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检索条件"任意字段=Conference on Image and Signal Processing for Remote Sensing IX"

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An Approach to Near-lossless Hyperspectral Data Compression using Deep Autoencoder 26

An Approach to Near-lossless Hyperspectral Data Compression ...

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conference on image and signal processing for remote sensing XXVI

作者： Kuester, Jannick Gross, Wolfgang Middelmann, Wolfgang Fraunhofer IOSB Fraunhofer Inst Optron Syst Technol & Image Explo Ettlingen Germany

ISBN: (纸本)9781510638808

In recent years, the development of sensor technology in the area of hyperspectral imaging has been continuously moving forward. The result is a significant increase in availability, applications and, consequently, data volume. Compression is required to facilitate the transmission and storage of hyperspectral data sets. The high spectral correlation between adjacent bands allows for decorrelation approaches to compress the data with minimal loss of important information. Since it is not known a priori which feature is essential, the compression of hyperspectral data is a challenging task. In this paper, we introduce an approach to compress hyperspectral data using a Deep Autoencoder. An Autoencoder is an artificial neural network that first learns the important features from the data, and subsequently reconstructs the data from the reduced encoded representation. The evaluation is done by comparing the classification performance between the original and the reconstructed data. As a classifier we use the Adaptive Coherence Estimator to compare the spectral signatures. Performance is assessed by comparing the mean classification accuracy for a fixed false alarm rate. Additionally, the signal to Noise Ratio and the spectral angle are used as metrics for evaluating the reconstruction performance. Airborne hyperspectral data were used in combination with simulated data, representing a linear mixture with different ratios of target and background spectra. Multiple target and background materials are tested to compare the performance. The selected data provide a representative set of target and background spectra to evaluate the compression method in relation to the detection limit. The compression rate is set to 4:1 and the reconstruction accuracy is investigated. Additionally, classification of noisy data is compared to the compression results to show the impact of information loss. If both results are similar, it can be deduced that the compression process is near-loss

关键词： hyperspectral near-lossless compression Autoencoder (AE) deep learning detection limit classification

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Research on Simulation Technology for remote sensing image Quality 8th

Research on Simulation Technology for Remote Sensing Image Q...

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8th International conference on Communications, signal processing, and Systems, CSPS 2019

作者： Sun, Hezhi Li, Yugao Mei, Xiao Gao, Yuting Yang, Dong School of Astronautics Harbin Institute of Technology Harbin China China Academy of Space Technology Beijing China

ISBN: (纸本)9789811394089

Full-link image quality simulation analysis is an important part of the satellite development process. It can not only predict the satellite image quality before launch, but also help to adjust the satellite related design according to the simulation results. This paper expounds the construction idea, system scheme and composition of each subsystem of the full-link image quality simulation system for optical remote sensing satellites and carries out serial full-link closed-loop simulation test on the preliminary simulation system to verify the reasonableness and feasibility of the system scheme. This system is constructed with functional modular design, while the model algorithm has good openness, good adaptability to different tasks and high application value. © 2020, Springer Nature Singapore Pte Ltd.

关键词： image quality

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UNSUPERVISED CHANGE DETECTION FOR MULTIMODAL remote sensing imageS VIA COUPLED DICTIONARY LEARNING AND SPARSE CODING

UNSUPERVISED CHANGE DETECTION FOR MULTIMODAL REMOTE SENSING ...

引用

IEEE International conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Ferraris, Vinicius Dobigeon, Nicolas Cavalcanti, Yanna Oberlin, Thomas Chabert, Marie Univ Toulouse IRIT INP ENSEEIHT Toulouse France Univ Toulouse ISAE SUPAERO F-31400 Toulouse France Univ Sao Paulo Inst Math & Stat Sao Paulo Brazil

ISBN: (纸本)9781509066315

Archetypal scenarios for change detection generally consider two images acquired through sensors of the same modality. The resolution dissimilarity is often bypassed though a simple preprocessing, applied independently on each image to bring them to the same resolution. However, in some important situations, e.g. a natural disaster, the only images available may be those acquired through sensors of different modalities and resolutions. Therefore, it is mandatory to develop general and robust methods able to deal with this unfavorable situation. This paper proposes a coupled dictionary learning strategy to detect changes between two images with different modalities and possibly different spatial and/or spectral resolutions. The pair of observed images is modelled as a sparse linear combination of atoms belonging to a pair of coupled overcomplete dictionaries learnt from the two observed images. Codes are expected to be globally similar for areas not affected by the changes while, in some spatially sparse locations, they are expected to be different. Change detection is then envisioned as an inverse problem, namely estimation of a dual code such that the difference between the estimated codes associated with each image exhibits spatial sparsity. A comparison with state-of-the-art change detection methods evidences the proposed method superiority.

关键词： change detection multimodal unsupervised resolution dictionary learning

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image Generation Based on Texture Guided VAE-AGAN for Regions of Interest Detection in remote sensing images

Image Generation Based on Texture Guided VAE-AGAN for Region...

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IEEE International conference on Acoustics, Speech and signal processing

作者： Libao Zhang Yanan Liu School of Artificial Intelligence Beijing Normal University Beijing China

Deep learning has shown great strength in regions of interest (ROIs) detection for remote sensing images (RSIs). However, for most of RSIs, the unbalanced distribution of positive and negative samples greatly limits the performance of the deep learning-based methods. To cope with this issue, we propose a novel method based on texture guided variational autoencoder-attention wise generative adversarial network (VAE-AGAN) to augment the training data for ROI detection. First, to generate realistic texture details of RSIs, we propose a texture guidance block to embed texture prior information into encoder and decoder networks. Second, we introduce the channel and spatial-wise attention layers in the discriminator construct to adaptively recalibrate the varying importance of different channels and spatial regions of input RSIs. Finally, we apply the RSI dataset balanced by our proposal to the weakly supervised ROI detection method. Experimental results demonstrate that the proposal can not only improve the performance of ROI detection, but also outperform other competing augmentation methods.

