Although significant advances have been made in the semantic segmentation of high-resolution remote sensing (RS) images, obtaining accurate pixelwise annotations remains resource-intensive. We propose SparseFormer, a credible dual-convolutional neural network (CNN) expert-guided Transformer model designed for semantic segmentation using point-level annotations to reduce this annotation burden. SparseFormer comprises three branches, where two CNN branches employ different attention mechanisms to encourage diverse outputs. To enhance the local consistency of pseudolabels, we introduce a pixel-adaptive refinement (PAR) module that dynamically refines CNN output probabilities by incorporating image information during training. A credible assessment is then performed to combine the CNN outputs, producing high-quality pseudolabels that supervise the CNN-Transformer hybrid branch. This hybrid branch integrates global representations with local features, achieving precise segmentation. To further strengthen the CNN branches, we introduce a knowledge distillation strategy that steadily feeds back information from the hybrid branch to CNN branches, mitigating overfitting risks caused by sparse supervision. SparseFormer employs credible assessment to reduce pseudolabel uncertainty, followed by continuous interaction and dynamic information enhancement among the three branches in an end-to-end training process. Extensive experiments on two benchmark datasets demonstrate that SparseFormer significantly outperforms state-of-the-art methods. Our code is available at:https://github.com/Yujia73/SparseFormer.

Accurate crop identification is crucial for agricultural trade, market risk management, and food security. Current research on automatic interannual sample extraction for crop mapping often emphasizes multisource feature fusion but overlooks the importance of feature distance differences in crop seeding processes. This study focuses on crop mapping in the Hetao Plain from 2020 to 2023, using high-resolution (HR) Sentinel-1 and Sentinel-2 remote sensing data. We introduce a method called BGSI-DFF-WMRF, which combines the bidirectional global selection index (BGSI) and distance feature fusion (DFF) under the interannual weaving net month probability random forest (WMRF). BGSI captures the coupling between phenological and spatial factors necessary for crop growth in different regions under feature fusion. Additionally, sample migration under feature fusion enhances the accuracy and representativeness of sample points across different years. WMRF integrates a monthly classifier with multisource feature distance interpolation. The BGSI-DFF-WMRF method achieved over 82% classification accuracy for crops like wheat, corn, and sunflower in the Hetao Irrigation District (HID) region, with an accuracy of 91.81% in the western area. Field samples from 2023 were successfully applied to previous years (2020–2022) through feature fusion expression (FFE) transfer. The method outperformed existing products and local statistical data, particularly for corn, demonstrating high accuracy and robustness in crop mapping. Coupling spatio-temporal factors enhances large-scale crop identification and holds great significance for the advancement and widespread adoption of large-scale crop identification techniques.

Change detection and extraction of buildings based on convolutional neural networks (CNNs) have made encouraging progress in the remote sensing community. Although these two tasks are different in objective and application scenarios, both focus on building objects. However, previous methods were accustomed to considering these two tasks separately, and the change detection task suffered from the precondition that bitemporal labeled images were used as paired supervision signals. In this study, we propose a stable prototype guided single-temporal supervised learning framework (PGLF) as a joint solution for building change detection and cross-temporal extraction by exploring two cores: knowledge commonality and task specificity. For knowledge commonality, we introduced a multi-prototype representation module (MPRM) to generate stable building prototypes from support foreground features and designed a prototype to query feature adaptive fusion (PQAF) module to suppress background noise and extract discriminative building features in a way that support prototypes-guided query feature enhancement. For task specificity, we designed a multiscale spatiotemporal interaction module (MSTI) to capture bidirectional change features with strong spatiotemporal correlations. Besides, we developed a pseudo bitemporal image pair construction method to improve the performance of building change detection and cross-temporal extraction under single-temporal supervision signals. PGLF was trained on pseudo bitemporal labeled image pairs and tested on the public aerial WHU building dataset and proposed satellite SPO building dataset. The comprehensive experimental results demonstrate the superiority of the proposed method.

