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Multidimensional Attention Learning for VHR remote sensing imagery recognition

IEEE GEOSCIENCE AND remote sensing LETTERS 2022年 19卷

作者： Fang, Jie Cao, Xiaoqian Han, Pengfei Wang, Dianwei Xian Univ Posts & Telecommun Sch Commun & Informa Tion Engn Ctr Imagery & Informat Proc CIIP Xian 710121 Peoples R China Shaanxi Univ Sci & Technol Sch Elect & Control Engn Xian 710021 Peoples R China

This work presents a multidimensional attention learning-based lightweight convolutional neural network for very-high-resolution (VHR) remote sensing imagery recognition, which incorporates channel attention, spatial attention, and saliency sampler attention into the backbone to improve its recognition performance. Specifically, channel attention can alleviate the interference of original feature cube by adaptively giving different coefficient weights to different feature channels. Spatial attention can emphasize the discriminative regions by using the sum of activation value in different locations of the image to weight the original feature cube. Saliency sampler attention can increase the influence of interesting regions on the final representation according to the saliency priors. In general, different from the existing methods, this work utilizes the constraints rather than the model scale to improve the recognition performances of the network. In addition, the novel flooding loss is used to optimize the network, which can improve the performance of the framework by alleviating the severe overfitting problem.

关键词： remote sensing image recognition Training Cost function Convolutional neural networks Testing Task analysis Flooding loss multidimensional attention very-high-resolution (VHR) imagery recognition

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GeoLLM-Engine: A Realistic Environment for Building Geospatial Copilots

GeoLLM-Engine: A Realistic Environment for Building Geospati...

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IEEE Computer Society Conference on Computer Vision and pattern recognition Workshops (CVPRW)

作者： Simranjit Singh Michael Fore Dimitrios Stamoulis CoStrategist R&D Group Microsoft Corporation USA

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Geospatial Copilots unlock unprecedented potential for performing Earth Observation (EO) applications through natural language instructions. However, existing agents rely on overly simplified single tasks and template-based prompts, creating a disconnect with real-world scenarios. In this work, we present GeoLLM-Engine, an environment for tool-augmented agents with intricate tasks routinely executed by analysts on remote sensing platforms. We enrich our environment with geospatial API tools, dynamic maps/UIs, and external multimodal knowledge bases to properly gauge an agent’s proficiency in interpreting realistic high-level natural language commands and its functional correctness in task completions. By alleviating overheads typically associated with human-in-the-loop benchmark curation, we harness our massively parallel engine across 100 GPT-4-Turbo nodes, scaling to over half a million diverse multi-tool tasks and across 1.1 million satellite images. By moving beyond traditional single-task image-caption paradigms, we investigate state-of-the-art agents and prompting techniques against long-horizon prompts.

关键词： Earth Geology Natural languages Benchmark testing Parallel processing Geospatial analysis Satellite images

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Detecting Adversarial Examples for Hyperspectral image Classification Via Mutation Testing

Detecting Adversarial Examples for Hyperspectral Image Class...

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Workshop on Hyperspectral image and Signal processing: Evolution in remote sensing, WHISPERS

作者： Yang Hao Zhifei Chen Liang Xiao School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China

With the widespread application of remote sensing technology in various fields, convolutional neural networks (CNNs) have exhibited remarkable performance in the field of hyperspectral image (HSI) classification. However, CNNs also introduce security concerns when demonstrating their accurate classification capabilities. By adding imperceptible and carefully designed adversarial perturbations to input images, CNN models can produce highly confident incorrect results. These images, which have adversarial perturbations added to them, are known as adversarial samples. In this work, we propose a mutation testing-based method for detecting adversarial examples. In our experiments, we employ different attack algorithms to deceive multiple high-accuracy HSI recognition models trained on multiple HSI datasets, and our method aims to determine whether the generated adversarial examples are normal or adversarial. We evaluate our method on the Indian Pines, Salinas, and University of Pavia datasets. The results demonstrate that our method can effectively detect adversarial examples generated by state-of-the-art attack methods.

