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Proceedings - 2021 International conference on signal processing and Machine Learning, CONF-SPML 2021

Proceedings - 2021 International Conference on Signal Proces...

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2021 International conference on signal processing and Machine Learning, CONF-SPML 2021

ISBN: (纸本)9781665417341

The proceedings contain 65 papers. The topics discussed include: coarse-to-fine loss based on Viterbi algorithm for weakly supervised action segmentation;key parameter mapping method of signal-to-noise ratio from link simulation to system simulation;research on modeling and control of closed-loop fiber optic gyroscope;design of photoelectric signal parameter test system for liquid crystal filters;an overview of recommender systems and its next generation: context-aware recommender systems;examination and categorization of keywords associated with online review sorting;performance review of generative adversarial network for a bi-directional task;an overview on remote sensing image classification methods with a focus on support vector machine;and research on the consensus problem of multi-agent systems via event-triggered mechanism.

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Digital signal processing of SAR Data for Surface Water Classification Using remote sensing Network 2

Digital Signal Processing of SAR Data for Surface Water Clas...

引用

2nd International conference on Electronics and Sustainable Communication Systems, ICESC 2021

作者： Purushotham, U. Abhishek, D K Chethan, K.S. PES University Dept of Electronics and Communication Bengaluru India RVITM Dept of Electronics and Communication Benagluru India

ISBN: (纸本)9781665428675

A semi-automated approach was adopted for mapping the water spread area in the tanks and ponds using microwave remote sensing data from Sentinel-1 Synthetic Aperture Radar (SAR) C band imagery. The SAR data was preferred due to its distinctive imaging ability in all weather conditions. However, the images obtained from SAR contain noises because of the complexities of data acquisition, landscape conditions, etc. Hence there is a necessity to improve the pre and post processing of SAR data which involved orbital correction, reduction of thermal and noise, executing radiometric calibration, and terrain correction. In this study, the first Sentinel-1 C band SAR raw signals were transformed into an image by applying standard image compression techniques and the transformed image was further calibrated for removing the radiometric and geometric errors. Additionally, the speckle noise reduction was carried out with four filter techniques namely Median, Gamma Map, IDAN, and Lee. The analysis results show that the Lee filter reduced the speckle noises better than the other three filters without compromising with the image sharpness and contrast which resulted in producing better quality SAR images. The digital classification of enhanced SAR images using supervised algorithms was performed for identification of surface water body which has provided appreciable results. The threshold-based image classification technique has provided a better accuracy of 90% for the successful extraction of water bodies. © 2021 IEEE.

关键词： Synthetic aperture radar

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Camera Intrinsic Calibration Based on Star Points 12

Camera Intrinsic Calibration Based on Star Points

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12th International conference on signal processing Systems

作者： Zhang, Chunxiao Zhou, Nan Beijing Inst Space Mech & Elect Beijing Key Lab Adv Opt Remote Sensing Technol Beijing Peoples R China

ISBN: (纸本)9781510642768;9781510642751

Aiming at the intrinsic calibration of infrared cameras with certain atmospheric absorption bands, this paper proposes intrinsic-parameter correction methods based on the angular invariance of the infrared-star calibration points. The four cases of inner product cosine invariant and outer product sine invariant under the image model with or without distortion are compared and analyzed. According to the experimental results, the outer product sine invariant has higher correction accuracy due to the higher linearity for small angles, while the image model without distortion is more sensitive to the noise of star-centroid extraction, and the intrinsic calibration error is large. In addition, the experiment also proved that the noise of the star-centroid extraction should be controlled within half a pixel as much as possible;otherwise the accuracy of the intrinsic calibration may be reduced when the distortion in the field of view is severe. Experiments show that the sine-invariant correction algorithm under the distortion model is very suitable for high-sensitivity infrared camera intrinsic calibration using star points as a large number of control points.

关键词： Intrinsic calibration star control points star angular distance

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AN END-TO-END ADVERSARIAL HASHING METHOD FOR UNSUPERVISED MULTISPECTRAL remote sensing image RETRIEVAL

AN END-TO-END ADVERSARIAL HASHING METHOD FOR UNSUPERVISED MU...

引用

IEEE International conference on image processing (ICIP)

作者： Chen, Xuelei Lu, Cunyue Shanghai Jiao Tong Univ Dept Instrument Sci & Engn Shanghai Peoples R China

ISBN: (纸本)9781728163956

Unlike natural images, remote sensing images are usually multispectral. And the lack of sufficient labeled data puts a limit on supervised learning for remote sensing image retrieval. In this paper, we propose a novel method for unsupervised multispectral remote sensing image retrieval. The proposed method makes use of the unsupervised representation learning ability of GAN. Meanwhile, a new reconstruction loss exploits the latent codes in GAN to make the final output informative and representative. Transfer learning and color histograms are used to generate an estimated similarity matrix to further guide the training. Hash constraints can make the output codes binary and compact. In the testing stage, the hash codes of multispectral images can be computed in an end-to-end manner. Experiments on a multispectral remote sensing image dataset, EuroSAT [1], show the superiority of the proposed method over other state-of-the-art methods.

