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Automatic recognition of debris rock lithology based on unsupervised semantic segmentation

COMPUTERS & GEOSCIENCES 2025年 196卷

作者： Qin, Shengda Wang, Qing Zeng, Qihong Ye, Maolin Fu, Anqi Chen, Guanzhou Yangtze Univ Sch Geosci Wuhan 430100 Peoples R China Hunan Univ Sci & Technol Hunan Prov Key Lab Geoinformat Engn Surveying Mapp Xiangtan 411201 Peoples R China PetroChina Res Inst Petr Explorat & Dev Beijing Peoples R China Wuhan Univ State Key Lab Informat Engn Surveying Mapping & Re Wuhan 430079 Peoples R China

Accurate identification of lithology in debris rock is crucial for optimizing resource development in geological exploration and the oil and gas industry. The traditional approach, which depends on experts manually analyzing remote sensing images, is not only laborious but also vulnerable to subjectivity. In contrast, supervised learning, although highly automated, is limited by the need for large-scale annotated data and sample imbalance issues. In our proposed unsupervised semantic segmentation method, automatic segmentation of rock images not only improves the efficiency and accuracy of lithology recognition but also reduces human errors, providing an effective solution for automated lithology analysis. We collected a large amount of debris rock data from the Qingshuihe-Karazha using remote sensing satellites and used an improved FCN network combined with superpixel segmentation to generate pseudo labels instead of manual labeling, achieving unsupervised segmentation. We compared this method with traditional K-Means, ISODATA, and CNN +K-Means pseudo-label generation methods. By calculating evaluation metrics named ARE, AMI, and FMI, which are used for unsupervised semantic segmentation methods, we found that our method maintains high consistency and robustness in various image sizes, especially when the size of debris rock images is large, and its stability is superior. At the same time, we addressed the boundary issues caused by the need for block division in the lithology image of ultra-large debris rocks, as well as the problem of a large number of similar blocks after block division. The efficiency and accuracy of this method in lithology identification were determined, providing more convenient and efficient data processing methods for geological researchers.

关键词： Lithology recognition Deep learning Unsupervised learning High-resolution remote sensing images

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Unsupervised remote sensing image thin cloud removal method based on contrastive learning

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IET image processing 2024年第7期18卷 1844-1861页

作者： Tan, Zhan Cong Du, Xiao Feng Man, Wang Xie, Xiao Zhu Wang, Gui Song Nie, Qin Xiamen Univ Technol Sch Comp & Informat Engn Xiamen 361024 Peoples R China Xiamen Univ Technol Inst Spatial Informat Technol Xiamen Peoples R China Fujian Key Lab Pattern Recognit & Image Understand Xiamen Peoples R China

Cloud removal algorithm is a crucial step of remote sensing image preprocessing. The current mainstream remote sensing image cloud removal algorithms are implemented based on deep learning, and most of them are supervised. A large number of data pairs are required for training to achieve cloud removal. However, real with/without cloud image pairs datasets are difficult to obtain in the real world, and the models obtained by training on synthetic datasets often need to generalize better to natural scenes. And the existing unsupervised thin cloud removal methods based on Cycle-GAN framework with considerable model complexity and unstable training are not an excellent solution to the problem of lack of paired datasets. Based on this, in this paper, the authors propose an unsupervised remote sensing image thin cloud removal method based on contrastive learning-GAN-UD. It is a network consisting of a frequency-spatial attention generator and a discriminator. In addition, the authors introduce local contrastive loss and global content loss to constrain the content of the generated images to ensure that the generated cloud-free images are consistent with the input cloud images in terms of image content. Experimental results show that the proposed method in this paper can still effectively remove thin clouds from remote sensing images without paired training datasets, outperforms current unsupervised cloud removal methods, and achieves comparable performance to supervised methods. The authors propose an unsupervised remote sensing image thin cloud removal method based on contrastive learning-Generative Adversarial Network of Unpair Data. It is a network consisting of a frequency-spatial attention generator and a discriminator. In addition, the authors introduce local contrastive loss and global content loss to constrain the content of the generated images to ensure that the generated cloud-free images are consistent with the input cloud images in terms of image content. ima

关键词： image reconstruction remote sensing unsupervised learning

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Scientific School of Academician VA Soifer in the Field of processing, Analysis, and recognition of images and Optical Signals

