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Evolutionary Algorithms Based Feature Selection For remote sensing image Classification

Evolutionary Algorithms Based Feature Selection For Remote S...

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International conference on Frontiers of signal processing (ICFSP)

作者： Mohammed Bilel Amri Dounia Yedjour Mohammed El Amin Larabi Agence Spatiale Algérienne Centre des Techniques Spatiales Arzew Algeria Département Informatique Laboratoire SIMPA Université Mohamed Boudiaf USTO-MB Oran Algeria d’Oran Mohamed Boudiaf USTO-MB Université des Sciences et de la Technologie Oran Algeria Algerian Space Agency Algeria

The amount of spatial datasets currently available has fundamentally increased and become an essential source of information for extremely important and strategic decisions. Due to the redundant and unused features, the direct usage of raw data might significantly affect the final decisions. To solve this issue, feature selection (FS), as a crucial preprocessing step, is used. It seeks to pick out the most relevant information in order to reduce the data dimensionality and the computational resources, and then increases the accuracy. This paper proposes the Wrapper Feature Selection (WFS) technique for remote sensing data classification. More specifically, evolutionary algorithms as Particle Swarm Optimization (PSO) and Genetic Algorithms (GA) have been utilized to pinpoint the optimum features of the Support Vector Machine (SVM) classifier. To this end, hyperspectral images from Earth Observing-1 (EO-1) have been used to test this strategy. 1 our work demonstrates that a good performance can be achieved for FS and image classification.

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High-Performance Hardware Accelerators for Next Generation On-Board Data processing 30

High-Performance Hardware Accelerators for Next Generation O...

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30th IFIP/IEEE International conference on Very Large Scale Integration (VLSI-SoC)

作者： Paschalis, Antonis Chatziantoniou, Panagiotis Theodoropoulos, Dimitris Tsigkanos, Antonis Kranitis, Nektarios Natl Kapodistrian Univ Athens NKUA Dept Informat & Telecommun Athens Greece Natl Kapodistrian Univ Athens NKUA Dept Aerosp Sci & Technol Psahna Evias Greece

ISBN: (数字)9781665490054

ISBN: (纸本)9781665490054

Hyperspectral imaging is a key remote sensing technology. The explosive growth in hyperspectral image data volume (several TBs per orbit) and instrument data rates (in the range of 20 Gbps), compete with limited available on-board storage resources and downlink bandwidth, making hyperspectral image data compression a mission critical on-board data processing task. In order to provide continuous and reliable data transfer from the on-board systems to the ground stations, channel coding is being applied at the end of the data processing pipeline to guarantee reliable communications even at low signal-to-noise ratio regimes. Moreover, erasure correcting codes working on information packets rather than bits or symbols, can provide downlink reliability in future RF and optical links especially when Automatic Repeat Queuing (ARQ) strategies are either problematic or impossible due to specific service delay constraints and intermittent connectivity. Committed to the vision for "Space Technology Designed in Greece", in this special session paper, we provide an overview of cutting-edge technology high-performance hardware accelerators developed in the NKUA Digital Systems and Computer Architecture Laboratory (DSCAL), applicable to next-generation payloads for earth observation, optical/RF communication and connectivity, including CCSDS hyperspectral image compression and channel-coding at bit-level and packet-level.

关键词： CCSDS FPGA hardware accelerators hyperspectral image compression channel-coding packet-level coding

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Industrial Video Surveillance image Denoising based on Non-subsampled Contourlet Transform

Industrial Video Surveillance Image Denoising based on Non-s...