关键词： image retrieval remote sensing GAN learning to hash unsupervised learning

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Illigal irrigation Mapping in Oases Using Optical and radar Time-Series Data and Machine-Learning Classifiers

Illigal irrigation Mapping in Oases Using Optical and radar ...

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International conference on Advanced Technologies for signal and image processing (ATSIP)

作者： Zeineb Kassouk Ferdaws Akacha Wafa Chebbi Hamadi Habaieb Atfa Saad Zohra Lili Chabaane National Agronomic Institute of Tunisia Lr17AGR01(Lr GREEN-TEAM) 43 AV. Charles Nicolle 1082 Carthage university Tunis Tunisia Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ) GmbH (GIZ- Tunisia) Rue Le Grand Boulevard de la Corniche Immeuble le Grand Boulevard Citeles Pins 1053 Berges du lac Tunis Tunisia

ISBN: (数字)9798350351484

ISBN: (纸本)9798350351491

The scarcity of water and the need for the irrigation of the oases leading to a significant overexploitation of the groundwater. The detection of irrigated areas based on illicit drilling is a well-resolved problem in Tunisia. The goal of thus paper is to explore the potential of optical and radar remote sensing data and machine learning in the identification and quantification of oases areas irrigated from illicit boreholes in the governorate of Kebili identification. We used a synergy of time-series optic Sentinel2(S2) and radar Sentinel1 (S1) data acquired from February to June 20022. The retrieved Oases land cover classes included three categories of stratums: 1 stratum (date Palm), 2 stratums (date palm and associated crops), 3 stratums (date palm, fruit trees and crops). In the first time, a hierarchical object-based classification approach was processed using the combined data derived from the normalized vegetation index (NDVI) and the 3 principal components (PCA1, 2 and 4) of the S2 time-series dataset. Three classifiers tested the random forest (R F), support vector machine (SVM) and k-nearest neighbor (KNN) classifiers, respectively using 40 samples of ground control data. The RF classifier performs better than SVM and KNN, with highest overall accuracy of 92% and a Kappa coefficient of 0.87. Obtained The Rf result map were crossed to the soil moisture map derived from S1 data to obtain irrigated crops outside the legal irrigated areas The result retained following this approach was compared to illegal drillings identified in the field survey and legal drilling and confirm more than 90% of illicit drillings. Furtherer more illegal extensions represent five times the area of the legal Public Irrigated Perimeters (PPI). The findings of this study might be useful for policy makers as well as planners.

关键词： Drilling Support vector machines Radar remote sensing Laser radar Normalized difference vegetation index Law Soil moisture

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The Impact of Non-uniform Label Noise on the Classification of Hyperspectral images 6

The Impact of Non-uniform Label Noise on the Classification ...

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6th International conference on signal and image processing, ICSIP 2021

作者： Li, Meizhu Huang, Shaoguang Pizurica, Aleksandra TELIN-GAIM Ghent University Belgium Department of Telecommunications and Information Processing Ghent Belgium

ISBN: (纸本)9780738133737

Erroneous labels affect the learning models in supervised classification, deteriorate the classification performance and hinder thereby subsequent tasks. These erroneous labels are referred to as label noise. The influence of label noise on the classification performance has been so far mainly studied using simulations with a uniform distribution of noisy labels accross the classes. In this paper, we propose a new label noise simulation approach for hyperspectral images (HSIs) where similar classes have a higher chance to be mixed up. Under such a realistic label noise simulation model, we compare the behaviour of different types of classifiers for HSI in remote sensing, including traditional machine learning and deep learning approaches. Our analysis reveals which levels of label noise are acceptable for a given tolerance in the classification accuracy and how robust are different learning models in this respect. © 2021 IEEE.

关键词： image classification

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Listless Block Cube Tree Coding for Low Resource Hyperspectral image Compression Sensors

Listless Block Cube Tree Coding for Low Resource Hyperspectr...

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International conference on Multimedia, signal processing and Communication Technologies, (IMPACT)

作者： Harshit Chandra Shrish Bajpai CE Department Faculty of Engineering and Information Technology Integral University Lucknow Uttar Pradesh India

ISBN: (纸本)9781665476485

Hyperspectral (HS) image has rich spectral information content, which facilitates multiple applications including remote sensing. Due to the big data size of the HS image, compression is a required process for the efficiency of image storage and transmission. However, the complexity of the compression algorithms turns real-time compression into a very challenging task. A novel listless set partitioned hyperspectral image compression algorithm is proposed. The proposed compression algorithm uses zero block cube tree structure to exploit the inter and intra sub-band correlation to achieve the compression. From the result, it has been clear that the proposed compression algorithm has low coding complexity with at-par coding efficiency. Thus, it can be a suitable contender for low-resource hyperspectral image sensors.