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Global Shallow Groundwater patterns From Soil Moisture Satellite Retrievals

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND remote sensing 2022年 15卷 89-101页

作者： Soylu, Mehmet Evren Bras, Rafael L. Georgia Inst Technol Sch Civil & Environm Engn Atlanta GA 30332 USA

Groundwater is the most significant freshwater source and plays a critical role in the earth's water and energy balance. The lack of groundwater observations with a high spatiotemporal resolution at a global scale hinders our ability to study and model the environment when shallow groundwater has a direct impact on surface soil moisture. This study aims to estimate the spatial and temporal distributions of shallow groundwater-influenced areas at a global scale. We trained an ensemble machine learning algorithm, using outputs from a variably saturated soil moisture flux model, to identify the shallow groundwater occurrence. Model simulations spanned various climate zones and soil types across the globe. The overall accuracy of the algorithm in reproducing the soil moisture flux model results was 95.5%. We applied the algorithm to spaceborne soil moisture observations retrieved by NASA's SMAP satellite and present a global-scale shallow groundwater map derived from the SMAP observations. The derived global distribution of shallow groundwater identifies wetlands, large riparian corridors, and seasonally inundated lowlands. The results showed that 19% of terrestrial land cover had been influenced by shallow groundwater at some point in time during the period of interest (2015-2018). Temporally, shallow groundwater follows an annual cyclic pattern with 2% to 6% of the land surface being influenced globally. This study shows that SMAP observations could be used in estimating shallow groundwater in high spatiotemporal resolution at a global scale, potentially providing invaluable inputs for modeling and environmental monitoring studies.

关键词： Soil moisture Atmospheric modeling Soil Biological system modeling Spatial resolution Moisture Meteorology Hydrology image processing machine learning shallow groundwater soil moisture active passive (SMAP) soil moisture

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A novel Traffic Sign Dataset with Condition Annotations

A novel Traffic Sign Dataset with Condition Annotations

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2023 IEEE Symposium Series on Computational Intelligence, SSCI 2023

作者： Sandhu, Hanni Kuhne, Joana Sawade, Oliver Stellmacher, Martin Matthes, Elmar Hellwich, Olaf Iav GmbH Camera-based Adas & Its Berlin Germany Technical University Berlin Computer Vision & Remote Sensing Berlin Germany

ISBN: (纸本)9781665430654

To develop robust and secure automated transportation systems, Traffic Sign Detection and recognition (TSDR) is a key part. It plays a crucial role in Advanced Driver Assistance Systems (ADAS), self-driving vehicles and traffic safety. However, the task of TSDR can be challenging due to traffic signs being subject to damages, discoloration, vandalism and occlusion. Even though a lot of progress is made in both research areas of Traffic Sign Detection (TSD) and Traffic Sign recognition (TSR), no study explicitly deals with the problem of qualitative poor traffic signs appearing in real-world scenarios. This can be assigned to the lack of an extensive traffic sign dataset containing flawless signs as well as imperfect signs. Neural networks trained exclusively on untainted data might fail at detecting flawed signs as they occur in real-world scenarios. Therefore, in this paper, a novel traffic sign dataset with condition annotations is proposed, indicating if a sign is good, discolored, vandalized, dirty or occluded. The custom dataset is created with a semi-supervised approach, in which machine learning models are trained to classify traffic signs in the condition categories. The resulting dataset can be used as basis for more precise traffic sign recognition as well as traffic sign condition classification which can be useful for maintenance planning. The dataset includes approx. 20.000 images of 10 sign classes, where 70% of data is incorporated in the training set, 10% in the validation set and 20% in the test set. © 2023 IEEE.