The imaging process of optical remote sensing images are easily affected by external conditions. Therefore, remote sensing images under different imaging conditions often show color differences, resulting in feature distribution differences between the source and target domain, hindering the migration of semantic segmentation models between domains. Currently, most domain adaptation methods are for single-source and single-target domains. Here, we proposed a novel and concise method, coined MDANet, for the adaptation of patch images of multi-source and multi-target domains and for reducing the distribution differences of different patch images by projecting them onto the virtual center of a mixed-domain. MDANet is a lightweight and self-supervised network that can be grafted with any semantic segmentation model. Our method significantly improved the segmentation accuracy of semantic segmentation models and showed higher stability and competitiveness than existing methods.

The ice, cloud, and land elevation satellite-2 (ICESat-2) is equipped with a photon-counting laser altimeter system and demonstrates outstanding ability to measure elevations in the ever-changing earth. However, the ICESat-2 data contain several noise photons affected by solar returns, and there are no reference data of signal or noise photons for evaluating the performance of denoising algorithms. In this letter, we propose a self-adaptive denoising algorithm based on a genetic algorithm (SADA-GA) for the ICESat-2 data, which uses the real-coded genetic algorithm to adaptively search for the global optimal denoising parameters in different data sets. The SADA-GA addresses the limitation of the selection method of the two parameters K and T in the localized statistics-based algorithm that normally cannot be applied to different data sets. To evaluate the algorithm performance, we created an ICESat-2 data set named WHU-PCL and compared the SADA-GA with two classic methods. The qualitative and quantitative analyses showed that our method can extract signal photons more efficiently from different ICESat-2 data sets and achieve the$F$value of 0.99 in nighttime data. In addition, we analyzed the factors that affect the SADA-GA performance and found that the signal-to-noise ratio (SNR) is the most important parameter.

GaoFen-3 (GF-3) is a C-band multipolarization synthetic aperture radar (SAR) satellite with 12 imaging modes. However, its initial positioning accuracy remains unsatisfactory, thereby hindering its use for large-area surveying and mapping. This study proposes a block orthorectification method without ground control points (GCPs) using the GF-3 Fine strip II (FSII) mode. To address the challenges with the accuracy and efficiency of this method, an integrated block orthorectification method was developed to conduct integrated processing of large-scale GF-3 satellite images without GCPs. Geometric calibration was used to improve the absolute positioning accuracy of each SAR image. Then, several tie points (TPs) were extracted using the SAR scale-invariant feature transform (SIFT) operator. A parallel matching strategy was used in the block images registration. The block adjustment model was constructed to solve the orientation parameter of all SAR images. The experimental results of 1,468 GF-3 images of China’s entire land area show a TPs root-mean-square error of 0.724 pixel and 8.014 m for the independent checkpoint, suggesting that the proposed method can effectively improve the geometric accuracy of GF-3 satellite images and demonstrate the feasibility of large-scale SAR mapping without GCPs.

Hyperspectral remote sensing images (HSIs) have been applied in urban planning, environmental monitoring, and other fields. However, they are susceptible to noise interference, such as Gaussian noise, stripe, and mixed noises, from various factors in the imaging process, which greatly limits their applications. Although previous efforts to improve HSI quality have achieved remarkable results, there are still many challenges to be solved. To avoid the poor generalization ability and improve the stripe removal performance of the network in real scenarios. In this paper, we proposed a novel deep learning model (Translution-SNet) for HSI stripe noise removal based on a semi-supervised training strategy that applies a convolution and transformer for feature extraction. Moreover, we used an unbiased estimation method to calculate the loss function of the unsupervised part from noisy data without a clean image. The semi-supervised method improved the ability of Translution-SNet to deal with various complex stripe noises during stripe removal and strengthened its robustness and generalization ability. Our experimental results showed that Translution-SNet could robustly handle stripe noise of images with different loads and achieve satisfactory results, proving its feasibility and effectiveness. In addition, Translution-SNet showed good generalization ability.

Remote sensing images, especially hyperspectral images (HSIs), are extremely vulnerable to random noise and stripe noise. As a key aspect of HSI data quality improvement, stripe noise removal has always been a pervasive issue in remote sensing image processing. Convolutional neural networks have been applied for HSI data destriping. However, the existing methods lose the stripe-free component of the original image to a certain extent. These models also ignore the global spatial context of images and the correlation between spatial information and spectral information. Therefore, we propose a novel destriping convolutional network to overcome the problems with the existing methods. Octave convolution is used to extract cross-frequency features, and separate and compress the low-frequency information of the images, while dilation convolution (Dila-Conv) is used to reduce the amount of required calculation and also preserve the key image information. In addition, Dila-Conv can expand the receptive field to obtain multiscale features. Finally, a cross-channel enhanced spatial–spectral feature fusion module is used to acquire and integrate spatial context information and interchannel dependencies on a global scale as auxiliary information so that the network model can learn and pay attention to key feature information, specifically, “what to look for” and “where to look at,” which can facilitate the distinction between stripe and stripe-free components. Experimental results obtained using multiple datasets demonstrated that the proposed method can outperform the existing comparable methods and can produce satisfactory results in terms of visual effects and quantitative evaluation.

