Relative radiometric correction of multiple remote sensing images is essential for remote sensing data processing and its product generation. Due to the limitation of RGB color space, many existing RGB-based methods may have local radiance anomalies and strong dependence on reference images when dealing with dramatic radiance differences. Therefore, a hue-distance constrained relative radiometric correction method considering the Helmholtz-Kohlrausch effect (H-K effect) is proposed, by which the radiance differences of images can be corrected without the reference image to obtain better radiance consistency results. For the radiance anomalies in the correction process, the method in this paper avoids the RGB color space limitation and minimizes the channel correlation in the correction process, and optimizes the correction results by using the hue-distance to impose adaptive constraints on the correction process. Considering the influence of the H-K effect, the subjective visual perception of the human eye is introduced into the radiance correction of remote sensing images for the first time and the global perceptual lightness mapping is performed on the images, which extends the perceptual lightness mapping from pixel level to image level. Experimental results show that the proposed method can effectively eliminate the huge radiance differences in multiple remote sensing images, suppress the possible radiance anomalies, and obtain the correction results in line with the subjective perception of the human eye, and it is of great help to the subsequent remote sensing processing.

Hyperspectral images (HSIs) have widespread applications in geoscience, environmental monitoring, and resource management. However, in practical engineering applications, random stripe noise in HSIs severely affects data quality and accuracy, impacting the subsequent use of HSIs. In this paper, we propose an HSI intensity adaptive destriping method based on reference image-guided pixel clustering. The proposed method removes stripe noise through three main stages. First, the HSI is analyzed to manually select a specific band image with minimal impact from random stripe noise. This image is designated the initial reference image, and its specific location is identified. Second, the proposed pixel threshold classification method, deep maximum inter-class variance, is used for pixel threshold classification for band images that are contaminated with random stripe noise and adjacent to the reference image. Finally, the resulting pixels are classified into two categories according to noise intensity: high intensity and low intensity. The proposed intensity adaptive grayscale value reconstruction algorithm is used to remove stripe noise from each pixel category, and the denoised image is updated as a new reference image. Starting from the initial reference image, these steps are repeated along the spectrum to achieve destriping of all bands. We compare the proposed method against traditional and deep learning methods using real HSIs. The experimental results show that after denoising of the image using our method, both visual quality and quantitative evaluation metrics are significantly improved, with particularly excellent stripe removal performance observed for HSIs with significant grayscale variation.

The goal of image stitching is to generate high-quality panoramic images with minimal computational cost. However, variations in viewpoint or scene depth can cause parallax effects in UAV images, complicating precise alignment and leading to artifacts such as ghosting, blurring, and misalignment. While advanced seamline detection algorithms reduce ghosting and blurring, structural distortions and misalignments near the seamline often remain, negatively affecting stitching quality. Moreover, these algorithms typically face challenges in balancing computational efficiency with alignment accuracy. In this paper, we propose a robust and flexible UAV image stitching method based on global optimal seamline detection and local alignment with seamline constraint. Our approach ensures precise alignment while maintaining processing efficiency. First, a global transformation-based alignment algorithm is used to pre-align the images to a common coordinate system. Then, an efficient weighted fast sweeping (WFS) algorithm is proposed to detect the globally optimal seamline, minimizing artifacts in overlapping regions caused by alignment errors and dynamic objects. Finally, an optical flow-guided local alignment method with seamline constraint is developed to correct residual misalignments along the seamline, reducing global structural distortion. Extensive experiments on a range of challenging datasets demonstrate that the proposed method outperforms existing approaches, producing more natural-looking stitching results.