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Information Technology,Big Data and Artificial Intelligence (ICIBA), International conference on IEEE International conference on

作者： Qingyun Zhang Zhuo Chen Pengfei Xie Yingxu Wang Ruibo Shi Qian Zhou Yuduo Pu Dept. of Electronic and Information Eng. Shandong University of Science and Technology Qingdao China Rizhao Guangming Electric Power Service Co. Ltd. Rizhao China State Grid Information & Telecommunication Branch Beijing China Wuhan Fiberhome Technical Services Co. Ltd Wuhan China

ISBN: (数字)9798350363609

ISBN: (纸本)9798350363616

With the continuous expansion of industrial video scale, visualization technology has become an indispensable technical means. However, during the collection and transmission of images, they may be affected by factors such as the device itself and environmental factors, which can lead to noise pollution and a decrease in image quality, which is not conducive to image post-processing. Therefore, industrial video surveillance image denoising has become an important research topic, and using various methods to improve image quality has become an important research direction. In order to address the ringing effect caused by hard threshold denoising, this paper proposes an adaptive smooth non-subsampled contourlet transform (NSCT) denoising algorithm. The proposed method utilizes the correlation of each sub-band to improve the denoising function, making it adaptive. The simulation results show that the algorithm proposed in this paper improves subjective perception such as contrast and detail clarity of remote sensing images, as well as objective evaluation indicators such as peak signal-to-noise ratio, information entropy, and correlation coefficient

关键词： image quality Correlation Wiener filters Heuristic algorithms Simulation Noise reduction Transforms Video surveillance remote sensing image denoising

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Useable Machine Learning for Sentinel-2 multispectral satellite imagery 27

Useable Machine Learning for Sentinel-2 multispectral satell...

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conference on image and signal processing for remote sensing XXVII

作者： Langevin, Scott Bethune, Chris Horne, Philippe Kramer, Steve Gleason, Jeffrey Johnson, Ben Barnett, Ezekiel Husain, Fahd Bradley, Adam Uncharted Software 2 Berkeley St 600 Toronto ON Canada Kung Fu AI 211 E 7th St Suite 100 Austin TX USA Jataware 6630 31st P1 NW Washington DC USA

ISBN: (数字)9781510645691

ISBN: (纸本)9781510645691;9781510645684

One of the challenges when building Machine Learning (ML) models using satellite imagery is building sufficiently labeled data sets for training. In the past, this problem has been addressed by adapting computer vision approaches to GIS data with significant recent contributions to the field. But when trying to adapt these models to Sentinel-2 multi-spectral satellite imagery these approaches fall short. Previously, researchers used transfer learning methods trained on imageNet and constrained the 13 channels to 3 RGB ones using existing training sets, but this severely limits the available data that can be used for complex image classification, object detection, and image segmentation tasks. To address this deficit, we present Distil, and demonstrate a specific method using our system for training models with all available Sentinel-2 channels. There currently is no publicly available rich labeled training data resource such as imageNet for Sentinel-2 satellite imagery that covers the entire globe. Our approach using the Distil system was: a) pre-training models using unlabeled data sets and b) adapting to specific downstream tasks using a small number of annotations solicited from a user. We discuss the Distil system, an application of the system in the remote sensing domain, and a case study identifying likely locust breeding grounds in Africa from unlabeled 13-channel satellite imagery.

关键词： remote sensing Machine Learning Satellite imagery image Classification

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Multispectral remote sensing image Classification Using Modern Machine Intelligence Approach 7

Multispectral Remote Sensing Image Classification Using Mode...

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7th International conference on signal processing and Communication, ICSC 2021

作者： Singh, Mohan Tyagi, Kapil Dev G.L. Bajaj Institute of Technology and Management Department of Electronics and Communication Engineering Greater Noida India

