Network topology diagram retrieval aims to retrieve the topology diagrams with both visual and topological similarities to a query diagram within the domain of network science and engineering, which can be viewed as a specific image retrieval task. However, current image retrieval approaches are mostly designed for natural images which typically identify the similar candidates by measuring the similarity of visual features. Therefore, these approaches are inherently limited in retrieving network topology diagrams, as they struggle to effectively capture the crucial topological semantics (i.e., the semantics characterized by device roles, connection types, and network structures) embedded in network topology diagrams. To address this issue, we propose the Network Topology-aware Retrieval Framework (NTRF) by incorporating network domain knowledge, which emphasizes the functionality conveyed by the topological semantics to enhance retrieval performance. To be specific, beyond the traditional visual retrieval module, we design a Network Topology Encoder (NTE) to capture the topological semantics by simultaneously representing the device roles, connection types, and network structures. Furthermore, we introduce a SubGraph-aware Re-ranking Module (SGRM) to refine the ranking of candidates, which pays attention to the core diagram regions with significant topological semantics. Intensive experimental results on the collected dataset based on Huawei’s Product Documentation demonstrate that our NTRF outperforms state-of-the-art image retrieval methods by 2.75%, 6.19%, and 9.97% in terms of Recall@1, Recall@5, and Recall@10, respectively.

The high cost of data annotation has become a major factor restricting the hyperspectral image (HSI) classification task. To address this issue, domain adaptation (DA) techniques have been developed to adapt models trained on abundantly labeled HSIs to those with scarce labels. As a novel DA paradigm, active domain adaptation (ADA) seeks to selectively annotate informative examples using active learning (AL) techniques under domain shift scenarios, ultimately enhancing model adaptation performance. However, current ADA methods require annotating a relatively large number of target examples, which is impractical for HSIs. Additionally, the target HSIs suffer from class imbalance, which limits the adaptation performance. To address the above issues, this paper proposes a prototype-guided class-balanced active domain adaptation (PCADA) method for HSI classification. PCADA alternately aligns the distributions between domains through prototype guidance and selects the most valuable target examples for annotation. Specifically, a prototype-guided domain alignment (PGDA) module is introduced, which generates target prototypes based on highly confident pseudo-labels and aligns the distributions of two domains. The inconsistency-aware example selection (IES) module identifies target-specific examples and select the most valuable ones for annotation. Furthermore, we propose a class-balanced self-training (CBST) module that generates pseudo-labels with balanced class distribution to solve the class imbalance issue in target domain. The experimental results conducted on multiple benchmark HSI datasets demonstrate the superior performance of our proposed method. The code will be available at https://github.com/Leap-luohaiyang/PCADA-2025.

Existing iterative methods for hyperspectral image classification extracts aposteriori spatial features from classification maps, and feed them back to the input of classifiers. While this feedback structure enables classifiers to learn from their outputs, the drawback is that all training examples are treated equally throughout the iteration process. This uniform treatment fails to consider the varying difficulty levels of individual examples for different stages of classifiers. Curriculum learning, on the other hand, involves training classifiers from easy to hard to smoothly enhance their generalization ability. In this paper, we introduce a novel soft curriculum generator-based iterative convolutional neural network (SCG-ICNN) for hyperspectral classification. In this method, the difficulty of data is associated with the aposteriori spatial feature of each pixel, and challenging training examples are progressively assigned more weight. Our experiments, conducted on two public hyperspectral datasets, demonstrate that SCG-ICNN outperforms other iterative methods that lack well-designed curriculums.

Variations in atmosphere, lighting, and imaging systems result in diverse category distributions in hyperspectral imagery, impacting the accuracy of cross-domain hyperspectral image classification (HSIC). Unsupervised domain adaptation (UDA) aims to address this issue by learning a model that generalizes effectively across domains, leveraging labels only from source domain (SD). Most existing UDA methods focus on aligning distributions between domains without fully considering the valuable information within individual domains. To fill this gap, this letter proposes a domain-collaborative contrastive learning (DCCL) method. DCCL integrates a novel pseudo-labeling strategy with a cross-domain contrastive learning (CL) framework. Specifically, in the pseudo-labeling phase, the confident examples in target domain (TD) are collaboratively labeled according to the labeled examples in SD and the class centers in TD. Then, the CL phase simultaneously minimizes in-domain and cross-domain contrastive loss to promote the aggregation of examples from the same category in both domains. Experimental results demonstrate that the DCCL achieves the accuracy rates of 93.47% and 54.59% on Pavia and Indiana datasets, respectively, surpassing the performance of other state-of-the-art UDA methods. Our source code is available athttps://github.com/Leap-luohaiyang/DCCL-2024.