Achieving color consistency is essential for stitching large-area optical satellite imagery. The narrow swath width of individual images, combined with varying acquisition conditions, inherently introduces color differences. These manifest as marked disparities in brightness, color tone, and local contrast, degrading overall regional consistency. Existing methods primarily focus on correcting color inconsistencies between adjacent images, while often overlooking intra-image illumination non-uniformity, thereby propagating radiometric errors into optimization frameworks. Whisk-broom sensors, which can acquire imagery over a much wider swath width along the parallels, frequently exhibit substantial intra-image brightness variations, particularly in high-latitude regions. Compounded by frequent cloud cover and rapid temporal changes of features, extracting reliable color correspondences for optimization becomes intractable. To address these challenges, we propose a novel color correction framework that simultaneously considers intra-image illumination non-uniformity and radiometric variations caused by cloud prevalence and dynamic surface changes. First, a solar elevation angle map is extracted for down-sampled source image based on their geographic metadata and sensor geometry. An inverse compensation based on the normalized sine value of the solar elevation angle is then applied to mitigate brightness disparities caused by varying incident radiance. Second, to address atmospheric effects that vary with wavelength, such as differential absorption and scattering that cause color casts especially in low-illumination regions, a reference spectral channel is selected to guide the correction. Finally, we introduce a hybrid strategy for selecting reliable color correspondences in overlapping regions, using both grayscale and texture similarity under complex coverage conditions. Residual radiometric information is incorporated into a cost function, which jointly considers original color control and overall color balance to enhance the global consistency of the corrected mosaic. Extensive experiments conducted on imagery from the Wide Swath Imager (WSI) of DaQi-1 (DQ-1) and the Chinese Ocean Color and Temperature Scanner (COCTS) of HaiYang-1E (HY-1E) demonstrate that the proposed method effectively removes uneven illumination and color discrepancies. Compared to three state-of-the-art methods, our approach achieves superior performance in both visual quality and quantitative metrics.

Semi-supervised building instance extraction aims to learn from limited labeled data alongside an extensive collection of unlabeled data, offering a promising approach for extracting building instances from high-resolution (HR) remote sensing images (RSIs). However, complex RSIs frequently encounter challenges such as background interference and intricate noise, struggling in generating reliable pseudo labels. To alleviate this, we propose a novel cross-level consistency constraint semi-supervised building instance extraction method (CLC2SIE) to enhance pseudo label generation. Specifically, CLC2SIE contains two core modules: object-level dynamic consistency (OLDC) and pixel-level saliency consistency (PLSC). The OLDC module dynamically converts building features from background into valuable supplementary information, enhancing the model’s perception of building instances in complex scenes. Additionally, the PLSC module is designed to mitigate the boundary noise in pseudo labels by saliency guidance, which improves model’s awareness of building contours. By co-learning these modules in an end-to-end manner, CLC2SIE facilitates pseudo label generation and improves extraction performance. Experiments were conducted on the three public building datasets, i.e., WHU, CrowdAI and TCC, demonstrate that CLC2SIE achieves superior performance compared to state-of-the-art semi-supervised instance extraction methods at different labeling ratios. This study explores a novel semi-supervised learning (SSL) framework that exploits cross-level consistency to improve pseudo label generation, offering a methodological reference for various SSL applications in RSIs.

Change detection (CD) identifies pixel-level land cover changes in multi-temporal remote sensing imagery, playing a critical role in environmental monitoring and disaster management. Despite the advantages of high-resolution imagery in providing detailed spatial and contextual information, it significantly intensifies the complexity of fine-grained CD by exacerbating sensitivity to spatial misalignment. While accurate CD conventionally hinges on precise image registration, inevitable misalignments in practical scenarios often lead to spurious change responses and compromise the stability of feature representation learning. To address these challenges, this paper proposes the Intra- and Cross-temporal Graph Interaction Network (ICGINet), a novel architecture that enhances CD robustness under registration uncertainty by modeling intra- and cross-temporal dependencies through adaptive graph-based message passing and relational reasoning. Specifically, ICGINet employs a dual-branch design that combines convolutional neural networks (CNNs) for local semantic extraction with graph neural networks (GNNs) for global relational modeling, enabling the integration of fine-grained spatial details and long-range contextual dependencies. To reconcile intra-temporal features from these heterogeneous branches, a Dual-Focus Feature Fusion (DFFF) module is proposed to facilitate attention-guided information exchange and semantic alignment. Furthermore, an Adaptive Neighborhood Graph Awareness (ANGA) module constructs a displacement-aware dynamic graph to capture cross-temporal neighborhood correspondences, thereby mitigating spatial misalignment during temporal interaction. Comprehensive experiments on two challenging datasets demonstrate that ICGINet achieves superior performance compared to state-of-the-art methods, both in terms of accuracy and robustness to registration error.

