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Nuand BladeRF Xa-4 Software Defined Radio (SDR) Implementation for Data processing and Reception of AQUA Satellite signals

Nuand BladeRF Xa-4 Software Defined Radio (SDR) Implementati...

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International conference on Information Technology, Information Systems and Electrical Engineering (ICITISEE)

作者： Farid Armin Supriyono Adi Aufarachman Putra Bambang Dwi Hidayat Gunawan Suhermanto Andy Indradjad Research Center for Satellite Technology National Research and Innovation Agency (BRIN) Bogor Indonesia

ISBN: (数字)9798350368970

ISBN: (纸本)9798350368987

Satellite data reception and processing systems require a large number of devices, large in size, complex and static. This research aims to implement SDR technology to replace the function of previous conventional devices in receiving and processing satellite data, especially AQUA satellite data. The SDR used is the Nuand BladeRF Xa-4 type with frequencies ranging from 47 MHz to 6 GHz and sample rate with 6.44 MHz. Whereas the antenna used to receive the signal emitted by AQUA's satellites uses a parabolic antenna with a diameter of 3 m. Digital signal processing (DSP) for recording and decoding data using GNU Radio Companion (GRC) SDR software and open source software. Data received through an antenna is converted down to the IF 720 MHz frequency and then demodulated and generates data in a signed 16-bit format (.s16) with a 10 Msps sampling rate. Using open-source software, data resulting from demodulation is then decoded. The test results showed that SDR is able to receive and process AQUA satellite data with level 0 image data output. The data obtained using the existing system with the built system has been compared. The advantages and disadvantages of the system have also been discussed. Further development requires more in-depth research related to the use of SDR technology for various needs in the field of satellite technology.

关键词： Satellite antennas Instruments Satellite broadcasting Receiving antennas Digital signal processing Recording Open source software remote sensing MODIS Software radio

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image processing Applications for Sustainable Site Planning in Urban Built Environment 2

Image Processing Applications for Sustainable Site Planning ...

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2nd IEEE International conference on Computational Intelligence and Knowledge Economy, ICCIKE 2021

作者： Rana Mahanta, Niva Samuel, Abraham K. Ahmed Chundeli, Faiz Amity University Dubai Amity School of Architecture and Planning Dubai United Arab Emirates School of Planning and Architecture Department of Architecture Vijayawada India

ISBN: (纸本)9781665429214

As per United States Green Building Council (USGBC) guidelines, sustainable site design tops the list of green design parameters. From a micro-level housing complex or neighborhood planning to a macro-level city planning, it has become mandatory to consider the factors responsible for sustainable designs of building sites. GPS and remote sensing are two signal processing applications that are essential tools to help architects, planners, and urban designers in the planning of sites for built spaces. They help to get multi-dimensional digital data required for all site features and characteristics which otherwise can be a very lengthy and complicated process, especially for remote site locations. In site planning, digital image processing is done for the environmental factors, natural factors (geology, topography, hydrology, soil, vegetation, wildlife, etc.), climatic factors, and existing land use. In this study, we evaluate and analyze the application of digital images for sustainable site planning with the help of a case study analysis of Dubai Silicon Oasis in Dubai. Normalized Difference in Vegetation Index (NDVI) and Normalized Difference in Water Index (NDWI) were derived for the case area using Landsat 8 dataset. Seasonal variation in NDVI and NDWI was observed in the study area. In this paper, we highlight the need for the inclusion of image processing applications in early site planning stages to make informed decisions. Appropriate size, shape, and placement of green and water components in the site plan would aid in heat mitigation. © 2021 IEEE.

关键词： remote sensing

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Farm Parcel Extraction in remote-sensing images Based on Semantic Segmentation

Farm Parcel Extraction in Remote-Sensing Images Based on Sem...