In real-world hyperspectral image (HSI) classification, the limited annotated examples usually lead to the insufficiently trained classifier, which further generates low classification accuracy. To overcome this challenge, Domain Adaptation (DA) methods have been developed to transfer learnable knowledge from the external HSIs with sufficient labeled examples (i.e., the source domain) to the interested HSI with scarce labeled examples (i.e., the target domain). Conventional DA approaches often pseudo-label the examples with high classification confidence and then incorporate them in the training process. However, due to the significant domain gap, relying solely on the confident examples may not be adequate to achieve satisfactory performance. Therefore, this paper proposes an Interactive Easy-to-Hard Domain Adaptation method (IEH-DA) to arrange the adaptation process so that the “easy” examples are adapted ahead of the “hard” ones. In an early stage, the easy examples with high pseudo-labeling confidence are selected for the adversarial learning based DA. In a later stage, the “hard” examples with high informativity are further selected, and they are interactively labeled by human expert to provide accurate supervision information for adaptation. As a result, the examples in target domain are used in an easy-to-hard way, which forms a curriculum sequence for orderly model training. Extensive experiments conducted on typical public datasets demonstrate that IEH-DA outperforms other state-of-the-art DA methods for HSI classification.

One difficulty in hyperspectral image (HSI) classification is that there are limited labeled examples to train a classifier. In practice, we often encounter an HSI with limited labels, while another HSI contains enough labels. Domain adaptation (DA) tries to use labeled auxiliary classes data in the second domain (i.e. source domain) to help classify classes in the first domain (i.e. target domain). The categories of source and target domains are not necessarily the same. However, the existing methods do not fully consider the conflict of data distribution between the two domains. To solve the challenge, this paper proposes a feature integration-based deep cross-domain fewshot learning (DCFSL-FI) method. Specifically, the information of source domain and target domain are integrated at the feature level, and the integrated data are used in the training process of FSL and DA at the same time, in an attempt to reduce the conflicts of data distribution and extract the common and discriminative information of the two domains. Experiments on three real datasets confirm the effectiveness of our method.

Acquiring labeled training examples for hyperspectral images (HSI) is an expensive task, and even labeling one more pixel requires a real-time field survey of tens of square meters. Therefore, it is highly demanded to achieve satisfactory accuracy for an HSI classification method when the number of labeled examples is extremely limited. However, most of the existing methods lack the ability to handle extremely sparse labeled data. To overcome this issue, we propose a novel graph-based framework for HSI classification, termed “dynamic spectral–spatial Poisson learning” (DSSPL). Specifically, three measures are used to enable the proposed model suitable for the situation of extremely limited labeled data. First, Poisson learning (PL) is adopted for predicting labels on a graph, as it can prevent undesirable constant output labels of traditional label propagation methods and generate more informative label determinations. Second, spectral and spatial graphs are constructed from various features and fused to build a spectral–spatial graph, which exploits comprehensive connective relationships among pixels. Third, in each iteration, the fused graph is dynamically updated by feeding back the up-to-date label information generated by each iteration. The feedback strategy progressively refines the fused graph, and the propagation on the updated graph in turn improves output labels iteratively. Intensive experimental results on three public datasets demonstrate that the proposed DSSPL significantly outperforms other state-of-the-art HSI classification methods when very few pixels (e.g., 3, 5, or 10 of each class) are labeled.

Acquiring training samples in remote sensing images is always expensive and time-consuming. As a consequence, it would be preferable if one domain without training samples (the target domain) could be classified givena prioriknowledge from another domain (the source domain). In this letter, an iterative training sample updating (ITSU) approach is proposed based ona posteriorispatial feature extraction. First, the classifier is trained with initial training samples from the source domain and applied to the target domain, producing a preclassification map. Then, as an invariant feature, thea posteriorispatial features are extracted with a guided filter. Based on the spectral features and thea posteriorispatial features, a criterion measuring the similarity of the cross-domain samples is defined. New training samples from the target domain are assigned with pseudo-labels, and the original samples in the source domain are removed. Furthermore, thea posteriorispatial feature maps are fed back to the input images, and new classifiers are trained with an updated training sample set in the updated feature space. This procedure is repeated until the stopping rule is satisfied. Finally, the adapted classifier is obtained based on the updated training samples. The experimental results on three hyperspectral data sets indicated that ITSU achieved the best performance compared with the other two state-of-the-art methods.

