Time-series images with rich spatiotemporal features contain comprehensive and accurate context information for image segmentation. Due to the variability of time-series images, a random offset phenomenon may occur in targets, interfering with the continuity of temporal features. Although windowed attention mechanisms are adopted to capture the complete image information, they are prone to triggering the edge-jagged phenomenon. To address the above issues, this article presents a Swin transformer with spatiotemporal feature correction (SwinTSFC) for the semantic segmentation of time-series images. A convolutional long-short-term memory (ConvLSTM) module with dynamic correction is proposed to adjust the target deviation of temporal data by capturing the offset relationship among sequences. It learns image semantic association and maintains object alignment among dynamic data. A global-to-local learning strategy is adopted to extract spatial features. Swin transformer blocks are adopted to capture the long-range dependencies of images by strengthening interaction capabilities among windows and to improve the overall recognition ability of SwinTSFC. Self-calibrated convolution (SCConv) adaptively extracts fine-grained information to optimize edge continuity features and overcome the phenomenon of edge-jagged. The superiority of the SwinTSFC to state-of-the-art algorithms is demonstrated via experimentation. The code is available at: https://github.com/fjc1575/Marine-Aquaculture/tree/main/SwinTSFC

The prevailing reliance on statistical yearbooks for current aquaculture yield data is problematic due to their inherent delays and biases, which significantly impede effective aquaculture management and optimal resource allocation. For the first time, this paper proposes a sparse broad learning system (SBLS) for algae and shellfish yield based on multivariate remote sensing data, utilizing extensive marine data analysis to enhance interpretability and accuracy. Firstly, the extracted shellfish and algae aquaculture area in Dalian from 2013-2021 and obtained monthly data on six regional environmental variables - sea surface temperature, chlorophyll concentration, organic carbon, turbidity, wind speed, and precipitation. Next, the correlation between these variables and yield is assessed using Spearman’s and Kendall’s correlation coefficients and distance coefficients, filtering the most relevant months and analyzing the biological growth characteristics of algae and shellfish. Finally, separate prediction models for shellfish and algae annual yields per unit area are constructed using the SBLS method with multiple windows, refining yield categories to obtain the density distribution of Dalian’s 2022 annual yield. The results show highly accurate predictions, with a mean absolute percentage error under 10%. In 2022, the total algal yield in the aquaculture area is 494044.58 tons, and the total shellfish yield is 391691.26 tons. This study represents a significant improvement over traditional methods relying on outdated yearbooks and can serve as an effective monitoring tool for aquaculture management in complex coastal environments.

Supervised deep learning neural networks provide an accurate method for synthetic aperture radar (SAR) image semantic segmentation, but require training that relies heavily on high-quality labeled data. Meanwhile, existing unsupervised methods to obtain high-precision segmentation results are challenging in complex scenarios. Moreover, focusing on the long-term serial monitoring task of marine aquaculture, a significant amount of unlabeled SAR data remains underutilized. To address these problems, this paper proposes an unsupervised transformer with generative label optimization (UTGLO) for marine aquaculture segmentation, which utilizes unsupervised training of generative pseudo-label optimization to improve segmentation performance. First, a dual network architecture based on pre-trained self-supervised transformer (SST) and semantic segmentation transformer (SegT) is proposed to improve the quality of pseudo-labels using the dual network iterative update mechanism. Second, to learn the global semantics of targets and reduce the interference between them and the background, a global optimal pseudo-label generator is created to obtain the target’s discriminative features, compute the similar semantics globally, and generate the initial pseudo-labels. In addition, to alleviate the network performance degradation problem caused by the coarseness of pseudo-labels, the fine-grained feature discrimination and complement module is designed to optimize the target boundary features and continuity features of pseudo-labels. The superiority of the proposed method over state-of-the-art algorithms is demonstrated via experimentation on GF-3, Radarsat-2, and Sentinel-1 SAR datasets and three different aquaculture regions. Code and data are available in the GitHub repository https: //github.com/fjc1575/Marine-Aquaculture/tree/main/UTGLO.