The widespread use of time delay and integration charge-coupled device (TDI CCD) technology in high-resolution spaceborne optical cameras has made high-frequency jitter effects a common issue, resulting in different levels of distortion in images. Current methods mostly concentrate on correction of obviously high levels of geometric distortion. Focusing on low levels of geometric distortion, which are more difficult to accurately detect, this paper proposes a progressive learning-based correction method for high-frequency jitter distortion in remote sensing images from spaceborne TDI CCD cameras, utilizing a Generative Adversarial Network (GAN). First, a distorted dataset with diverse jitter levels for progressive training is generated through jitter simulation model by adjusting the parameters. Then, a GAN model is employed for the correction task. The generator consists of the Distortion Net for geometric distortion correction and the Detail Enhancement Net for image detail restoration. Finally, a progressive learning strategy is used to gradually enhance the ability of network to correct minor geometric distortion. The proposed method is validated using simulated images and real-world satellite images. Experimental results demonstrate that the proposed method outperforms existing restoration methods both in simulated datasets and practical scenarios.

Recently, many new results of sharpness assessment for digital images have been achieved, which help to select valuable images from massive images with ragged quality. However, remote sensing images encompass a wide range of scenes with diverse characteristics, and their acquisition is often influenced by blurs and noises. Many commonly used sharpness assessment methods based on uniform metrics face challenges in ensuring both subjective and objective impartiality when applied to remote sensing images. Therefore, a novel method for assessing the sharpness of remote sensing images based on a deep multi-branch network considering scene features is proposed. In the method, a multi-task module, comprising scene classification and sharpness assessment tasks, is proposed to comprehensively consider the potential impact of diverse scene characteristics on the assessment of sharpness. The accuracy of sharpness assessment is improved by sharing features that reflect the correlation between the scene and sharpness. To overcome the issue of imbalanced task predictions during the joint training of multiple tasks, a total loss function using gradient balance strategy is designed. In addition, the improved attention module and the feature fusion module are used to better utilize feature information at different scales. Experimental results obtained from datasets demonstrated that the proposed method can outperform the existing comparable methods and achieve satisfactory results, proving its feasibility and effectiveness.

Cloud detection is one of the crucial tasks in the field of remote sensing, which is regarded as binary classification at the pixel level. Although a large number of recent deep learning-based methods have made great progress, most of their appealing performances come at the expense of a large amount of computation, which reduces the real-time performance accordingly. In order to bridge the gap between segmentation performance and inference speed, we propose a novel asymmetric nested network (ANNet) architecture termed ANNet, which is designed for real-time cloud detection with excellent performance. In the encoder branch of ANNet, we introduce an effective tiny U-shape block (TUB) to enrich detailed spatial contexts in each stage, which also allows ANNet to embed spatial recovery ability earlier, and a lightweight, simple feature fusing module (SFFM) is designed to refine the semantic features map at a lower level of TUB for better performance. Following the encoder, which is diverse from the most symmetric U-shape approaches, an asymmetric and lightweight decoder (ALD) with only convolution and bilinear up-sample operations is employed for spatial recovery. We also, moreover, demonstrated that using a constant channel size instead of a larger channel volume as the network goes deeper is an efficient and effective design for cloud detection tasks. Substantial experiments are performed on GF1_WHU and 95-Cloud datasets, which show that ANNet has achieved excellent performance with low computation cost compared to most of the existing state-of-the-art methods. On GF1_WHU dataset, ANNet-l achieves 93.79% on mIoU at 125 FPS, while ANNet-s with only 29.5 K parameters yields 90.74% mIoU at 251 FPS on the Nvidia RTX 2080Ti.