The forward prediction of mining subsidence in coal mining areas is key to evaluating mining risk and improving mine management plans. At present, the conditions that ensure successful application of the time function method to predict future subsidence in coal mining areas remain poorly known, and a prediction method suitable for large-scale subsidence prediction during coal mining has not been established. Based on the characteristics of logistic model and simulation experiments, we determined its predictable conditions for coal mining subsidence prediction. We propose a small baseline subset-synthetic aperture radar interferometric (SBAS-InSAR) coal mining subsidence prediction method with a predictable dynamic range based on the predictable condition obtained. The method uses time-series subsidence data obtained by SBAS-InSAR as the fitting data. The logistic model parameters are obtained pixel-by-pixel via the Levenberg–Marquardt (LM) algorithm. The predictable range is subsequently determined based on the predictable condition. Finally, future subsidence in the predictable range is predicted. The methodology was tested in two coal mining areas in Inner Mongolia–one a single working face mine and one a parallel double working face mine. The predicted results agree well with the InSAR monitoring results. The average root mean square error (RMSE) of the predicted results was 0.0119 m. In addition, we used the Knothe model to conduct comparative experiments without considering predictable conditions. The results reflect the advantages of our proposed method and the necessity of predictable conditions. The new prediction method is beneficial for risk assessment and coal mining planning.

Optical satellites are affected by factors such as seasonal and atmospheric variation, illumination, and sensor distortion. Thus, satellite images covering large-scale area often show conspicuous color differences, resulting in poor color continuity of the mosaicked satellite image. This study proposes a novel combined model color correction (CMCC) method for high-resolution optical satellite images, which constructively combines a defogging model with a radiation correction model. First, this study analyzed the feasibility of using easily available low-resolution satellite images as external references to correct the color of high-resolution images and describes the selection criteria for external references. Second, considering the negative effects of atmosphere on the color and clarity of remote sensing images, we proposed an optical satellite image enhancement method, which is based on the content characteristics of remote sensing images and the dark channel prior defogging method. Finally, we designed a two-stage color correction process: 1) correcting the color of downsampled images via low-frequency modeling and replacement and 2) mapping the color of downsampled images to original images through local modeling and super-resolution color correction. Furthermore, this study proposes an indicator of quality considered mean absolute error (QCMAE) for quantitative evaluation of the color correction result. We selected 328 Gaofen-1 (GF-1) high-resolution images for the experiments. Visual effects and statistical results of images after being processed by the proposed CMCC are both superior to the three state-of-the-art methods, which verifies the effectiveness and reliability of the proposed method.

Sensor instability, dark currents, and other factors often cause stripe noise corruption in hyperspectral remote sensing images and severely limit their application in practical purposes. Previous studies have proposed numerous destriping algorithms that have yielded impressive results. Although most destriping algorithms are based on the premise of additive noise, a few studies have focused directly on multiplicative stripe noise. This article fully analyzes the characteristics of the stripe noise of OHS-01 images and proposes a multiplicative stripe noise removal method. Specifically, stripe noise is tackled by performing radiometric normalization of different columns in the image. First, the relative gain coefficients of adjacent columns are separated based on prior knowledge. Second, the local relative intensity correspondence of the image columns are established by means of intensity propagation, intensity connection, and so on. Finally, the above-mentioned process is iterated in multiscale space, and the accumulated gain correction coefficient maps were used to correct the radiation of the original image. The results of extensive experiments on simulated and real remote sensing image data demonstrate that the proposed method can, in most cases, yield desirable results. In certain cases, the results are even better, visually, and quantitatively, than those obtained using classical algorithms. Moreover, the proposed method has high robustness and efficiency. Thus, it can conform to the requirements of engineering applications.