Marine floating raft aquaculture is an important development of marine natural resources, therefore it is necessary to plan the floating raft aquaculture area reasonably. Polarimetric synthetic aperture radar (PolSAR) can record polarization information of scattered echoes from floating rafts and has become an important means of monitoring floating raft aquaculture areas. At present, mainstream semantic segmentation architectures include convolutional neural networks (CNN) and Transformers. There are many methods that can be used for the segmentation task of PolSAR floating rafts, but they are difficult to combine both local and global features of PolSAR data simultaneously. Therefore, this paper introduces a method that integrates CSwin Transformer and ResNet into a segmentation network (CRSegNet). It effectively extracts global and local information of aquaculture rafts, not only grasping the relative position relationship between background and rafts, but also smoothly segmenting rafts from each other. Therefore, it achieved better results than mainstream models.

Fully convolutional encoder-decoder networks have been developed for the segmentation of sensing synthetic aperture radar (SAR) images. A recent one called the multiscaled attention U-net with dilated convolution and offset convolution (MDOAU-net) has been proposed for SAR image segmentation in aquaculture raft monitoring. Despite its excellent performance, its hyperparameters have to be handcrafted based on human experience, consuming a significant amount of time to tune. In this letter, a swarm intelligence algorithm is leveraged to optimize the hyperparameters of fully convolutional encoder-decoder networks (particularly MDOAU-net), including their kernel size, dilation rate, learning rate, batch size, and activation function indicator. Based on segmentation performance, early-stop termination criteria are introduced into a particle swarm optimization (PSO) algorithm to avoid overusing computing resources to train the networks. Specifically, the hyperparameters are optimized using the PSO algorithm with early-stop termination criteria. Experimental results show that the segmentation accuracy of the proposed method reaches 91.49%, which statistically outperforms other methods.

The application of deep learning algorithms to oil spill identification in synthetic aperture radar (SAR) remote sensing images has enabled substantial progress. However, the end-to-end learning approach of deep learning has inefficient interpretability, making it difficult to ensure the reliability of oil spill identification tasks. In addition, how to effectively apply the physical information of SAR images to make intelligent decoding methods more interpretable is the key to understanding and evaluating its oil spill identification results. To address these issues, this paper proposes a multi-physical interpretable deep learning network (MIDLN) for oil spill identification based on SAR images. MIDLN defines a dual neural network consisting of a multi-physical deep convolutional neural network and an adaptive grad-weight selection class activation explainer. The explainer adaptively selects the most important interpretation weights for the category, mitigating visual background noise in the explainer results. The high-quality results of the explainer are utilized to provide feedback signals to the network, and the learning strategy of the network is adjusted to structure a deep learning framework with interpretable feedback. Meanwhile, to enhance the physical interpretability of the network, this study employs a scribble-style interactive sampling to streamline the oil spill physical information extraction process. The effectiveness of two novel oil spill physical features is validated through the combination of pixel-level oil-water feature analysis and deep learning methods. In addition, a multi-physical feature extraction head is designed for different features to enhance the uniqueness of physical information and reduce the redundant interference of multiple features. The superiority of the proposed method over existing algorithms is demonstrated by experiments on a large number of Sentinel-1 oil spill datasets. The code for this work will be made available at https://github.com/fjc1575/Marine-Oil-Spill/tree/main/MIDLN for the sake of reproducibility.

The rapid development of the marine aquaculture industry has brought about a series of environmental problems that need to be monitored and planned. There is abundant marine aquaculture data obtained through synthetic aperture radar (SAR) remote sensing over a long period. With a large amount of unlabeled data, self-supervised learning can describe the feature representation of targets. However, when self-supervised learning meets big data, it often leads to semantic information loss, such as inter-class misjudgment and intra-class discontinuity. To address this issue, this paper proposes a self-supervised transformer with feature fusion (STFF) for the semantic segmentation of SAR images in marine aquaculture monitoring. STFF consists mainly of a self-attention encoding module with a hybrid loss function and a semantic segmentation decoding module with feature fusion. For encoding, the transformer is pretrained via self-supervised learning based on a hybrid loss function to enrich local, global and edge information for dealing with semantic information loss and data imbalance in whole-scene SAR images. For decoding, the features extracted from transformer blocks are fused to enhance semantic characteristics, improve the intra-class continuity of segmentation, and reduce the occurrence of inter-class misjudgment. The superiority of the proposed method to state-of-the-art algorithms is demonstrated via experimentation on GaoFen-3 and Radarsat-2 SAR datasets. The code has been available at https://github.com/fjc1575/Marine-Aquaculture/tree/main/STFF-code for the sake of reproducibility.