ISBN: (纸本)9781665427395

Classification of multispectral images is a processing tool used in various applications of remote sensing in natural hazards assessment, agricultural and environmental monitoring to assess and analyze current events so as to avoid damage caused by untimely disasters in nature. Several image classifiers have been presented in the past, some of which are supervised classifiers and some unsupervised ones that have been applied to different remote sensing images to classify them. But the classification accuracy on any of the multispectral images is not good enough to identify the real and correct image classes. Therefore, the real and true classification of multispectral images remains a challenging task even today. To accomplish this challenging task, we have presented a novel machine intelligence approach in this paper. The proposed approach is based on the pixels of multispectral images in which the spectral properties of the image have not been changed. The model consists of a total of five layers consisting of three hidden layers in which seven features vectors are applied to the input layer and all hidden layers are activated with the ReLU function. There are four neurons on the output layer, which is activated with the softmax function. The accuracy of the proposed model was achieved at 99.97% with a 0.9997 Kappa coefficient which is 9.04% higher than the previously proposed models. Due to the good accuracy of this model, it can be used for the accurate assessment of any natural disaster and agricultural health monitoring. © 2021 IEEE.

关键词： Landsat

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SELF ATTENTION BASED SEMANTIC SEGMENTATION ON A NATURAL DISASTER DATASET

SELF ATTENTION BASED SEMANTIC SEGMENTATION ON A NATURAL DISA...

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IEEE International conference on image processing (ICIP)

作者： Chowdhury, Tashnim Rahnemoonfar, Maryam Univ Maryland Baltimore Cty Dept Informat Syst Comp Vis & Remote Sensing Lab Bina Lab Baltimore MD 21228 USA

ISBN: (纸本)9781665441155

Global image dependencies help in full image understanding. Self-attention based methods can map the mutual relationship and dependencies among pixels of an image and thus improve semantic segmentation accuracy. In this paper, we propose two segmentation networks based on a novel baseline self-attention network. Compared to existing self-attention methods we utilize lower level feature maps to generate position attention modules which constitute a baseline network. This baseline network is incorporated with global average pooling and U-Net to create two segmentation schemes. These two segmentation networks are evaluated on a natural disaster dataset and perform excellent in damage assessment with a Mean IoU score of 95.61%.

关键词： Semantic segmentation natural disaster damage assessment self-attention UAV

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Boundary Detector Network with Squeeze-and-Excitation Network for Semantic Segmentation of remote sensing images 23

Boundary Detector Network with Squeeze-and-Excitation Networ...

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Proceedings of the 2nd International conference on signal processing, Computer Networks and Communications

作者： Haiyan Cheng Ruiqi Yang Guangzhi Di College of big data and intelligent engineering Southwest Forestry University China

ISBN: (纸本)9798400716430

With the rapid development of deep learning, remote sensing image interpretation using semantic segmentation has been widely used in various fields. Deep convolutional Neural network (DCNN) is an important tool for semantic segmentation. However, increasing the complexity of the network and deepening the neural network not only can improve the accuracy of the network, but also lead to the overfitting problem. Based on this point, this paper used the squeeze-and-excitation (SE) modules to optimize the network. SE modules are superimposed after each layer of the ResNet-101 network. This method not only improves network performance but does not lose small target information too much. In this experiment, RGB and nDSM multi-channels of high resolution remote sensing (RS) images with multi-semantic information are used. Experimental results show that the accuracy of the proposed method in the training set and verification set can reach 98.11% and 98.45%, and the model is in continuous convergence. OA, Precision, Recall, F1 and MIOU can reach 85.73%, 84.64%, 85.76%, 85.67%, and 75.47% respectively.

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A Dual Network Approach with Enhanced Feature Guidance for SAR-to-Optical image Translation

A Dual Network Approach with Enhanced Feature Guidance for S...

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

作者： Jingfei He Yongfei Mao Liangbo Zhao Zihan Cui Yu Wang Hao Shi National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing Beijing Institute of Technology Beijing China Institute of Remote Sensing Satellite China Academy of Space Technology Beijing China Beijing Institute of Space Mechanics and Electricity China Academy of Space Technology Beijing China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

Synthetic Aperture Radar (SAR) images own dominant merits, such as all-weather and all-day working conditions, but it is difficult for unprofessional people to interpret SAR images. Translating SAR images into optical images to assist interpretation can facilitate the transformation of SAR data into usable information. Therefore, an advanced SAR to Optical (S2O) image translation method utilizing a parallel Generative Adversarial Network (GAN) framework was proposed. This method incorporates an optical image reconstruction network alongside the S2O translation network, enhancing the fidelity and color consistency of the translated images. By employing a domain alignment module and a deep supervision feature loss module, the network effectively utilizes abundant optical image features to overcome the scarcity of SAR-optical image pairs. Experiments conducted on the SEN1-2 dataset demonstrate superior performance over existing methods, particularly in preserving detailed structural information in images.