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International conference on signal processing Systems, ICSPS

作者： Yirong Wang Liang Qi Bangyu Li School of Electronics and information Jiangsu University of Science and Technology Zhenjiang China Institute of Automation Chinese Academic Sciences Beijing China

Vectorized and structured data extracted from farm parcel segmentation and classification provides basic information for the research on pest control of large areas, crop yield forecasts, and crop varieties classification. On one hand, we investigate the effectiveness of an automated image segmentation method based on DeeplabV3+ architecture to enable it to automate the task of farm parcel delineation that originally relied on high labor costs. On the other hand, we detail an efficient post-processing on vectoring binary segmentation image to get more optimized image segmentation results. Overall, on the 0.8m resolution image dataset, the precision reaches 85.22%, the recall reached 83.78 % , the F1-S was 81.08%, the accuracy was 82.54%, and IoU was 82.87%. Meanwhile, without losing too much precision, we train and test the model with 3.2m resolution images, which has the advantage of processing speed over 0.8m resolution. The method can be effectively applied to the task of segmentation of agricultural land. Extensive experiments show the effectiveness of using the DeeplabV3+ architecture and post-processing by vectoring binary segmentation for farm parcel segmentation in high remote sensing images.

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An Optical remote sensing Satellite Relative Radiometric Calibration Method Based on Statistical Sample Weighting 12th

An Optical Remote Sensing Satellite Relative Radiometric Cal...

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12th International conference on signal and Information processing, Networking and Computers, ICSINC 2024

作者： He, Huaying Deng, Chao Huang, Xiaoyu Zhang, Ao Zeng, Jian Han, Qijin Wu, Yu China Centre for Resources Satellite Data and Application Beijing100095 China

ISBN: (纸本)9789819649662

Aiming at the strip noise in the overlap region of a multi-spectral image of the space optical remote sensing camera with reflective mirror splicing, it is proposed in this paper that a normalized synchronization method of distortion correction and radiometric information of homonymic pixels in the overlap region. Pixels in the overlap region. To solve the problem of strip noise in the overlap region of the multi-spectral image, the radiometric models are both reconstructed by adding the radiometric models in the overlap region. Reconstructed by adding the radiometric energy of homonymic pixels after the distortion corrections of homonymic pixels in the overlap region, and the moment matching method is used to treat the edge of the overlap region with transitional uniform color. At the same time, the fringe coefficient is optimized, the fringe coefficient is optimized so that the fringe coefficient is more sensitive to the fringe region and has no effect on the non-fringe region, and the fringe energy analysis is quantified. Experimental results show that the strip noise in the overlap is reduced or even disappears, the fringe coefficient declines, and the radiometric uniformity of the multi-spectrum is reduced. Experimental results show that the strip noise in the overlap is reduced or even disappeared, fringe coefficient declined and the radiometric uniformity of multi-spectral images is enhanced significantly. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Radiometry

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Feature profiles for semisupervised hyperspectral image classification with limited labeled training samples 27

Feature profiles for semisupervised hyperspectral image clas...

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conference on image and signal processing for remote sensing XXVII

作者： Uchaev, D., V Uchaev, Dm, V Moscow State Univ Geodesy & Cartog Moscow 105064 Russia

ISBN: (数字)9781510645691

ISBN: (纸本)9781510645691;9781510645684

In this study, we present spatial feature profiles that can be used in addition to spectral profiles for semisupervised small-sample-size (SSS) classification of hyperspectral images (HSIs). These profiles have been obtained by combining extended multi-attributive profiles (EMAPs) and the recently proposed Chebyshev moment multifractal profiles (CMMPs). In order to demonstrate SSS classification capabilities of the introduced feature profiles, several experiments were performed on two test HSIs. In these experiments, we used a graph-based ensemble learning method for semisupervised HSI classification and a small number of labeled samples for training. The experiments performed on test HSIs demonstrate that the proposed feature profiles provide good classification performance in terms of the overall accuracy (OA), average accuracy (AA) and Kappa statistics. We also compared the classification results obtained using EMAPs and CMMPs with those obtained using EMAPs alone, CMMPs alone, and another spatial feature sets. It has been established that the classification based on CMMPs and EMAPs shows obvious improvements, especially when the number of labeled training samples is extremely small. In the final part of the study, the HSI classification results obtained using the proposed feature profiles were compared with classification maps obtained by deep learning methods adopted for small training samples. This comparison showed that the semisupervised classification by EMAPs and CMMPs is characterized by higher values of OA, AA and Kappa coefficients. Moreover, in contrast to deep learning methods, the classification procedure based on the calculation of the proposed feature profiles and graph-based ensemble learning is not time-consuming.