Due to significant geometric distortions and illumination differences, developing techniques for high precision and robust multisource remote sensing image registration poses a great challenge. This article presents an iterative image registration approach, called iterative scale-invariant feature transform (ISIFT) for remote sensing images, which extends the traditional scale-invariant feature transform (SIFT)-based registration system to a close-feedback SIFT system that includes a rectification feedback loop to update rectified parameters in an iterative manner. Its key idea uses consistent feature point sets obtained by maximum similarity to calculate new alignment parameters to rectify the current sensed image and the resulting rectified sensed image is then fed back to update and replace the current sensed image as a new sensed image to reimplement SIFT for next iteration. The same process is repeated iteratively until an automatic stopping rule is satisfied. To evaluate the performance of ISIFT, both the simulated and real images are used for experiments for the validation of ISIFT. In addition, several data sets are particularly designed to conduct a comparative study and analysis with existing state-of-the-art methods. Furthermore, experiments with different rotation are also performed to verify the adaptability of ISIFT under different rotation distortions. The experimental results demonstrate that ISIFT improves performance and produces better registration accuracy than traditional SIFT-based methods and existing state-of-the-art methods.

A spectral-spatial (SS) hyperspectral classifier generally implements a spectral classifier (SC) followed by a spatial filter (SF) for classification. This article develops a new approach to fusing multiple SC-SF classifiers for hyperspectral image classification (HSIC) as to improve classification performance. To accomplish this goal an iterative process is particularly designed to fuse the spatial-filtered classification maps (SFMaps) produced by each of SC-SF classifiers into one single SFMap via maximum a posteriori (MAP) criterion. Such fused SFMaps are then fed back and added to the current data cube to create a new data set for next round SC-SF classifier fusion. The same process is repeated iteratively until it satisfies an automatic stopping rule. To further fuse more than two SS methods, two approaches are also developed, called simultaneous multiple SC-SF fusion (SMSSF) method and progressive multiple SC-SF fusion (PMSSF) method. Experimental results demonstrate that fusing multiple SC-SF classifiers can indeed perform better than using an individual single SC-SF classifier alone without fusion.

Urban impervious surface area (ISA) is a key index toward urban eco-system and sustainable urban planning strategy. In this paper, a subpixel approach is proposed to estimate urban ISA values using linear spectral mixture analysis (LSMA)-based hyperspectral imaging techniques. In doing so the concept of virtual dimensionality (VD) is used to first estimate the number of endmembers, then an endmember finding approach is implemented to find VD-determined number of endmembers in a hyperspectral image. Finally, nonnegativity constrained least squares (NCLS) is performed for endmember unmixing. The hyperspectral image used in our approach provides a larger number of spectral dimensions than a multispectral image does so that a sufficient number of endmembers can be found from a hyperspectral image for ISA estimation. What is more, a relationship between ISA values and fractional endmember abundances can be further constructed by linear regression.

This letter presents a study of vehicle detection in high-resolution images using superpixel segmentation and iterative convolutional neural network strategy. First, a novel superpixel segmentation integrated with multiple local information constraints method is proposed to improve the segmentation results with a low breakage rate. To make training and detection more efficient, we extract meaningful and nonredundant patches based on the centers of the segmented superpixels. For reducing the instability in detection performance because of manual or random selection of samples, a training sample iterative selection strategy based on convolutional neural network is proposed. After a compact training sample subset is obtained from the original entire training set, a representative feature set with high discrimination ability between vehicle and background is extracted from these selected samples for detection. To further avoid overfitting the training and promote the detection efficiency, data augment and a main direction estimation method are used. Comparative experimental results on Toronto data indicated the effectiveness of our proposed method.

This letter extends one of popular spectral-spatial classification methods for hyperspectral images, called edge preserving filtering (EPF)-based method to an iterative version of EPF method, referred to as iterative EPF (IEPF). Instead of finding maximum of the final soft probability maps obtained from the initial binary probability maps by EPF, the proposed IEPF feeds back the soft probability maps and combines them with the currently being processed image cube to create a new image cube as the next input to IEPF to reimplement support vector machine (SVM) for classification. The process is carried out iteratively by repeatedly feeding back the spatial information provided by EPF-obtained soft probability maps and terminated by a Tanimoto index (TI)-based automatic stopping rule. The experimental results demonstrate that IEPF performed better than EPF by providing higher classification accuracy.