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pattern recognition AND image ANALYSIS 2023年第4期33卷 1080-1103页

作者： Ilyasova, N. Yu. Sergeyev, V. V. Demin, N. S. Russian Acad Sci Image Proc Syst Inst Branch Fed Sci Res Ctr Crystallog & Photon Samara 443001 Russia Samara Univ Samara 443080 Russia

This article is the first in a series of publications dedicated to the leading scientific school of Academician V.A. Soifer in the field of processing, analysis, and recognition of images and optical signals. The article briefly describes the creation and development of the Samara scientific school of computer image processing. Examples of obtained fundamental results and solved applied problems are given. The most significant publications of the scientific school are listed and analyzed.

关键词： computer optics image processing nanophotonics diffraction optics Earth remote sensing biomedical research

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Advances in Deep Learning Applications for Plant Disease and Pest Detection: A Review

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remote sensing 2025年第4期17卷 698-698页

作者： Wang, Shaohua Xu, Dachuan Liang, Haojian Bai, Yongqing Li, Xiao Zhou, Junyuan Su, Cheng Wei, Wenyu Hainan Aerosp Informat Res Inst Key Lab Earth Observat Hainan Prov Sanya 572029 Peoples R China Chinese Acad Sci Aerosp Informat Res Inst State Key Lab Remote Sensing & Digital Earth Beijing 100101 Peoples R China Lanzhou Jiaotong Univ Fac Geomat Lanzhou 730070 Peoples R China Univ Chinese Acad Sci Coll Resources & Environm Beijing 100049 Peoples R China Lanzhou Jiaotong Univ Sch Architecture & Urban Planning Lanzhou 730070 Peoples R China

Traditional methods for detecting plant diseases and pests are time-consuming, labor-intensive, and require specialized skills and resources, making them insufficient to meet the demands of modern agricultural development. To address these challenges, deep learning technologies have emerged as a promising solution for the accurate and timely identification of plant diseases and pests, thereby reducing crop losses and optimizing agricultural resource allocation. By leveraging its advantages in image processing, deep learning technology has significantly enhanced the accuracy of plant disease and pest detection and identification. This review provides a comprehensive overview of recent advancements in applying deep learning algorithms to plant disease and pest detection. It begins by outlining the limitations of traditional methods in this domain, followed by a systematic discussion of the latest developments in applying various deep learning techniques-including image classification, object detection, semantic segmentation, and change detection-to plant disease and pest identification. Additionally, this study highlights the role of large-scale pre-trained models and transfer learning in improving detection accuracy and scalability across diverse crop types and environmental conditions. Key challenges, such as enhancing model generalization, addressing small lesion detection, and ensuring the availability of high-quality, diverse training datasets, are critically examined. Emerging opportunities for optimizing pest and disease monitoring through advanced algorithms are also emphasized. Deep learning technology, with its powerful capabilities in data processing and pattern recognition, has become a pivotal tool for promoting sustainable agricultural practices, enhancing productivity, and advancing precision agriculture.

关键词： deep learning disease detection plant diseases and pests image classification object detection convolutional neural network

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remote sensing image scene recognition based on densenet-169

Remote sensing image scene recognition based on densenet-169

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2024 International Conference on image, Signal processing, and pattern recognition, ISPP 2024

作者： Cui, Xiyue School of Information Science and Technology Dalian Maritime University Dalian116026 China

ISBN: (纸本)9781510680425

Scene classification has become an effective technique for classifying high spatial resolution remote sensing ***, in the traditional deep learning convolutional neural network, as the remote sensing image passes through the layers in the network, some of the features will be gradually lost, resulting in a significant decrease in the classification accuracy and precision of scene recognition, and there is the problem of underutilization of *** addition, remote sensing images themselves have high *** overcome these challenges, we adopt the DenseNet ***, we first train remote sensing images from the UCMerced dataset as network ***, we introduced the DenseNet-169 model based on migration *** with DenseNet-121, DenseNet-169 has more neural network layers, and this difference is mainly manifested in the number of convolutional layers in the dense ***-169 has more convolutional layers, which increases the complexity and the number of parameters of the model, bringing the following advantages: stronger expressive power, which enables the extraction of more complex feature patterns;faster training time, thanks to the densely-connected nature, which efficiently utilizes the gradient flow;and better generalization ability, especially on large-scale complex *** our experiments, the introduced DenseNet-169 shows excellent performance compared to other state-of-the-art deep convolutional networks on the UCMerced dataset, with an accuracy of 95.14%, a precision of 95.31%, a Kappa coefficient of 94.90%, and an F1-score of 95.11%.The experimental results show that the method can make full use of the features of remote sensing images and show good visual effect, providing a good method for scene recognition. © 2024 SPIE.