Offshore aquaculture raft information extraction from synthetic aperture radar (SAR) images is essential for large-scale marine resource exploitation and protection. In this letter, a deep learning model called multi-scaled attention U-net with dilated convolution and offset convolution (MDOAU-net) is proposed for aquaculture raft monitoring via SAR image segmentation. The U-net backbone and attention gate of the Attention U-net are used in the MDOAU-net model. In addition, the MDOAU-net model consists of three distinctive parts. First, a multi-scale feature-fusion block is adopted in its input to extract features from raw images. Moreover, adapted from the Attention U-net for SAR image segmentation, fewer channels are used in each convolution layer of the MDOAU-net to match latent features in SAR images. Furthermore, nine dilated convolution blocks are adopted in the encoder–decoder structure to extract semantic features in the presence of speckle noises. In addition, offset convolution blocks are developed to convert spatial information into channel information for the precise segmentation of blurry boundaries. Four skip connections of the U-net backbone are replaced by four offset convolution blocks. Experimental results are elaborated to demonstrate the superior performance of the MDOAU-net model to seven existing methods in terms of overall accuracy (OA) and number of parameters.

Marine aquaculture is an important natural resource exploration that requires rational planning to avoid environmental damage. Synthetic Aperture Radar (SAR) images are essential in remote sensing to monitor the marine ecological environment. Unsupervised methods provide an adequate solution to avoid the cost of training sample collection. However, unsupervised methods often struggle to discover effective semantic information and incrementally utilize newly acquired data. To address these challenges, this article presents an incremental double unsupervised deep learning (IDUDL) model, which is specially designed to characterize unlabeled marine aquaculture and achieve the results semantically. Based on the idea of alternately generating and updating pseudo-labels, the proposed IDUDL model defines the double neural networks comprised of the feature extraction network (FEN) and the fully convolutional semantic segmentation network (FCSSN). A patch estimation (PE) is proposed to generate pseudo-labels with aquaculture semantic information based on the features extracted by the FEN network. Then, the aquaculture extraction results are obtained by the FCSSN with generated pseudo-labels. After that, the pseudo-labels and extraction results are updated in turns until the pseudo-labels are stable. In addition, due to the unique structure of double neural networks, newly acquired marine aquaculture SAR images can also be added to the pre-trained FCSSN and followed pseudo-labels updated based on the FEN and PE part, which can achieve new data incremental learning without retrained the whole IDUDL model. Experiments demonstrate the effectiveness of the proposed approach based on two different ways of the marine aquaculture including raft and cage types, which consist of GaoFen-3 (GF-3) and RADARSAT-2 SAR images from the Dalian and Ningde areas, respectively. The incremental experiments are also designed to verify the generalization of the IDUDL model for new obtained marine aquaculture SAR images. The code of this work will be available at https://github. com/fjc1575/Marine-Aquaculture/tree/main/IDUDL for the sake of reproducibility.

This paper aims at the monitoring of birds and their ecological environment in the island and coastal wetland ecosystems. A new approach of island bird detection is proposed based on the Faster R-CNN (Regions with Convolutional Neural Networks) model under multiple backgrounds. It includes feature extraction, region proposal, bounding box regression, classification into the whole neural network structure. This key technology can automatically achieve automatic bird species identification and quantitative statistics in the faster computation speed. The details of constructing Faster R-CNN are described. In the end, many actual images are utilized to demonstrate the effectiveness of the proposed models.

To improve the retrieval result obtained from a pairwise dissimilarity, many variants of diffusion process have been applied in visual re-ranking. In the framework of diffusion process, various contextual similarities can be obtained by solving an optimization problem, and the objective function consists of a smoothness constraint and a fitting constraint. And many improvements on the smoothness constraint have been made to reveal the underlying manifold structure. However, little attention has been paid to the fitting constraint, and how to build an effective fitting constraint still remains unclear. In this article, by deeply analyzing the role of fitting constraint, we firstly propose a novel variant of diffusion process named Hybrid Regularization of Diffusion Process (HyRDP). In HyRDP, we introduce a hybrid regularization framework containing a two-part fitting constraint, and the contextual dissimilarities can be learned from either a closed-form solution or an iterative solution. Furthermore, this article indicates that the basic idea of HyRDP is closely related to the mechanism behind Generalized Mean First-passage Time (GMFPT). GMFPT denotes the mean time-steps for the state transition from one state to any one in the given state set, and is firstly introduced as the contextual dissimilarity in this article. Finally, based on the semi-supervised learning framework, an iterative re-ranking process is developed. With this approach, the relevant objects on the manifold can be iteratively retrieved and labeled within finite iterations. The proposed algorithms are validated on various challenging databases, and the experimental performances demonstrate that retrieval results obtained from different types of measures can be effectively improved by using our methods.