关键词： Integrated optics Optical losses Translation image color analysis Optical fiber networks Optical imaging Generative adversarial networks Adaptive optics Radar polarimetry Synthetic aperture radar

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PanFormer: A Transformer Based Model for Pan-Sharpening

PanFormer: A Transformer Based Model for Pan-Sharpening

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2022 IEEE International conference on Multimedia and Expo, ICME 2022

作者： Zhou, Huanyu Liu, Qingjie Wang, Yunhong Beihang University The State Key Laboratory of Virtual Reality Technology and Systems Beijing China Hangzhou Innovation Institute Beihang University Hangzhou China

ISBN: (数字)9781665485630

ISBN: (纸本)9781665485630

Pan-sharpening aims at producing a high-resolution (HR) multi-spectral (MS) image from a low-resolution (LR) multi-spectral (MS) image and its corresponding panchromatic (PAN) image acquired by a same satellite. Inspired by a new fashion in recent deep learning community, we propose a novel Transformer based model for pan-sharpening. We explore the potential of Transformer in image feature extraction and fusion. Following the successful development of vision transformers, we design a two-stream network with the self-attention to extract the modality-specific features from the PAN and MS modalities and apply a cross-attention module to merge the spectral and spatial features. The pan-sharpened image is produced from the enhanced fused features. Extensive experiments on GaoFen-2 and WorldView-3 images demonstrate that our Transformer based model achieves impressive results and outperforms many existing CNN based methods, which shows the great potential of introducing Transformer to the pan-sharpening task. Codes are available at https://***/zhysora/PanFormer. © 2022 IEEE.

关键词： remote sensing

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Complex-valued Butterfly Transform for Efficient Hyperspectral image processing

Complex-valued Butterfly Transform for Efficient Hyperspectr...

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IEEE International conference on Fuzzy Systems (FUZZ-IEEE) / IEEE World Congress on Computational Intelligence (IEEE WCCI) / International Joint conference on Neural Networks (IJCNN) / IEEE Congress on Evolutionary Computation (IEEE CEC)

作者： Singhal, Utkarsh Yu, Stella X.

ISBN: (数字)9781728186719

ISBN: (纸本)9781728186719

Hyper-spectral imaging (HSI) is a critical remote sensing modality that captures high-resolution spectral information in addition to high spatial resolution. Due to its ability to capture rich information about the material properties of the target, HSI has found applications such as agriculture, ecological monitoring, urban planning, and medicine. However, HSI images tend to have high spatial resolution and many channels, thus requiring models that can integrate information over a large context and identify spectral signatures. HSI classification datasets are also highly imbalanced, sparsely labeled, and significantly smaller than standard vision datasets like imageNet or CIFAR, thus motivating smaller models with high sample efficiency. This work combines the strengths of multi-scale representations and data-driven feature learning. We generalize the butterfly transform to a learned complex-valued butterfly layer, allowing for parameter-efficient extraction of hierarchical complex-valued features from 1D signals. This allows us to create a lean yet highly accurate hyperspectral image classification model. Benchmarked on the Indian Pines, ROSIS-03 Pavia University, and Salinas datasets, our method demonstrates accuracy on par with SSDGL while using 7x fewer parameters. On the most imbalanced and sparsely labeled dataset, our method outperforms SSDGL.

关键词： complex-valued deep learning FFT butterfly transform signal processing hyper-spectral imaging

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