关键词： image sensors image coding Correlation signal processing algorithms Transforms signal processing Computational complexity

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Monitoring deformation of Genhe-Labudalin Highway using Sentinel-1 InSAR analysis

Monitoring deformation of Genhe-Labudalin Highway using Sent...

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signal, Information and Data processing (ICSIDP), IEEE International conference on

作者： Xuguo Shi Wei Chen Pan Wang Li Zhang School of Geography and Information Engineering China University of Geosciences Wuhan China China Aero Geophysical Survey and Remote Sensing Center for Natural Resources Beijing China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

The Genhe-Labudalin Highway (GenLa Highway) serves as a critical infrastructure link between Genhe City and Labudalin Town in Inner Mongolia, traversing extensive areas of continuous and island permafrost. The stability of this highway is compromised by hazards related to freeze-thaw cycles. Therefore, monitoring its stability is of paramount importance. Interferometric Synthetic Aperture Radar (InSAR) technology has proven invaluable in detecting slow-moving deformations over large areas, making it a vital tool for infrastructure stability monitoring. This study employs 114 Sentinel-1 images, acquired between May 2016 and April 2020, to perform time-series InSAR analysis of the Genla Highway. The analysis reveals linear displacement trends and seasonal displacement along the GenLa Highway and its surrounding areas. The time-series data indicate seasonal deformations, with a maximum displacement of ~20 mm. Significant uneven deformations are observed along the roadbed and adjacent areas, posing further threats to the highway’s stability. Additionally, human activities influence the seasonal freeze-thaw cycles of the permafrost, potentially exacerbating the instability of infrastructure in these regions.

关键词： Road transportation image segmentation Deformation Urban areas Sentinel-1 Market research Stability analysis Permafrost Monitoring Synthetic aperture radar

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Combined Classification of Hyperspectral and LiDAR Data based on Dual-Channel Cross-Transformer 23

Combined Classification of Hyperspectral and LiDAR Data base...

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Proceedings of the 2023 5th International conference on Video, signal and image processing

作者： Binbin Zhou Qingyan Wang Junping Zhang Yujing Wang School of Measurement-Control and Communication Engineering Harbin University of Science and Technology China School of Electronics and Information Engineering Harbin Institute of Technology China

ISBN: (纸本)9798400709272

In the face of complex scenes, single-modal dominant classification tasks encounter limitations in performance due to insufficient information. On the other hand, joint classification of multimodal remote sensing data faces challenges such as data sample differences and lack of correlation in physical features between modalities, which can impact classification performance. To fully integrate the heterogeneous information of multimodal data and improve classification performance, we propose a dual-channel cross-transformer feature fusion extraction network. The framework leverages self-attention mechanisms to aggregate features within each modality, and the feature fusion module based on cross-modal attention fully considers the complementary information between modalities. Classification tasks are performed using the fused spatial-spectral features obtained from the joint representation of modalities. Extensive experiments conducted on the Houston and MUUFL datasets demonstrate the effectiveness of the proposed model compared to existing methods.

关键词： Hyperspectral image LiDAR data cross modality feature fusion transformer

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Performance Improvement of Water Body Segmentation by DeeplabV3+Using Two Dimensional Variational Mode Decomposition

Performance Improvement of Water Body Segmentation by Deepla...

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International conference on signal processing and Integrated Networks (SPIN)

作者： Bichu George Variyar.V.V Sajith V Sowmya. Ramesh Sivanpillai Center for Computational Engineering and Networking(CEN)Amrita School of Engineering Coimbatore Amrita Vishwa Vidyapeetham India Wyoming GIS Center University of Wyoming Laramie WY USA

Monitoring changes in surface water bodies and other earth surface features uses remote sensing data. The DeeplabV3+ network is an encoder decoder based deep neural network that is widely used to segment images with good precision. To improve the predictions made by the DeeplabV3+ model, a novel technique based on the two-dimensional variational mode decomposition (2D-VMD) is proposed in the present work. Sentinel 2A/B images dataset from Kaggle is used for this study. The images and their corresponding annotations are also available. The masks were obtained using the index known as Normalized Water Difference Index (NDWI). From 2841 images, we cropped 100 x100 subsets resulting in 1,50,204 images. The proposed method is found to be effective in improving the predictions made by DeeplabV3+ model. With respect to the images considered for the study, the average F1 score increased from 0.33 to 0.47. The average Jaccard score increased from 0.21 to 0.34.

关键词： Earth Deep learning image segmentation Neural networks Predictive models signal processing Sensors

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