Aquaculture areas extraction based on remote sensing( RS) is vital for coastal resource utilization and production management. However, studies focusing on coastal aquaculture areas remain limited due to spectral and spatial similarities between aquaculture areas and evaporation ponds in saltpans. To tackle this issue, a Local Fusion Network(LAFNet) for extracting coastal aquaculture around saltpans is proposed. To capture horizontal, vertical contextual relations and global semantic information, Local Window-Multi Head self- Attention (LW-MSA) containing three specialized attention layers is designed. Moreover, we designed the Multi-level Upsampling(MLU) decoder to allow model to retain more information and details of higher-level features from the upsampling pyramid. Our datasets are derived from the multispectral Sentinel-2 imagery satellite data around the Changlu Hangu, Huaibei and Yinggehai salt fields and introduced for the first time following recommendations from domain experts. Experimental results indicate that LAFNet achieves superior performance compared with other state-of-the-art methods on test datasets. Specifically, it attains an intersection over union (IoU) of 88.80%, surpassing the highest scores of other experimental methods by 0.53 percentage points. Furthermore, T-SNE results prove that the proposed method can effectively distinguish between saltpans and aquaculture areas.

Road extraction from remote sensing images has attracted widespread attention of researchers due to its crucial role in the fields of autopilot, urban planning, navigation, and other fields. However, the task becomes challenging as the roads in remote sensing images are easily occluded by obstacles such as shadows, buildings and trees. In this letter, a cascade fusion network for road extraction (CFRNet) in remote sensing images is proposed. Considering the lightweight characteristics of MobileNet block (MbBlock), it is used as the feature extraction module of the backbone network. To enable CFRNet to generate and fuse more features at multiscale, we design several cascade stages. Each stage includes a sub-backbone for feature extraction and a triple-level adaptive feature fusion (TAFF) module for feature fusion. This structure can more deeply and effectively fuse multiscale features with most of the parameters in the entire backbone. The experimental results demonstrate that the proposed CFRNet significantly outperforms other state-of-the-art methods on the publicly available Istanbul City Road dataset and DeepGlobe Road dataset. Specifically, it achieves an intersection over union (IoU) of 89.76%, reflecting a 5.3% improvement on the Istanbul dataset, and 67.22% with a 0.98% enhancement on the DeepGlobe Road dataset. Our code is available athttps://github.com/XYQ1517/CFRNet.

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.

The illegal misuse of non-cooperative UAVs poses huge threats to society and life safety. Infrared imaging is reliable to monitor unmanned aerial vehicles (UAVs) and the anti-UAVs technology via infrared images has attracted more and more attention. In order to provide sufficient time for follow-up, UAVs are acquired at long distances, usually exhibiting the features of weak and small. Furthermore, infrared images are usually with low signal-to-clutter ratio (SCR). These factors make the correct detection of UAVs a challenge. Existing methods do not fully exploit the phenomenon that the UAVs are easily isolated, resulting in unsatisfactory detection results. For alleviating the issue, a novel detection method via isolation Forest (iForest) is proposed. In the proposed method, the multi-direction couple-order derivative properties are firstly analyzed, which enlarges the feature difference between UAVs and background. Then, a global iForest is constructed, which takes full advantage of the phenomenon that UAVs are susceptible to being isolated. As far as we know, this is the first time that iForest is constructed in infrared small targets detection field. Furthermore, a local iForest is created, which further eliminates the residual false alarms of the result of global iForest. Experiments on nine sequences demonstrate the performance of the proposed method, which is capable of detecting various UAVs under diverse background.