This paper presents a new spectral-spatial (SS) approach to hyperspectral image classification (HSIC), called SS feedback close network system (SSFCNS), which has not been explored in the past. Unlike commonly used SS-based methods SSFCNS includes a feedback close network system (FCNS) to obtain spatial information via a selective spatial filter in an iterative manner. More specifically, SSFCNS takes advantage of FCNS which utilizes a particularly selected spatial filter to capture a posteriori spatial information directly from spectral-classified data samples and then feeds back such obtained spatial-filtered image to be combined with the current image cube to create a new image cube that can be used as a new input to re-implement SSFCNS. The process is carried out in such a way that the spatial information obtained from spectral classification results is updated by FCNS iteratively and terminated by a Tanimoto index (TI)-derived automatic stopping rule. To evaluate the performance of SSFCNS several spatial filters (i.e., Gaussian, bilateral, guided, and Gabor filters) are explored for real image experiments. The experimental results demonstrate that SSFCNS performs significantly better in classification accuracy compared to SS-based methods which do not use FCNS.

In hyperspectral image classification spectral information and spatial information are always integrated to improve the classification accuracy. This paper develops an iterative version of support vector machine, to be called iterative SVM (ISVM) to perform hyperspectral image classification by extracting spatial information iteratively via feedback loops. In processing ISVM an initial hyperspectral data cube is obtained by combining the original image and its first principal component. SVM is then implemented to the resulting data cube to produce an initial classification map. In each feedback loop, a Gaussian filter is applied to obtain the spatial information of the SVM-classification map so that the Gaussian-filtered map is further fed back to combine with the currently processed hyperspectral cube for the next round of iteration. As for terminating the iterative process an automatic stopping rule is also developed. To evaluate the performance of ISVM real image experiments are conducted in comparison with state-of-the-art spectral-spatial hyperspectral classification methods. The experiment results demonstrate that ISVM performed better by providing higher classification accuracy.

In hyperspectral image processing technologies, anomaly detection is a valuable and practical way of searching small unknown targets based on spectral characteristics. For the lack of prior knowledge of targets, background modeling on hyperspectral images is the key process that affects the outcome of anomaly detection operator. In this paper, a novel method of anomaly detection based on quadratic modeling is proposed. The innovation of the proposed algorithm is that it divides the detection process into two main steps: one is initial detection, which provides a preliminary judgment of background pixels; the other is the quadratic background modeling to reduce the contamination of outliers, consisting both anomaly pixels and abnormal background pixels. In the part of experiments, a semisimulated hyperspectral image and a real hyperspectral image are both used to evaluate the performance of our proposed method. Visual analysis and quantative analysis of receiver operating characteristic (ROC) curves both show that our algorithm performs better when compared with other classic approaches and state-of-the-art approaches.

Image fusion is one of the most important image processing methods in the field of remote sensing. Multispectral (MS) or hyperspectral (HS) images are often fused with panchromatic (PAN) images to enhance their spatial resolution while preserving the spectral information, which will lead to a better interpretation in subsequent applications. In order to achieve this goal, an improved method of image fusion, which is based on the amélioration de la résolution spatiale par injection de structures (ARSIS) Concept, is proposed in this paper. In the new method, the degree of diversity between each pixel and its surroundings is measured utilizing Center-Surround Spectral Angle (CSSA) model, and the more different a pixel is, the more detailed information is injected into the MS image. In order to verify the advantages of our proposed fusion method, experiments are processed on two data sets. Several assessment criteria are used to demonstrate the effectiveness of our proposed algorithm. Moreover, from the view of application, accuracy of classification on fused images is also evaluated to further show the superiority of our method.

In hyperspectral images, there is abundant spectral information for classification. In most cases, spectral information based methods yield good classification results. However, different objects may have similar spectrums due to similar physical property. Spectral information based classification methods can't give accurate results for the similar physical property among objects belonging to the same main category. On the other hand, as the resolutions of sensors increase in recent years, more spatial information, such as shape and texture information can be extracted and described more precisely. In this paper, we proposed a spatial information aided similar spectral classification. In our method, spatial features, including the pixel shape index (PSI), and the gray-level co-occurrence matrix (GLCM), are extracted for accurate classification. We compared the Bhattachary Distance before and after spatial information being aided and we found that the B Distance was amplified sharply, which indicated that the separability among classes increased. Classification is practiced on two hyperspectral data sets, Kennedy Space Center and Pavia City, and the proposed method is compared with classification based on different features. It is found that each feature makes contribution to classification and the accuracy of the proposed method is the highest among all methods, which certifies the effectiveness of our algorithm.