关键词： Deep Learning image processing Transportation and Vehicle Systems

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Supervised Semantic Segmentation of Urban Area Using SAR

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remote sensing 2025年第9期17卷 1606-1606页

作者： Pluto-Kossakowska, Joanna Wangiyana, Sandhi Warsaw Univ Technol Fac Geodesy & Cartog PL-00661 Warsaw Poland Warsaw Univ Technol Fac Elect & Informat Technol PL-00661 Warsaw Poland

Cyclical analyses of dynamic changes in urban areas are critical and necessary for policymakers and societies. remote sensing data processing methods are currently in place to determine the distribution of built-up and sealed areas on global and continental scales. However, there is a lack of research on distinguishing among urban classes at a larger scale for a city or its district. SAR sensors register features of urban areas that, when further processed, such as textures, can help in automatic recognition. We present a novel dataset for urban classification focusing on density analysis. Machine learning methods, including a selection of artificial neural networks and other classifiers, have been used to distinguish among different classes of built-up areas, as defined according to the Urban Atlas database. This dataset was used to establish benchmarks for classification, conduct verification tests, and evaluate accuracy. The C-band of Sentinel-1 images, for the same study areas as ICEYE X-band images and their texture derivatives, were used to classify variants. Better results were obtained using the CNN-based Unet model. The best overall accuracy was 79% for the X-band and 73% for the C-band datasets. The results indicate that the single-polarization X-band is more suitable for this classification despite the presence of more SAR features in the C-band with dual polarization.

关键词： urban density urban area classification texture image GLCM CNN

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Adaptive hybrid attention network for hyperspectral image classification *

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pattern recognition LETTERS 2021年 144卷 6-12页

作者： Pande, Shivam Banerjee, Biplab Indian Inst Technol Ctr Studies Resources Engn Mumbai 400076 Maharashtra India

The task of land cover classification from hyperspectral images (HSI) has recently witnessed sharp improvements with the applications of deep convolutional networks (CNN). This is mainly attributed to the hierarchical spectral-spatial feature learning capabilities of the CNN models. However, it is important to encode the short to long range spatial dependencies to predict the pixelwise labels without inducing additional redundancy at the feature level. The standard CNN based land cover classification models for HSI overlook this aspect in general. To overcome this, we propose a hybrid attention based 3D classification model for hyperspectral images. Our model comprises of 1D and 2D CNNs to individually generate the attention masks that respectively highlight the spectral and spatial characteristics of our input image. For optimal learning the spectral and spatial features in our model are combined adaptively with the original input and sent to the 3D classification module. To enhance the classification performance, we incorporate classwise Wasserstein loss alongwith the crossentropy loss. Our methods are evaluated on three widely used hyperspectral datasets: Houston datasets (DFC-2013 and DFC-2018) and Salinas dataset, and has satisfactorily outperformed all the prior benchmark methods. (c) 2021 Elsevier B.V. All rights reserved.

关键词： Hyperspectral image classification remote sensing Spectral-spatial attention Wasserstein loss

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remote sensing image Registration Based on Local Affine Constraint With Circle Descriptor

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IEEE GEOSCIENCE AND remote sensing LETTERS 2022年 19卷

作者： Zhang, Haipeng Leng, Chengcai Yan, Xiao Cai, Guorong Pei, Zhao Yu, Naigong Basu, Anup Nanchang Hangkong Univ Sch Math & Informat Sci Nanchang 330063 Peoples R China Northwest Univ Sch Math Xian 710127 Peoples R China Nanjing Univ Sci & Technol Sch Automat Nanjing 210094 Peoples R China Jimei Univ Coll Comp Engn Xiamen 361021 Peoples R China Shaanxi Normal Univ Sch Comp Sci Xian 710119 Peoples R China Beijing Univ Technol Fac Informat Technol Beijing 100124 Peoples R China Univ Alberta Dept Comp Sci Edmonton AB T6G 2E8 Canada

Many methods have been developed to improve the performance of image registration. In this letter, we introduce a novel method based on a local affine constraint for remote sensing image registration, which can be widely used in image processing and pattern recognition. Our algorithm has three components. First, we exploit the scale invariant feature transform (SIFT) method to extract feature points and calculate the gradient magnitude to establish feature descriptors with a circular instead of square neighborhood. Second, an initial matching is implemented by the nearest neighbor distance ratio (NNDR) and the fast sample consensus (FSC) algorithm. Finally, fine registration is established using more correct matches obtained by the local affine transformation circular region search algorithm. Experimental results show that the proposed method achieves subpixel accuracy. In addition, both the correct matching rate and registration demonstrate the effectiveness and efficiency of our method.