With the increasing availability of multitemporal hyperspectral imagery, hyperspectral change detection under heterogeneous backgrounds is a challenging task. Due to the complexity of background features, traditional change detection algorithms in the spectral domain cannot effectively detect changed features. A novel method using multiple morphological profiles (MMPs) is proposed for hyperspectral change detection to make full use of spatial information. In the designed framework, first, the max-tree/min-tree strategy is applied to extract different attributes of multitemporal hyperspectral images (HSIs), i.e., area attribute and height attribute. Second, a spectral angle weighted-based local absolute distance (SALA) method is designed to reconstruct the discriminative spectral domain. Then, the absolute distance (AD) is adopted to extract changes in constructed feature domain. Finally, a change map is obtained by guided filtering. Experiments conducted on four real hyperspectral datasets demonstrate that the proposed detector achieves better detection performance.

Existing infrared small-target detection methods tend to perform unsatisfactorily when encountering complex scenes, mainly due to the following: 1) the infrared image itself has a low signal-to-noise ratio (SNR) and insufficient detailed/texture knowledge; 2) spatial and structural information is not fully excavated. To avoid these difficulties, an effective method based on three-order tensor creation and Tucker decomposition (TCTD) is proposed, which detects targets with various brightness, spatial sizes, and intensities. In the proposed TCTD, multiple morphological profiles, i.e., diverse attributes and different shapes of trees, are designed to create three-order tensors, which can exploit more spatial and structural information to make up for lacking detailed/texture knowledge. Then, Tucker decomposition is employed, which is capable of estimating and eliminating the major principal components (i.e., most of the background) from three dimensions. Thus, targets can be preserved on the remaining minor principal components. Image contrast is further enhanced by fusing the detection maps of multiple morphological profiles and several groups with discontinuous pruning values. Extensive experiments validated on two synthetic data and six real data sets demonstrate the effectiveness and robustness of the proposed TCTD.

The key to hyperspectral anomaly detection is to effectively distinguish anomalies from the background, especially in the case that background is complex and anomalies are weak. Hyperspectral imagery (HSI) as an image–spectrum merging cube data can be intrinsically represented as a third-order tensor that integrates spectral information and spatial information. In this article, a prior-based tensor approximation (PTA) is proposed for hyperspectral anomaly detection, in which HSI is decomposed into a background tensor and an anomaly tensor. In the background tensor, a low-rank prior is incorporated into spectral dimension by truncated nuclear norm regularization, and a piecewise-smooth prior on spatial dimension can be embedded by a linear total variation-norm regularization. For anomaly tensor, it is unfolded along spectral dimension coupled with spatial group sparse prior that can be represented by the${l}_{2,1}$-norm regularization. In the designed method, all the priors are integrated into a unified convex framework, and the anomalies can be finally determined by the anomaly tensor. Experimental results validated on several real hyperspectral data sets demonstrate that the proposed algorithm outperforms some state-of-the-art anomaly detection methods.

Robust infrared small-target detection has always been a research hotspot in target search and tracking systems. However, the image itself has low signal-to-noise ratio (SNR), and the targets usually lack detailed/texture information. In addition, the background is complex and diverse. All the above factors make it easy for the targets to be submerged. In this paper, a novel method is proposed based on a three-order creation and the Tucker decomposition. First, the morphological profiles (i.e., area attribute and height attribute of max-tree) are applied to create a three-order tensor, which compensates for the lack of detailed information by supplementing spatial information in infrared images. Then, the Tucker decomposition is employed on the created tensor, in which most of the background can be estimated and eliminated from three dimensions. Finally, the target is detected on the remaining and the results of diverse morphological profiles are fused, which further enhances the target information. Experimental results demonstrate the effectiveness of the proposed method.

Recently, the low-rank and sparse decomposition model (LSDM) has been used for anomaly detection in hyperspectral imagery. The traditional LSDM assumes that the sparse component where anomalies and noise reside can be modeled by a single distribution which often potentially confuses weak anomalies and noise. Actually, a single distribution cannot accurately describe different noise characteristics. In this article, a combination of a mixture noise model with low-rank background may more accurately characterize complex distribution. A modified LSDM, by modeling the sparse component as a mixture of Gaussian (MoG), is employed for hyperspectral anomaly detection. In the proposed framework, the variational Bayes (VB) algorithm is applied to infer a posterior MoG model. Once the noise model is determined, anomalies can be easily separated from the noise components. Furthermore, a simple but effective detector based on the Manhattan distance is incorporated for anomaly detection under complex distribution. The experimental results demonstrate that the proposed algorithm outperforms the classic Reed-Xiaoli (RX), and the state-of-the-art detectors, such as robust principal component analysis (RPCA) with RX.