In recent years, deep learning neural networks have seen wide adoption in synthetic aperture radar (SAR) image applications. Comparatively, convenient and fast neural network models have attracted less attention. In this letter, a novel incremental Wishart broad learning system (IWBLS) is specifically designed to achieve polarimetric SAR (PolSAR) image classification for the first time. IWBLS can effectively transfer essential Wishart distribution and other types of polarimetric decomposition and spatial features to establish mapped feature and enhancement nodes in one layer without deep learning structures, which means that massive layer-by-layer training consumption can be decreased significantly. Incremental learning concept is incorporated to deal with new PolSAR images or additional features, thereby avoiding retraining entire neural networks, whose properties are very appropriate for long-term monitoring or stepwise feature integration. The experiments substantiate advantages of PolSAR image classification based on our proposed IWBLS algorithm.

The classification of polarimetric synthetic aperture radar (PolSAR) image is of crucial significance for SAR applications. In this letter, a superpixel restrained deep neural network with multiple decisions (SRDNN-MDs) is proposed for PolSAR image classification, which not only extracts effective superpixel spatial features and degrades the influence of speckle noises but also deals with the limited training samples. First, the polarimetric features of coherency matrix and Yamaguchi decomposition are extracted as initial features, and superpixel segmentation is conducted on the Pauli color-coded image to acquire the superpixel averaged features. Then, an SRDNN based on sparse autoencoders is proposed to capture superpixel correlative features and reduce speckle noises. After that, MDs, including nonlocal decision and local decision, are developed to select credible testing samples. Finally, our deep network is updated by the extended training set to yield the final classification map. Experimental results demonstrate that the proposed SRDNN-MD yields higher accuracies compared with other related approaches, which indicate that the proposed method is able to capture superpixel correlative information and adds the information of unlabeled samples to improve the classification performance.

Land cover classification using polarimetric synthetic aperture radar (PolSAR) images is an important tool for remote sensing analysis. In view that PolSAR image effective interpretation is commonly affected by the absence of discriminative features and the presence of speckle noises, this letter proposes an adaptive nonlocal stacked sparse autoencoder (ANSSAE) to achieve PolSAR image classification. It extracts the adaptive nonlocal spatial information by adaptively calculating weighted average values of each pixel from nonlocal regions, which can reduce the influence of speckle noises and retain edge details. In the first layer of the ANSSAE, the adaptive nonlocal spatial information is introduced into the objective function to obtain the robust feature representation, whose effects would transfer to the rest of layers. Therefore, the ANSSAE can automatically capture spatial-related, robust, and distinguishable features, which can suppress speckle noises and gain accurate classification results. Experimental results on two real PolSAR images demonstrate that the proposed approach can significantly improve the classification accuracy.

This paper presents a two-phase fuzzy clustering algorithm based on neurodynamic optimization with its application for polarimetric synthetic aperture radar (PolSAR) remote sensing image segmentation. The two-phase clustering algorithm starts with the linear-assignment initialization phase with the least similar cluster representatives to remedy the inconsistency of clustering results from random initialization and is, then, followed with multiple-kernel fuzzy C-means clustering. By incorporating multiple kernels in the clustering framework, various features are incorporated cohesively. A winner-takes-all neural network is employed to acquire the highest kernel weights and associated cluster centers and membership matrices, which enables better characterization and adaptability in each individual cluster. Simulation results for UCI benchmark datasets and PolSAR remote sensing image segmentation are reported to substantiate the effectiveness and the superiority of the proposed clustering algorithm.