关键词： hyperspectral images small-sample-size classification semisupervised learning extended multi-attributive profiles multifractal profiles

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Hyperspectral Unmixing via Multi-scale Representation by CNN-BiLSTM and Transformer Network

Hyperspectral Unmixing via Multi-scale Representation by CNN...

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

作者： Jianan Wang Rong Liu School of Geography and Planning Sun Yat-sen University Guangzhou China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

Hyperspectral unmixing (HU) is a powerful technique to solve the challenge associated with mixed pixels that widely exist in hyperspectral images (HSIs). HU can obtain the pure signature of ground objects (endmembers) and their constituent proportion (abundances) within mixed pixels. Recently, deep learning techniques, with their powerful feature extraction capabilities, have significantly advanced the development of new HU technologies. A HU network based on an autoencoder, which combines a multi-branch convolutional neural network (CNN), a bidirectional long and short-term memory (BiLSTM) network, and transformers is proposed to enhance the spectral and spatial feature extraction ability of the network in this paper. Given the complex distribution of ground objects observed in hyperspectral remote sensing images, we employ a multi-branch CNN and a BiLSTM to extract multi-scale spatial features and spectral features respectively to address the issue that the transformer is not sensitive to low-level features. Then, the joint features are input into the transformer encoder to extract non-local features. Finally, the decoder reconstructs the HSI, estimating both endmembers and abundances. The efficacy of the proposed method is validated through one synthetic hyperspectral dataset, in addition to three real hyperspectral datasets: Samson, Jasper, and Apex.

关键词： Deep learning Bidirectional long short term memory Estimation Feature extraction Transformers Decoding Convolutional neural networks image reconstruction Hyperspectral imaging Synthetic data

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A Lightweight and Fine-Grained Feature Fusion Network for remote sensing Scene Classification

A Lightweight and Fine-Grained Feature Fusion Network for Re...

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2021 IEEE International conference on signal processing, Communications and Computing, ICSPCC 2021

作者： Bai, Lin Liu, Qingxin Li, Cuiling Ye, Zhen Hui, Meng Chang'An University School of Electronic and Control Xi'an710064 China

ISBN: (纸本)9781665429184

Deep learning (DL) has become a hot topic in the research field of high-resolution remote sensing image (HRRSI) scene classification in recent years. Although traditional convolutional neural network (CNN) has achieved excellent achievements in scene classification, with the increasing depth and size of deep learning methods, its application in mobile and embedded vision applications has brought significant challenges. We present a novel lightweight and fine-grained feature fusion network for classification with low model parameters and high accuracy to address this issue. This paper uses MobileNetV2 as the backbone, and the multi-scale features are extracted in a more fine-grained way. The receptive field was extended by intergroup fusion depthwise convolution (IFDW Conv). We also use squeeze and excitation (SE)-block to extract dependence between channels. Therefore, we call our model SE-IFDWNet. Ablation experiments verify the effectiveness of IFDW Conv and SE-block proposed in our network. Experimental results on three standard HRRSI datasets show that SE-IFDWNet has advantages in classification accuracy and efficiency compared with the current state-of-The-Art methods. © 2021 IEEE.

关键词： Convolution

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image Defogging based on Combined Sparse Gradient Minimization and CNN Architecture

Image Defogging based on Combined Sparse Gradient Minimizati...