关键词： image registration Feature extraction remote sensing Histograms Power capacitors Robustness image edge detection Feature extraction image registration scale invariant feature transform (SIFT)

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Fully convolutional recurrent networks for multidate crop recognition from multitemporal image sequences

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ISPRS JOURNAL OF PHOTOGRAMMETRY AND remote sensing 2021年 171卷 188-201页

作者： Chamorro Martinez, Jorge Andres Cue La Rosa, Laura Elena Feitosa, Raul Queiroz Sanches, Ieda Del'Arco Happ, Patrick Nigri Pontifical Catholic Univ Rio de Janeiro BR-22451900 Rio De Janeiro Brazil Natl Inst Space Res BR-12227010 Sao Jose Dos Campos Brazil

Crop recognition in tropical regions is a challenging task because of the highly complex crop dynamics, with multiple crops per year. Nevertheless, most automatic methods proposed thus far are devoted to temperate areas where normally a single crop is cultivated along the crop year. This paper introduces convolutional recurrent networks for crop recognition in areas characterized by complex spatiotemporal dynamics typical of tropical agriculture, where a per date classification is required. The proposed networks consist of two sequential steps. First, a deep network simultaneously models spatial and temporal contexts. Second, a post-processing algorithm enforces prior knowledge about the crop dynamics in the target area based on the posterior probabilities computed in the first step. The paper proposes deep network architectures that join a fully convolutional network (FCN) for modeling spatial context at multiple levels and a bidirectional recurrent neural network to explore the temporal context. The recurrent network is configured as N-to-N, where N is the sequence length. This allows it to produce classification outcomes for the entire sequence of multi-temporal images using a single network. Different network designs are proposed based on three FCN architectures: U-Net, dense network, and Atrous Spatial Pyramid Pooling. A convolutional Long-Short-Term-Memory (ConvLSTM) accounts for sequence modeling, whereas the Most Likely Class Sequence (MLCS) algorithm is adopted for enforcing prior knowledge. The paper finally reports experiments conducted on Sentinel-1 data of two publicly available datasets from different tropical regions. The experimental results indicated that the proposed architectures outperformed state-of-the-art methods based on recurrent networks in terms of Overall Accuracy and per-class F1 score.

关键词： Convolutional recurrent networks Fully convolutional networks Recurrent networks Crop recognition Deep learning remote sensing

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Study on Modeling Method of Forest Tree image recognition Based on CCD and Theodolite

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IEEE ACCESS 2020年 8卷 159067-159076页

作者： Shi, Yeqiong Wang, Sainan Zhou, Shuna Kamruzzaman, M. M. Northeast Forestry Univ Harbin 150040 Peoples R China Harbin Normal Univ Harbin 150025 Peoples R China Shenyang Inst Sci & Technol Dept Informat & Control Engn Shenyang 110100 Peoples R China Inner Mongolia Univ Manzhouli Coll Manzhouli 021400 Peoples R China Jouf Univ Dept Comp & Informat Sci Sakaka 72311 Saudi Arabia

Forest vegetation is the main body that constitutes forest resources. Accurate identification of the types of forest trees can lay the foundation for the research and utilization of forest resources. With the development of remote sensing technology, traditional optical remote sensing can only describe the horizontal pattern of ground features, which makes it difficult to identify single tree species. Therefore, it is of great significance to study the method of forest tree image recognition. This article mainly studies the forest image recognition system based on CCD and theodolite. In this article, the forest image recognition system based on CCD and theodolite uses near, middle, and far CCD cameras to detect the infrared radiation of the target and collect the target image. The image processing algorithm is designed for the image processing module, and the flow chart of the image processing algorithm is given. The processing function has designed the interface of the image processing module. The image processing module can extract the main information of the target from simple background and complex background. In this article, an experimental optical path is built, the forest image recognition simulation platform is verified, and the data obtained from the experiment is processed. The implemented color detection algorithm can achieve a detection accuracy rate of more than 91% for forest tree image recognition detection. The test results show that the image acquisition, transmission and display functions of the camera system realized by this subject are normal, and the system can achieve accurate recognition of the target.

关键词： CCD forest tree image image preprocessing image processing system image recognition

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