Synthetic aperture radar (SAR) image classification is a fundamental process for SAR image understanding and interpretation. With the advancement of imaging techniques, it permits to produce higher resolution SAR data and extend data amount. Therefore, intelligent algorithms for high-resolution SAR image classification are demanded. Inspired by deep learning technology, an end-to-end classification model from the original SAR image to final classification map is developed to automatically extract features and conduct classification, which is named deep recurrent encoding neural networks (DRENNs). In our proposed framework, a spatial feature learning network based on long-short-term memory (LSTM) is developed to extract contextual dependencies of SAR images, where 2-D image patches are transformed into 1-D sequences and imported into LSTM to learn the latent spatial correlations. After LSTM, nonnegative and Fisher constrained autoencoders (NFCAEs) are proposed to improve the discrimination of features and conduct final classification, where nonnegative constraint and Fisher constraint are developed in each autoencoder to restrict the training of the network. The whole DRENN not only combines the spatial feature learning power of LSTM but also utilizes the discriminative representation ability of our NFCAE to improve the classification performance. The experimental results tested on three SAR images demonstrate that the proposed DRENN is able to learn effective feature representations from SAR images and produce competitive classification accuracies to other related approaches.

Marine Reclamation detection can realize the rational exploitation and effective utilization of coastal resources. Gaofen 3 (GF-3), as a new launched full polarimetric SAR satellite, can be applied into many fields of oceanic environment and marine sea-area use. In this paper, GF-3 SAR imagery are adopted for marine reclamation detection. Different kinds of reclamation in SAR imagery are analyzed compared with GF-2 multispectral data. A coastline detection algorithm is proposed to coastline extraction based on GF-3 imagery. Jinzhou reclamation experiment demonstrates the effectiveness of GF-3 SAR data.

Marine reclamation plays an increasingly important role in expanding living space, which should be monitored to ensure legitimate development. In this paper, a patch-based recurrent neural network is developed for change detection of marine reclamation. To capture spatial difference of image patches in two images, a patch-based recurrent neural network is proposed to extract features, where patches from two multispectral images are stacked as a sequence for inputting. After training the deep network, Softmax classifier is applied to detect the changed region. It is illustrated that our network can obtain the difference of two images to improve detection accuracies. Experiments on the study area of the Jinzhou Bay demonstrate that the proposed method outperforms other approaches.

Classification of multisensor data provides potential advantages over a single sensor in accuracy. In this paper, deep bimodal autoencoders are proposed for classification of fusing synthetic aperture radar (SAR) and multispectral images. The proposed deep network based on autoencoders is trained to discover both independencies of each modality and correlations across the modalities. Specifically, the sparse encoding layers in the front are applied to learn features of each modality, then shared representation layers in the middle are developed to learn fused features of two modalities, finally softmax classifier in the top is adopted for classification. Experimental results demonstrate that the proposed network is able to yield superior classification performance compared with some related networks.

Existing monitoring approaches are not effective in deal with routine thermal discharge monitoring requirements of nuclear power plants. This paper describes a monitoring methodology using an aerial remote sensing monitoring system based on an unmanned aerial vehicle (UAV) platform by taking the monitoring of the thermal discharge of the Hongyanhe Nuclear Power Plant as an example, and conducts a study on remote sensing extraction of thermal diffusion information of the thermal discharge. In this study, quartic polynomial fitting and parallel real data correction are used to correct the wide-angle distortion and acquire the water body surface temperature information, respectively. Synchronized measured data validation of independent samples indicates that the system can acquire diffusion information of the thermal discharge accurately, and the retrieval error of the surface water temperature is within 0.4°C. Results analysis shows that this efficient and convenient aerial remote sensing monitoring system based on a UAV platform can effectively make up inadequacies of existing monitoring technical measures and offers high precision. This system is expected to be further adopted and applied for post-assessment of the environmental impact of nuclear power plant thermal discharge and service monitoring.