Remote sensing image change detection is extensively utilized in various applications in the field of remote sensing, particularly in the realm of cropland conservation, where it plays a critical role in protecting the agro-ecosystem and ensuring global food security. However, the progressive improvement in resolution and size of remote sensing imagery has led to a ’scale gap’ challenge in the detection of small building changes in cropland areas. To address this challenge, an innovative multi-scale feature fusion change detection network (M-Swin) based on transformer using hierarchical windows is proposed. In order to obtain clearer edges and better separation of the change results, a novel saimese transformer encoder (MSW encoder) is proposed, which can better capture the change information in small building through hierarchical windows and fuse the multi-scale feature obtained from different windows. To effectively reduce missed and misdetected small-area of changing buildings, a novel bi-temporal image feature fusion module (BFFM) is proposed, which can enhance the features based on a priori guidance, thus improving the saliency of change regions. Additionally, a new remote sensing image change detection dataset for cropland, called LuojiaSET-CLCD, has been proposed. Experimentally demonstrates that M-Swin has good potential for highly accurate change detection of small buildings within cropland areas and outperforms several newly existing methods in three datasets (LEVIR, WHU-CD and LuojiaSET-CLCD). Our dataset will be publicly available at https://github.com/RSIIPAC/LuojiaSET-CLCD.

Time delay integration charge-coupled device (TDI CCD) is sensitive to the platform’s stability during push-broom imaging. Due to variations in total integration time, panchromatic and multispectral images suffer varying degrees of geometric distortion caused by satellite jitter with high frequency, which leads to different inner distortion in different band images and different band-to-band mismatching errors between different band combinations. To address this problem, this paper proposes a rigorous parallax observation model considering multi-stage integration time and presents a jitter distortion correction method for remote sensing panchromatic and multispectral images captured by TDI cameras based on it. First, the law of the amplitude attenuation and phase offset of platform jitter deviation on the image under different TDI stages is determined through simulation verification. Then, the rigorous parallax observation model is proposed to establish an accurate relationship between the relative jitter error of two multispectral images with multi-stage integration and the absolute single-stage integration jitter error by introducing the amplitude attenuation factor and phase offset. Finally, the jitter distortion curves of images with different integration stages and integration time can be reconstructed based on the estimated absolute jitter error and the imaging parameters. Subsequently, the jitter distortion can be further corrected by image resampling. The proposed method was verified through both simulation and real data experiments using GaoFen-9 satellite images. Experimental results show that the proposed method can effectively correct high-frequency jitter distortion in panchromatic and multispectral images, which cannot be corrected by traditional single-stage integration jitter detection model.

Relative radiometric calibration (RRC) is a crucial step in enhancing the quality of satellite images, and it serves as a fundamental technology to ensure the reliability of information extracted from these images. Traditional side-slither RRC methods typically use homogeneous scenes as imaging sites to ensure approximately identical input for detectors of multiple non-collinear time-delay integration-charge-coupled devices (TDI-CCDs). However, as spatial resolution improves, the demand for site uniformity is increasing, limiting the application of these traditional methods. Therefore, we propose a push-broom data-aided side-slither RRC method, which involves sequential calibration of a single TDI-CCD and an entire field of view (FOV). The first step is designed to eliminate response differences within each TDI-CCD, while the latter incorporates push-broom data to determine the adjacent radiometric relationship via adjustment with maximum standard deviation preservation (MSDP). Experiments show that our method has achieved better results in both visual effect and quantitative assessment, compared with the other two advanced RRC methods.

Scene classification of aerial images is the basis of automatic recognition of complex scenes, and it is also a challenging computer vision task. In recent years, with the rapid development of deep learning, the semantic feature extraction method based on a convolutional neural network (CNN) has made great progress. Moreover, a recent study indicates that combining the semantic information of deep-layer features with the detailed texture information of shallow-layer features in CNN can further improve the performance of classification. In this letter, an end-to-end multilevel feature-based network named multilevel inheritance network (MINet) is proposed for aerial scene classification. First, the feature extraction module based on the feature pyramid network (FPN) is used to get multilevel feature maps. In the process of merging shallow features, high-level semantics of deep-layer are inherited. Then, an attention mechanism is added after the multilevel features to reduce the interference of redundant information and noise. Finally, we use a feature fusion module to automatically learn the weight of each feature layer and make a comprehensive decision. The effectiveness of the proposed method is verified in AID, WHU-RS19 and NWPU-RESISC45 datasets. Results show that the proposed method achieves competitive classification accuracy.