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International conference on Microwave, Optical and Communication Engineering (ICMOCE)

作者： Anisha Jana Upendra Kumar Sahoo Department of Electronics and Communication Engineering National Institute of Technology Rourkela Odisha India

Some environmental factors like haze or fog de-grades the quality of the image. These factors affect some real time processes such as object detection and recognition, automated vehicles and remote sensing which needs clear visible images for making critical decisions. Therefore, restoring the true image from the foggy image becomes significant. Now with the advancement in image processing, many image defogging and dehazing algorithms has been developed to improve the quality of the image. Many standard filtering techniques such as high boost filter, homomorphic filter can be used for image defogging but it fails to restore the foggy images completely so some advanced techniques like DCP, decomposition techniques, CNN based algorithms are used. The proposed work mainly focuses on CNN based algorithm for image defogging combined with SGM module for edge preservation. image quality assessment (IQA) is done to measure the quality of the defogged image. These performance metrics mainly includes mean squared error (MSE), structural similarity index metric (SSIM),peak signal to noise ratio (PSNR).

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An innovative graphene/QDs photodetector for remote sensing

An innovative graphene/QDs photodetector for remote sensing

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conference on AOPC - Optical sensing and Imaging Technology

作者： Xu, Yunfei Wang, Peng Deng, Xuguang Wang, Huaiyi Beijing Inst Space Mech & Elect Beijing 100094 Peoples R China Beijing Univ Technol Coll Mat Sci & Engn Beijing 100124 Peoples R China Key Lab Adv Opt Remote Sensing Technol Beijing Beijing Peoples R China Minist Educ China Key Lab Adv Funct Mat Beijing Peoples R China

ISBN: (纸本)9781510650060;9781510650053

Infrared imaging generally refers to the technology that takes radiation energy of the target itself as received signal and converts it into an image. We can obtain geometric characteristics as well as temperature characteristics of the target day and night by applying the infrared imaging technology as it can hardly be affected by illumination conditions and adverse weather, hence it is extremely important for both military and civil use. Since the combination of infrared and visible light imaging enables us to achieve the target information more comprehensively, the remote sensing cameras rapidly accomplished the transition from single pass to multipass, which allows them to obtain visible light and infrared images through different optical paths simultaneously. However, this imaging system greatly increased the volume, mass, power consumption and cost budgets. Here, we discussed an innovative graphene/QDs photodetector for remote sensing. As the bandgap of QDs is controllable through its size-dependence, its response spectrum could easily be modulated through the size control and ligand exchange(1), thus enables us to obtain a detector with response spectrum range from visible light to medium wave. And with the help of PMMA-assisted transfer processing(2), we can arrange the QDs with different response spectrum on purpose. Therefore, it is possible for us to customize the response spectrum for each pixel, and this truly enables the visible infrared integrated single pass imaging system and could generate a variety of strategies for remote sensing.

关键词： remote sensing graphene/QDs photodetectors wide spectrum visible infrared integrate

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A fast back-projection algorithm based on equivalent slant range model for high-squint SAR imaging

A fast back-projection algorithm based on equivalent slant r...

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IET International Radar conference (IRC 2023)

作者： GaoTian Xu Tianyi Ca Yachao Li Tinghao Zhang National Key Laboratory of Radar Signal Processing Xidian University Xi’an People's Republic of China Beijing Institute of Remote-Sensing Equipment Beijing People's Republic of China

ISBN: (数字)9781837240982

Because back-projection algorithm (BPA) is independent of the azimuth-invariant hypothesis of the echo signal, it has significant advantages for synthetic aperture radar (SAR) imaging with arbitrary trajectory. Although Cartesian fast back-projection algorithm (CFBPA) is proposed to improve the efficiency of BPA, for high-squint SAR (HS-SAR) imaging, the geometric configuration of HS-SAR greatly enhances the space-variant property of the spectrum, which leads to the ineffectively compressed spectrum and decreases the efficiency of the sub-image merging in CFBPA processing. In this paper, an equivalent slant range model (ESRM) based on hybrid coordinate system (HCS) is proposed for HS-SAR imaging. Benefiting from the designed ESRM, the space-variant property of the wavenumber spectrum can be significantly degraded to improve performance in spectrum compression and accelerates the sub-image merging. Promising results from simulation experiments are provided to validate the high performance of the proposed algorithm.

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