Tensor-based fusion that couples the high spatial resolution of a multispectral image (MSI) to the high spectral resolution of a hyperspectral image (HSI) is considered. The fusion problem is first formulated mathematically as a convex optimization of a tensor trace norm imposing low-rank spatially as well as spectrally, with an alternating-directions optimization featuring linearization providing the solution. Although prior tensor-based fusion approaches typically resort to tensor decomposition, the proposed algorithm exploits ideas from the field of tensor completion to directly impose a low-rank property spatially and spectrally while avoiding the computationally complex patch clustering and dictionary learning common to competing fusion techniques. Additionally, small modifications to the basic optimization permit a fusion process robust to missing hyperspectral values such as those that can result from dead stripes in real hyperspectral sensors. The experimental evaluations on both synthetic imagery as well as real imagery demonstrate that the resulting low-rank tensor-approximation (LRTA) fusion algorithm preserves both spatial details and texture, yielding significantly improved image quality when compared to other state-of-the-art fusion methods as well as effective restoration under conditions of missing stripes within the HSI.

Infrared small-target detection under heterogeneous background, as a challenging task, plays an important role in many applications. In practice, there are not only bright targets but also dim targets, e.g., rescue aircraft and vehicles in the forest fire scene. Considering that most existing infrared small-target detection methods are merely aimed at bright targets, a novel method using multiple morphological profiles (MMP) is proposed, which can detect various types of targets whose brightness varies greatly. In the designed morphological feature extraction, different attributes, i.e., area attribute and height attribute, are applied to extract spatial size and contrast information of small-target in the max-tree and min-tree, respectively. Furthermore, discontinuous pruning values are further utilized for different attributes, and a designed fusion strategy of different pruning values results in more robust detection performance. Experimental results validated on two synthetic data and six real data sets demonstrate that the proposed MMP can not only detect a variety of brightness of targets and different types of targets and kinds of spatial sizes of targets but also further improve the contrast between targets and background, and the background clutter is significantly suppressed.

Target detection in hyperspectral images (HSI) is an important technique and many target detection algorithms have been developed in recent years. The most widely detection algorithms by the original spectral characteristics may lack the ability of target signal enhancement and background suppression. This paper presents an efficient algorithm for detecting hyperspectral targets based on fractional Fourier transform (FrFT). Firstly, fractional Fourier transform primary search is used as preprocessing to obtain the better intermediate domain features with complementary characteristics between the original reflection spectrum and the Fourier transform domain. Secondly, fractional Fourier transform secondary search and constrained energy minimization (FrFT-CEM) was adopted to find an optimal fractional order to distinguish the target from the background. The proposed method has been proved to be superior in two real hyperspectral data sets.

Infrared (IR) small target detection of low signal-to-noise ratio (SNR) is a very meaningful and challenging subject in detecting and tracking system. Therefore, an effective method is proposed in this paper. First of all, a morphological feature extraction method is used to reconstruct a new image that the small target is disappeared, then the original image and the reconstruction image are made difference as preprocessed image. In this way, important spatial information can be extracted well in IR image. Then, a low-rank and sparse decomposition method is employed to obtain the background image and the target image respectively, the target separation can be enhanced and the background clutter can be suppressed simultaneously. Finally, the obtained target image is segmented by a simple adaptive segmentation method. The experimental results indicate that the proposed method is of great improvement compared with several existing methods, what's more, it can achieve the highest SNR among these methods.