In the field of remote sensing image processing, clouds heavily affect the quality of the remote sensing images and their application potential. Thus, in recent years, with the prevalence of deep learning techniques used in the field of image processing, many methods have been proposed for cloud removal using single remote sensing images. The existing single-image cloud removal methods suffer from poor generalization capabilities that prevent them from being applied to diverse remote sensing images. Thus, a novel method using a multimodal architecture is proposed which provides multiple most likely outputs for the image and selects the best one through perception-based image quality evaluator (PIQE). In addition, adversarial consistency loss is used to replace cycle consistency loss, which encourages the model to retain more texture information of the original image, and thus the quality of the generated image increases. Experiments demonstrate that the presented method can easily achieve a considerable increase in the peak signal-to-noise ratio and the structural similarity index compared with other methods.

There are many areas with cloud coverage in remote sensing images frequently, and they will shelter the ground objects under clouds. When images with clouds are used in mosaicking, cloud coverage areas may still exist in mosaics, which constrain the application of mosaics. Therefore, a simple and fast image mosaicking method based iteratively minimizing cloud coverage areas is proposed in this letter. In the proposed method, both cloud-free images and images with clouds are used to generate a mosaic with minimal cloud coverage areas. After the generation of initial effective mosaic polygons (EMPs) for images, a quadtree iterative procedure is presented to optimize each EMP by excluding the residual cloud coverage areas in each EMP. The procedure fully takes into account the spatial distribution of cloud coverage areas, and each area with cloud coverage is minimized by as few images as possible. The experimental results show that the mosaic generated by the proposed method can achieve a better radiometric continuity and higher efficiency than the pixel-based mosaic method. Besides, the proposed method can make the full use of the cloud-free areas in images, which can improve the utilization of images and the currency of mosaics.

Although time delay integration charge-coupled devices (TDI CCDs) have been widely used in high-resolution spaceborne optical cameras, they are sensitive to satellite jitter: the images obtained by them are affected by both distortion and blur. Therefore, according to the multistage integral imaging characteristics of TDI CCDs, this article not only proposes a continue dynamic shooting model (CDSM) to reflect the real push-broom mode of the satellite but also presents a method containing jitter detection and image restoration based on it. In the presented method, the CDSM subdivides the TDI CCD integration intervals. The subdivision number of CDSM is determined by the proposed integral transformation function (ITF). Then, it feeds back into the ITF and also contributes to the point spread function (PSF) estimation. Among the abovementioned, ITF defines the relationship between the parallax images and the jitter curve, and aims to improve the jitter detection performance. Finally, an adaptive image restoration based on context is conducted, which combines time, space, and spectrum information. Besides the simulated images, multispectral images of GaoFen-1 02 satellite were also adopted to validate the performance of the presented method. Experimental results indicate that the accuracy of the jitter detection is increased, and the geometric and radiometric qualities of restored images are also improved.

The launch of the ZY-3 surveying and mapping satellite (ZSMS) by China has resulted in a significant increase in the volume of image data collected for subsequent processing. In this letter, we present our research on the message passing interface (MPI), open multiprocessing (OpenMP), and compute unified device architecture (CUDA)-based (MOC-based) preprocessing of ZSMS images in a system that consists of multiple central processing units (CPUs) and graphics processing units (GPUs). First, CPUs and GPUs in the system are organized into atomic computing resources (ACRs) by means of an MPI. Then, three cooperative methods are proposed for potential performance improvement of the processors with OpenMP and CUDA. The input/output (I/O) overhead is also addressed in this letter. The experimental results show that the total execution time of the 12 ZSMS nadir images with four ACRs is reduced to 86.10 s, which could provide near-real-time response for the time-critical applications that follow.

This letter presents a method of seamline determination based on segmentation for orthoimage mosaicking in an urban area. Image segmentation is used to achieve regions of objects. First, preferred regions through which seamlines are inclined to be passed are determined by spans of segmented regions. Second, pixel-level optimization is carried out using Dijkstra's algorithm based on differential cost to find the optimized seamline. The experimental results on digital aerial orthoimages in the urban area prove that the new method is promising for the seamline determination in mosaicking.

