winter wheat planting units are smaller in size and fragmented in distribution compared to plain areas. Unfortunately, most crop-mapping research based on deep learning ignores the impact of topographic relief on crop distribution and struggles to handle hilly areas effectively. In this paper, we propose a cross-modal segmentation network for winter wheat mapping in complex terrain using remote-sensing multi-temporal images and DEM data. First, we propose a diverse receptive fusion (DRF) module, which applies a deformable receptive field to optical images during the feature fusion process, allowing it to match winter wheat plots of varying scales and a fixed receptive field to the DEM to extract evaluation features at a consistent scale. Second, we developed a distributed weight attention (DWA) module, which can enhance the feature intensity of winter wheat, thereby reducing the omission rate of planting areas, especially for the small-sized regions in hilly terrain. Furthermore, to demonstrate the performance of our model, we conducted extensive experiments and ablation studies on a large-scale dataset in Lanling county, Shandong province, China. Our results show that our proposed CM-Net is effective in mapping winter wheat in complex terrain.

Change detection (CD) in remote sensing images is a critical task that has achieved significant success by deep learning. Current networks often employ pixel-based differencing, proportion, classification-based, or feature concatenation methods to represent changes of interest. However, these methods fail to effectively detect the desired changes, as they are highly sensitive to factors such as atmospheric conditions, lighting variations, and phenological variations, resulting in detection errors. Inspired by the Transformer structure, we adopt a cross-attention mechanism to more robustly extract feature differences between bitemporal images. The motivation of the method is based on the assumption that if there is no change between image pairs, the semantic features from one temporal image can well be represented by the semantic features from another temporal image. Conversely if there is a change, there are significant reconstruction errors. Therefore, a Cross Swin Transformer based Siamese U-shaped network namely CSTSUNet is proposed for remote sensing change detection. CSTSUnet consists of encoder, difference feature extraction, and decoder. The encoder is based on a hierarchical Resnet with the Siamese U-net structure, allowing parallel processing of bitemporal images and extraction of multi-scale features. The difference feature extraction consists of four difference feature extraction modules that compute difference feature at multiple scales. In this module, Cross Swin Transformer is employed in each difference feature extraction module to communicate the information of bitemporal images. The decoder takes in the multi-scale difference features as input, injects details and boundaries iteratively level by level, and makes the change map more and more accurate. We conduct experiments on three public datasets, and the experimental results demonstrate that the proposed CSTSUNet outperforms other state-of-the-art methods in terms of both qualitative and quantitative analyses. Our code is available at https://github.com/l7170/CSTSUNet.git.

Fine-grained classification is of significant importance in the field of remote sensing. However, obtaining valuable and rare target images is often a challenging task, giving rise to the few-shot fine-grained classification problem. In response to this challenge, various meta-learning approaches have been introduced, with the feature map reconstruction network emerging as a prominent method. Targets in remote sensing images exhibit arbitrary orientation, substantial inter-class similarity and intra-class diversity. Nevertheless, the conventional feature map reconstruction network exhibits subpar performance due to its inability to handle rotational variations. Moreover, it only reconstructs features from a single channel dimension of support features, neglecting the interplay between different dimensions and resulting in inaccurate reconstruction errors. To overcome the challenges of imprecise rotational variation features for reconstruction and inaccurate reconstruction errors, we propose a rotation-invariant feature map complementary reconstruction network (RIFCRN). The RIFCRN involves several key innovations. First, we introduce a novel rotation-invariant module (RIM) based on active rotating filters and oriented response pooling, enabling the extraction of rotation-invariant features for reconstruction. This modification enhances the suitability of the feature map reconstruction network for the few-shot fine-grained classification problem. Second, we put forward a novel feature map complementary reconstruction (CPR) method that calculates the complementary reconstruction errors (CRE) which effectively captures relationships among different feature map dimensions and results in more accurate reconstruction errors. Finally, extensive experiments have been conducted to validate the effectiveness of the proposed RIFCRN in addressing the few-shot fine-grained classification problem. The code will be available at https://github.com/liyangfan0/RIFCRN.

With the advancement of high-resolution remote sensing satellites, the tracking of high-value targets such as planes and ships within satellite videos has become imperative. In recent years, several object tracking methods designed for satellite videos based on correlation filters have been proposed. However, these traditional correlation filters typically identify the location with the highest response value on the response map as the target position. In the context of satellite videos, where targets are often very small and surrounded by numerous similar objects, depending only on response values to determine the target’s location can easily lead to interference from nearby objects, resulting in tracking failures. Moreover, targets frequently encounter occlusion during their motion, further complicating tracking tasks due to the absence of distinctive target appearance features and leading to the issue of model drift. To address these challenges, we propose a novel distractor–occlusion-aware correlation particle filters. Instead of determining the position with the maximum response value, our method initially selects the top $k$ response values from the response map, creating a pool of candidates. Subsequently, we introduce an innovative quality score, rooted in motion information and response scores related to the target, for each candidate. Finally, these quality scores are employed to filter the most suitable candidate. This novel distractor-aware module effectively equips our tracking method to perform well in the presence of distractors. Additionally, to handle occlusion, we integrate the occlusion-aware module into the correlation particle filters, improving the tracker’s performance in occluded scenarios. To ensure the effective collaboration of the distractor-aware module and the occlusion-aware module, we introduce the dual Kalman Filter method. Our comprehensive experiments conducted on the SatSOT datasets conclusively demonstrate the effectiveness and superiority of our proposed tracking method. The code will be available at https://github.com/liyangfan0/DOCPF .

Clouds in optical remote sensing images significantly limit image utilization. Traditional cloud removal methods using single or multi-temporal data sources struggle to ensure reliable reconstruction for thick cloud (TKC) areas. Synthetic aperture radar (SAR) images are increasingly used to recover information obscured by clouds, but their performance in cloud-obscured regions is unstable. Therefore, an adaptive cloud removal network for remote sensing images, named DG2-TCR, is proposed based on SAR-driven dual-flow fusion guidance (DFG). DG2-TCR uses SAR and optical remote sensing imagery (ORSI) to construct DFG, including local spatial-spectral feature reconstruction (LSSFR) flow and global texture feature compensation (GTFC). LSSFR, driven by ORSI and SAR, efficiently extracts useful features in noncloud areas and focuses on local information reconstruction using the designed spatial-spectral features inference reconstruction block (SSIRB). Based on SAR images, GTFC guides the compensation of global texture information. DFG can adaptively extract features and reconstruct missing information from local and global scales. The public SEN12MS-CR-TS dataset is divided into four sub-datasets with different coverage to evaluate the recovering capability in varying clouds. Experiments show that the peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), root mean square error (RMSE), Fréchet inception distance (FID), and normalized cross correlation (NCC) indicator values on four sub-datasets and the SIMLE-CR datasets are better than the seven comparison methods. Furthermore, the ablation experiments show that the generalization and robustness of this proposed method on images with different cloud coverage are better than other comparison methods. Therefore, DG2-TCR can reliably recover information on cloud occlusions with various coverage and thickness, which is significant for cloud removal in practical applications.