Detection of a marine floating raft is significant for ocean utilization, which provides a basis for marine ecosystem protection. In this case study, supervised classifiers of weighted fusion-based representation are proposed to detect marine floating raft using synthetic aperture radar images. To remove the speckle noise and obtain more discriminative features, a weighted low-rank matrix factorization (WLRMF) model is developed to optimize features before detection, where the matrix of patch features is decomposed to acquire the denoised features. Weighted fusion-based representation classifiers (WFRCs) with weighted multiplication are proposed to combine the sparse representation classifier (SRC) and the collaborative representation classifier (CRC) for floating raft detection, which can capture the competition between the floating raft and water surface as well as the collaboration within-class samples. Experiments on the study area of the Bohai Sea confirm that the proposed approach produces better results than some related methods. It is demonstrated that the WLRMF model extracts effective features and overcomes the influence of speckle noise at the same time, and the WFRC model is able to take advantages of the SRC in competition and CRC in collaboration for improving detection accuracies.

The classification of a synthetic aperture radar (SAR) image is a significant yet challenging task, due to the presence of speckle noises and the absence of effective feature representation. Inspired by deep learning technology, a novel deep supervised and contractive neural network (DSCNN) for SAR image classification is proposed to overcome these problems. In order to extract spatial features, a multiscale patch-based feature extraction model that consists of gray level-gradient co-occurrence matrix, Gabor, and histogram of oriented gradient descriptors is developed to obtain primitive features from the SAR image. Then, to get discriminative representation of initial features, the DSCNN network that comprises four layers of supervised and contractive autoencoders is proposed to optimize features for classification. The supervised penalty of the DSCNN can capture the relevant information between features and labels, and the contractive restriction aims to enhance the locally invariant and robustness of the encoding representation. Consequently, the DSCNN is able to produce effective representation of sample features and provide superb predictions of the class labels. Moreover, to restrain the influence of speckle noises, a graph-cut-based spatial regularization is adopted after classification to suppress misclassified pixels and smooth the results. Experiments on three SAR data sets demonstrate that the proposed method is able to yield superior classification performance compared with some related approaches.

Nonnegative matrix factorization (NMF) is an advanced method for nonnegative feature extraction, with widespread applications. However, the NMF solution often entails to solve a global optimization problem with a nonconvex objective function and nonnegativity constraints. This paper presents a collective neurodynamic optimization (CNO) approach to this challenging problem. The proposed collective neurodynamic system consists of a population of recurrent neural networks (RNNs) at the lower level and a particle swarm optimization (PSO) algorithm with wavelet mutation at the upper level. The RNNs act as search agents carrying out precise local searches according to their neurodynamics and initial conditions. The PSO algorithm coordinates and guides the RNNs with updated initial states toward global optimal solution(s). A wavelet mutation operator is added to enhance PSO exploration diversity. Through iterative interaction and improvement of the locally best solutions of RNNs and global best positions of the whole population, the population-based neurodynamic systems are almost sure able to achieve the global optimality for the NMF problem. It is proved that the convergence of the group-best state to the global optimal solution with probability one. The experimental results substantiate the efficacy and superiority of the CNO approach to bound-constrained global optimization with several benchmark nonconvex functions and NMF-based clustering with benchmark data sets in comparison with the state-of-the-art algorithms.

Global optimization is a long-lasting research topic in the field of optimization, posting many challenging theoretic and computational issues. This paper presents a novel collective neurodynamic method for solving constrained global optimization problems. At first, a one-layer recurrent neural network (RNN) is presented for searching the Karush-Kuhn-Tucker points of the optimization problem under study. Next, a collective neuroydnamic optimization approach is developed by emulating the paradigm of brainstorming. Multiple RNNs are exploited cooperatively to search for the global optimal solutions in a framework of particle swarm optimization. Each RNN carries out a precise local search and converges to a candidate solution according to its own neurodynamics. The neuronal state of each neural network is repetitively reset by exchanging historical information of each individual network and the entire group. Wavelet mutation is performed to avoid prematurity, add diversity, and promote global convergence. It is proved in the framework of stochastic optimization that the proposed collective neurodynamic approach is capable of computing the global optimal solutions with probability one provided that a sufficiently large number of neural networks are utilized. The essence of the collective neurodynamic optimization approach lies in its potential to solve constrained global optimization problems in real time. The effectiveness and characteristics of the proposed approach are illustrated by using benchmark optimization problems.