The area Voronoi diagrams with overlap (AVDO) method was recently presented and has been used to generate a seamline network for the mosaicking of orthoimages. The method shows considerable potential advantages for seamless mosaics covering a large geographic region. In this paper, the method is further improved, and a seamline network refinement approach based on AVDO is presented. The improvements of the presented approach include the detection of valid regions for orthoimages, a more general algorithm for the generation of bisectors, and the refinement of the seamline network combining the bottleneck model and the Dijkstra's algorithm. Finally, a number of digital aerial orthoimages are employed to test the proposed approach, and the experimental results demonstrate its efficacy.

The building detection is one of the most challenging issues in remote sensing image processing. In this paper, a novel approach for building detection using corner detection, segmentation and adaptive windowed Hough Transform is presented. In the first, the Mean shift segmentation is used to split the image into a numbers of classes. In the second step, the scale invariant feature transform (SIFT) is used to extract the corners in the original image. In the third step the corners are used as one of the evidences to verify the presence of buildings. In the Mean shift segmentation result image, around the corners detected by SIFT algorithm, the approximate boundary of the buildings is extracted. With the help of approximate boundary of the buildings, the size of the building can be estimate. Finally, in order to extract the precise building roof boundary, the adaptive windowed Hough Transform is used to extract the straight line of the building boundary. Preliminary experimental results indicate that the proposed method produced promising results.

High precision MTF measurement is the basis of high quality image restoration. Since the presence of noise in images, traditional MTF measurement based on Target image will produce biased result, and the biased result will introduce new noise after image restoration. In this paper, based on analysis of characteristics and limitation of traditional image restoration methods, we propose an image restoration approach based on Kalman filter, this approach firstly uses Gaussian fitting to obtain theoretical value of line spread function, then it uses KALMAN filter to obtain the true value of line spread function from theoretical value and measured value. Experiments on TDI-CCD images show that the approach proposed in this paper make better performance.

Considering band misalignment caused by attitude jittering or other factors, band registration becomes the most critical pre-processing step for multispectral imagery as the registration result will directly influence the following applications. So band registration accuracy evaluation is necessary before registered imagery going through the next processing step. This paper proposes an automatic approach to evaluate the band registration accuracy for multi-spectral imagery. The proposed method is based on the theory of image matching, which includes three main steps: 1) feature points detection, and then 2) corresponding points matching, and 3) accuracy evaluation. Experiments are designed for the validation of the proposed approach with RGB image of Toronto in Canada captured by the Microsoft Vexcel's UltraCam-D (UCD) camera, in which quantitative analyses are applied to assess the accuracy and reliability of the method. And evaluation result for multi-spectral images of satellite, i.e., ZiYuan-3, by this method were presented. The result shows that the proposed method can automatic evaluate band registration accuracy for multi-spectral imagery accurately, efficiently and objectively.

Digital aerial orthoimages have been widely used in surveying, mapping, geographic information systems, visualization, and other applications. However, when producing digital aerial orthoimages, radiometric equalization over large areas is often a most time-consuming and costly process and has become a bottleneck. This letter presents a network-based radiometric equalization approach to eliminate the radiometric differences between images. The network is constructed using the area Voronoi diagrams with overlap and is based on the topological relationship of the constructed network; transferring paths between images are determined, and a global-to-local strategy is used to improve the algorithm, both in its global and local performance. Digital aerial orthoimages from both film-based and digital cameras are used to evaluate the performance of the presented algorithm.

The mosaicking of orthoimages has been used to cover a large geographic region for various applications ranging from environmental monitoring to disaster management. However, existing mosaicking methods mainly focus on the generation of seamlines between two adjacent orthoimages. In this paper, we present a novel approach based on the use of a seamline network formed by a novel area Voronoi diagrams with overlap and the use of effective mosaic polygons (EMPs) to define the pixels of each orthoimage for the final mosaic. The generated seamline network is global based and is also optimized after refinement. It gives an effective partitioning for the regions of all orthoimages to form EMPs. The partitioning is unique, seamless, and has no redundancy. The algorithm is parallel, and the EMP of each orthoimage only has relation to orthoimages which have overlaps with it. It can ensure the flexibility and efficiency of mosaicking, without an intermediate process and independent of the sequence of the image composite. The experimental results obtained from the mosaicking of 40 color orthoimages demonstrate considerable potential for generating a seamline network automatically and effectively. This is extremely useful when a seamless mosaic is required to cover a large geographic region.