关键词： Convolutional neural networks

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RSSFormer: Foreground Saliency Enhancement for remote sensing Land-Cover Segmentation

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IEEE TRANSACTIONS ON image processing 2023年 32卷 1052-1064页

作者： Xu, Rongtao Wang, Changwei Zhang, Jiguang Xu, Shibiao Meng, Weiliang Zhang, Xiaopeng Chinese Acad Sci Inst Automat Sch Artificial Intelligence Natl Lab Pattern Recognit Beijing 100190 Peoples R China Beijing Univ Posts & Telecommun Sch Artificial Intelligence Beijing 100876 Peoples R China

High spatial resolution (HSR) remote sensing images contain complex foreground-background relationships, which makes the remote sensing land cover segmentation a special semantic segmentation task. The main challenges come from the large-scale variation, complex background samples and imbalanced foreground-background distribution. These issues make recent context modeling methods sub-optimal due to the lack of foreground saliency modeling. To handle these problems, we propose a remote sensing Segmentation framework (RSSFormer), including Adaptive TransFormer Fusion Module, Detail-aware Attention Layer and Foreground Saliency Guided Loss. Specifically, from the perspective of relation-based foreground saliency modeling, our Adaptive Transformer Fusion Module can adaptively suppress background noise and enhance object saliency when fusing multi-scale features. Then our Detail-aware Attention Layer extracts the detail and foreground-related information via the interplay of spatial attention and channel attention, which further enhances the foreground saliency. From the perspective of optimization-based foreground saliency modeling, our Foreground Saliency Guided Loss can guide the network to focus on hard samples with low foreground saliency responses to achieve balanced optimization. Experimental results on LoveDA datasets, Vaihingen datasets, Potsdam datasets and iSAID datasets validate that our method outperforms existing general semantic segmentation methods and remote sensing segmentation methods, and achieves a good compromise between computational overhead and accuracy.

关键词： remote sensing Transformers Semantic segmentation Task analysis Buildings Background noise Convolution remote sensing segmentation foreground saliency enhancement transformer

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Visual grounding of remote sensing images with multi-dimensional semantic-guidance

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pattern recognition LETTERS 2025年 189卷 85-91页

作者： Ding, Yueli Wang, Di Li, Ke Zhao, Xiaohong Wang, Yifeng Xidian Univ 2 South Taibai Rd Xian 710071 Shannxi Peoples R China

Visual grounding in remote sensing images aims to accurately locate specified targets based on query expressions. Existing methods often use separate feature extractors to independently process visual and textual features. However, this approach results in initial features that lack correlation between the two modalities, hindering effective feature fusion and limiting localization precision. To address this challenge, we propose a novel framework called MSVG, which enhances visual grounding accuracy through a multidimensional text-image alignment module and a visual enhancement fusion module. The multi-dimensional text-image alignment module employs both channel-wise and spatial-wise alignment at various stages of visual feature extraction, guiding the generation of visual features in a manner that increases their relevance to the accompanying textual descriptions. Meanwhile, the visual enhancement fusion module refines feature relevance by learning contextual features, effectively excluding objects and backgrounds unrelated to the target. Experiments demonstrate that our approach achieves a remarkable accuracy of 83.61% on DIOR-RSVG dataset, representing a substantial 6.83% improvement over previous methods and setting a new benchmark.

关键词： Visual Grounding remote sensing Attention

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A fusion algorithm of SAR and panchromatic remote sensing images for intertidal extraction

A fusion algorithm of SAR and panchromatic remote sensing im...