GF-2 is a civil remote sensing satellite developed by China in 2014. In addition to the highest spatial resolution, it is also featured with high radiometric precision and high positioning accuracy, etc.. However, regarding current supervision of sea resources, perfect data processing work flow has not been formed yet. In this paper, four methods of Brovey, Gram-Spectral pan sharpening (G-S), PC Spectral SharPening (PC) and Pansharp (PSH) are adopted to conduct comparative experiments for image fusion and evaluate images that have been fused both subjectively and objectively. Objective evaluation indexes including the average value, the variance, the entropy, the average gradient and the correlation coefficient are selected to compute and analyze fusion effects. According to the research results, it could be indicated that such four fusion methods of GF-2 Satellite are able to be used to significantly improve both the spatial resolution and the utilization ratio of images. And the method of PSH should be employed in case that GF-2 images are applied into visual interpretation and thematic charting.

Speckle noises are inherent issues in synthetic aperture radar (SAR) images, which hampers the analysis and interpretation of SAR images. In this paper, we propose an iterative low-rank representation algorithm for SAR image despeckling. The original SAR image is first transformed to the logarithmic image, which is then filtered iteratively by the proposed low-rank representation model. Specifically, in each iteration, similar patches measured by the Mahalanobis distance are collected into a group, and then filtered by the nuclear regularized low-rank representation. Finally, all of the filtered patches are aggregated to form the denoised image. Experimental results demonstrate that the proposed algorithm is able to yield state-of-the-art SAR image despeckling performance.

Polarimetric synthetic aperture radar (PolSAR) images are widely applied in terrain and ground cover classification. Feature extraction and classifier design are both important in Pol- SAR image classification. In this paper, various target decompositions are applied to obtain different polarimetric features. Since that neighboring pixels usually belong to the same species, they can be simultaneously represented through linear combinations of training samples. Therefore, a collaborative representation-based classifier with spatially joint regularization is adopted for classification. Experimental results demonstrate that the joint collaborative representation model performs better than other state-of-the-art methods, such as support vector machine and simultaneous sparse representation.

Nuclear power industry had a great development in China in recent years and the environmental problems, such as thermal plume, has caused wide public concern. Taking the Hongyanhe nuclear power plant as example, this paper computed and achieved the sea surface temperature distribution with MODIS, HJ-1B and Landsat-8 thermal infrared data separately. These data were imaged in same time phase but different spatial resolution. Based on verified method of average gulf temperature, thermal plume distribution in three kinds of data was achieved. The comparison showed that the Landsat-8 data with higher monitoring precision and better details in thermal plume was more suitable for small area monitoring than the other two, which were affected by mixed pixels caused by low spatial resolution. Considering the different hydrogeological conditions of various nuclear power plants, it's smart to monitoring thermal plume with different temporal and spatial resolution satellite data. It can be foreseen that unmanned plane with infrared scanner and remote sensing technique will be complementary for each other in thermal plume monitoring.

Floating raft aquaculture is an important part of the coastal marine environment monitoring. In order to achieve the accurate monitoring on the range and area of floating raft, combined with on-site underway survey, adopt the high resolution SAR satellite remote sensing data to conduct floating raft aquaculture information extraction. Choosing Beidaihe and its adjacent fields as a key demonstration of floating raft aquaculture study, verify that the proposed joint sparse representation classification approach can quickly and accurately obtain the floating raft aquaculture range and area.

Synthetic aperture radar (SAR) image classification is a hot topic in the interpretation of SAR images. However, the absence of effective feature representation and the presence of speckle noise in SAR images make classification difficult to handle. In order to overcome these problems, a deep convolutional autoencoder (DCAE) is proposed to extract features and conduct classification automatically. The deep network is composed of eight layers: a convolutional layer to extract texture features, a scale transformation layer to aggregate neighbor information, four layers based on sparse autoencoders to optimize features and classify, and last two layers for postprocessing. Compared with hand-crafted features, the DCAE network provides an automatic method to learn discriminative features from the image. A series of filters is designed as convolutional units to comprise the gray-level cooccurrence matrix and Gabor features together. Scale transformation is conducted to reduce the influence of the noise, which integrates the correlated neighbor pixels. Sparse autoencoders seek better representation of features to match the classifier, since training labels are added to fine-tune the parameters of the networks. Morphological smoothing removes the isolated points of the classification map. The whole network is designed ingeniously, and each part has a contribution to the classification accuracy. The experiments of TerraSAR-X image demonstrate that the DCAE network can extract efficient features and perform better classification result compared with some related algorithms.

To improve self-made satellites in the marine oil spill monitoring accuracy, it is presented that a Gao Fen (GF-1) satellite marine oil spill remote sensing (RS) image classification algorithm based on a novel evolutionary neural network. First, a non-negative matrix factorization (NMF) algorithm is employed to extract the image features. Compared with basic features, such as the image spectrum and texture, structuring more targeted oil spill image localization non-negative character fits better for the physical significance of remote sensing images. Furthermore, on the basis of the new features, a new feedforward neural network structure with particle swarm optimization (PSO) algorithm is proposed for GF-1 RS image segmentation. Simulation results of the oil spill event substantiate the effectiveness of the proposed approach to GF-1 satellite image segmentation.

Large-scale optimization arises in a variety of scientific and engineering applications. In this paper, a particle swarm optimization (PSO) approach with dynamic neighborhood that is based on kernel fuzzy clustering and variable trust region methods (called FT-DNPSO) is proposed for large-scale optimization. The cooperative coevolution incorporated with a kernel fuzzy C-means clustering strategy is introduced to divide high-dimensional problems in to subproblems, and explore their search spaces. Furthermore, the independent variable ranges change adaptably by using the variable trust region learning method, which expedites the convergence process and explores in the effective space. In addition, the dynamic neighborhood topology assists the PSO algorithm in cooperating with neighbor particles and avoids the problem of premature convergence. Simulation results substantiate the effectiveness of the proposed algorithm to solve large-scale optimization problems with many well-known benchmark functions.

This paper presents a new nonlinear model predictive control (NMPC) strategy based on the Gaussian particle swarm optimization (GPSO). Through the Taylor expansion, NMPC transform to a quadratic programming problem with unknown parameters. Hence, for the global convergence character and higher optimization accuracy, GPSO is employed to dynamically perform nonlinear constraint optimization. Finally, the proposed control strategy is applied to Ball-Plate system to verify the effectiveness.

A dynamic feedforward neural network (DFNN) is proposed for predictive control, whose adaptive parameters are adjusted by using Gaussian particle swarm optimization (GPSO) in the training process. Adaptive time-delay operators are added in the DFNN to improve its generalization for poorly known nonlinear dynamic systems with long time delays. Furthermore, GPSO adopts a chaotic map with Gaussian function to balance the exploration and exploitation capabilities of particles, which improves the computational efficiency without compromising the performance of the DFNN. The stability of the particle dynamics is analyzed, based on the robust stability theory, without any restrictive assumption. A stability condition for the GPSO+DFNN model is derived, which ensures a satisfactory global search and quick convergence, without the need for gradients. The particle velocity ranges could change adaptively during the optimization process. The results of a comparative study show that the performance of the proposed algorithm can compete with selected algorithms on benchmark problems. Additional simulation results demonstrate the effectiveness and accuracy of the proposed combination algorithm in identifying and controlling nonlinear systems with long time delays.

In order to improve the generalization capacity of neural networks for poorly known nonlinear dynamic system with long time-delay, a novel adaptive dynamic feedforward neural network on modified particle swarm optimization (PSO) algorithm is proposed. The adaptive time delay operator is adopted between input layer and the first hidden layer, and also the last hidden layer and output layer. Utilizing these dynamic time delay parameters, the proposed structure can adequately identify different classes of nonlinear systems expressed in the input-output representation form and pure time delay. Otherwise, to overcome the particles' premature convergence, the white noise and logistic mapping are used to enhance the particles' search performance. Furthermore, the parameters in the dynamic feedforward neural network are trained by the modified PSO method. The proposed neural network shows a satisfactory global search and quick convergence capability, avoiding the complexity of gradient calculation. Simulation results demonstrate that the proposed algorithm is effective and accurate in identifying long-time delay nonlinear systems through the comparison with other methods.

This paper presents a novel dynamic neural network (DNN) predictive control strategy based on modified particle swarm optimization (PSO) for long time delay nonlinear process. The proposed dynamic NN structure could approximate to the actual system model and obtain the pure delay time exactly. An improved version of the original PSO is put forward to train the parameters of NN to enhance the convergence and accuracy. The effectiveness of the proposed control scheme is demonstrated by simulation as well as a test on an experiment on the actual pH Neutralization Process.