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2024 International Conference on image, Signal processing, and pattern recognition, ISPP 2024

作者： Li, Wenchao Liu, Sheng Ding, Hao He, Liang Chen, Zhenhua Li, Qingliang Xue, Kaichuang China Wenchang Spacescraft Launch Site 799 Shugang Ave Hainan Wenchang471300 China National University of Defense Technology 109 Deya Road Hunan Changsha410073 China

ISBN: (纸本)9781510680425

Intertidal terrain extraction based on satellite remote sensing images is of great military application and civilian value. In this paper, we propose a fusion algorithm based on SAR and optical remote sensing image to enhance intertidal feature. With the SAR and panchromatic images from different phases, we use an improved shift-invariant discrete wavelet transform algorithm to obtain higher quality images that can not only highlight the shape of the intertidal zone, but also highlight the characteristics of the coastline. The real data experiment and comparison experiments with other algorithms show that the algorithm proposed in this paper is effective in enhancing the features of the intertidal zone. © 2024 SPIE.

关键词： Optical remote sensing

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Optical Camera/SAR Consistent Imaging Alignment Method Based on SAR Imaging Compensation Under Airborne Co-Aperture System

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IEEE GEOSCIENCE AND remote sensing LETTERS 2025年 22卷

作者： Wang, Ke Wang, Yinshen Song, Chong Wang, Bingnan Xiang, Maosheng Yao, Yuan Chinese Acad Sci Aerosp Informat Res Inst Natl Key Lab Microwave Imaging Beijing 100094 Peoples R China Univ Chinese Acad Sci Sch Elect Elect & Commun Engn Beijing 101408 Peoples R China Chinese Acad Sci Changchun Inst Opt Fine Mech & Phys State Key Lab Dynam Opt Imaging & Measurement Changchun 130033 Peoples R China

Taking the advantages of optical camera and synthetic aperture radar (SAR) to perform complementary imaging is a research hot spot in remote sensing image processing, which can be used for target recognition and detection after image registration and fusion. Optical/SAR images acquired by noncommon aperture system have temporal and spatial inconsistencies, there are also significant differences in imaging and radiation mechanism, which make the image-domain alignment process complicated, and the alignment effect and accuracy are also limited. Therefore, this thesis proposes to acquire synchronal optical/SAR heterogeneous data by optics/frequency separation technology under the airborne co-aperture system and puts forward a consistent imaging alignment method based on data domain, which unifies optical photographs and SAR imaging results into the same pixel coordinate system and then establishes the theoretical relationship between the pixel deviation of the two and the position deviation of SAR imaging, finally achieves consistent imaging alignment with higher resolution optical photographs as a reference through the deviation compensation during SAR imaging process. Experimental simulations verified the feasibility of achieving consistent imaging alignment of optical camera/SAR within one pixel in distance and two pixels in azimuth based on SAR imaging compensation, which opens up a new direction for the enhancement of imaging processing efficiency of heterogeneous data and high-precision image processing.

关键词： Imaging Optical sensors Optical imaging Azimuth Adaptive optics Cameras Synthetic aperture radar remote sensing Apertures Radar polarimetry Airborne co-aperture system consistent imaging processing optical camera/synthetic aperture radar (SAR) alignment in data domain

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Forward-Forward Algorithm for Hyperspectral image Classification

Forward-Forward Algorithm for Hyperspectral Image Classifica...

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IEEE/CVF Conference on Computer Vision and pattern recognition (CVPR)

作者： Reyes-Angulo, Abel A. Paheding, Sidike Michigan Technol Univ 1400 Townsend Dr Houghton MI 49931 USA Fairfield Univ 1073 N Benson Rd Fairfield CT 06824 USA

ISBN: (纸本)9798350365474

The back-propagation algorithm has long been the defacto standard in optimizing weights and biases in neural networks, particularly in cutting-edge deep-learning models. Its widespread adoption in fields like natural language processing, computer vision, and remote sensing has revolutionized automation in various tasks. The popularity of back-propagation stems from its ability to achieve outstanding performance in tasks such as classification, detection, and segmentation. Nevertheless, back-propagation is not without its limitations, encompassing sensitivity to initial conditions, vanishing gradients, overfitting, and computational complexity. The recent introduction of a forward-forward algorithm (FFA), which computes local goodness functions to optimize network parameters, alleviates the dependence on substantial computational resources and the constant need for architectural scaling. This study investigates the application of FFA for hyperspectral image classification. Experimental results and comparative analysis are provided with the use of the traditional back-propagation algorithm. Preliminary results show the potential behind FFA and its promises.

关键词： Forward-Forward Algortihm Hyperspectral image Classification remote sensing

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