关键词： Learning systems Deep learning image synthesis Training data signal processing Generative adversarial networks Decoding

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Parallel Cascade R-CNN for object detection in remote sensing imagery 5

Parallel Cascade R-CNN for object detection in remote sensin...

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2020 5th International conference on Intelligent Computing and signal processing, ICSP 2020

作者： Hou, Jingyou Ma, Hongbing Wang, Shengjin Department of Electronic Engineering Tsinghua University Beijing100086 China Beijing National Research Center Information Science and Technology [BNRist China

Object detection in remote sensing image is a challenging task in computer vision. remote sensing images have different characteristics compared with conventional images. Especially object detection in remote sensing image needs to focus on small targets with different ratio and orientation. In this paper, we propose a novel detection architecture for remote sensing imagery to solve the problem of scale diversity. On the basis of Cascade R-CNN, we have developed Parallel Cascade R-CNN. In the second stage, parallel detection heads are used for separate detection, and their RoIAlign modules have different output sizes. In addition, different preprocessing methods are applied according to the shape and quantity characteristics of different classes. We evaluated our algorithm on DOTA dataset. Experiments have shown that our algorithm can achieve performance improvement on a high baseline. And the detection performance of those categories with smaller objects has been improved. In the detection task of horizontal bounding box, we obtained the mAP of 78.96%, which reached the state of the art. Our algorithm is simple and has good performance, easy to be migrated to various network structures. © 2019 Published under licence by IOP Publishing Ltd.

关键词： Object detection

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Aircraft type classification in remote sensing images using deep learning 2

Aircraft type classification in remote sensing images using ...

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2nd IEEE International conference on Electronics, Control, Optimization and Computer Science, ICECOCS 2020

作者： Youssef, Youssef Ben Merrouchi, Mohamed Abdelmounim, Elhassane Gadi, Taoufiq Interdisciplinary Laboratory of Applied Sciences National School of Applied Sciences Berrechid Morocco Lab Computing Imaging and Modeling of Complex Systems FST of Settat Hassan First University Settat Morocco Lab of Signal Analysis and Information Processing FST of Settat Hassan First University Settat Morocco

ISBN: (纸本)9781728169217

In aeronautic, redundancy of data is strongly desired to make optimal decisions. A large data source is available with the development of the technology used in remote sensing images. remote sensing image Classification (RSIC) is widely exploited in military and civil fields. To improve the performance of multi-label classification, we addressed the problem of RSIC based on the Convolutional Neural Network (CNN) for remote sensing images of aircraft types. Previous studies have used intensive preprocessing which limits the rate of classification. We improved the network structure to make it more accurate and to limit underfitting or overfitting problems. A recent public dataset called Multi-Type Aircraft remote sensing images (MTARSI) is used in this work to validate our method. Extensive experiments prove the effectiveness of the proposed method in terms of accuracy. © 2020 IEEE.

关键词： Convolutional neural networks

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Standoff Heart Rate Estimation from Video - A Review

Standoff Heart Rate Estimation from Video - A Review

引用

conference on Mobile Multimedia/image processing, Security, and Applications

作者： Deng, Yunbin Kumar, Arya BAE Syst Inc FAST Labs Burlington MA 01803 USA Univ Michigan Ann Arbor MI 48109 USA

ISBN: (纸本)9781510635760;9781510635753

Recent breakthroughs in EO/IR sensing, real-time signal processing, and deep machine learning technologies have enabled standoff heart rate estimation from facial and body video. This technology is also known as remote photoplethys-mography (rPPG). Research and development of rPPG has attracted much attention recently. This paper gives a timely review of this fast-paced field to give the researcher, engineer, and graduate student a quick grasp of the recent advancement of rPPG. We first review two rPPG design approaches: color variation based and motion-based detections. To enable rPPG for less constrained use cases, various signal processing and machine learning algorithms are developed to handle signal variabilities introduced by lighting source, view angle, and subject motion. To help newcomers quickly start work in this field, we then describe some existing rPPG research datasets, open-source rPPG research tools, and some demonstration systems. Six commonly used rPPG algorithm evaluation metrics are described to evaluate and visualize the research advance in this field. As the rPPG technology matures, more application domains become possible. We cover six applications of rPPG in commercial, security, and defense domains, including emerging applications in biometric liveness and video media authenticity. Finally, we outline some challenges yet to overcome, especially in the domain of security and defense. These challenges include unconstrained outdoor environment, rPPG form air-platform, night time operation, moving and non-cooperative subjects. These challenges require special algorithmic considerations.

关键词： remote photoplethysmography (rPPG) remote heart rate detection standoff health monitor suspect detection counter terrorism border security homeland security

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Transferring Deep Belief Networks for the Classification of LANDSAT8 remote sensing imagery 5

Transferring Deep Belief Networks for the Classification of ...

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2020 5th International conference on Intelligent Computing and signal processing, ICSP 2020

作者： Zou, Qingqing Chen, Chen School of Computer Engineering Bengbu University Bengbu Anhui233000 China

This paper studies the structure and characteristics of LANDSAT8 OLI data, and transfers Deep Belief Networks model to OLI datasets. Firstly, the sub-sampling window is used to filter the pretraining data, which reduces the calculation cost of the hidden layer feature representation of the model. The parameters of each layer of DBN are trained forward, optimization parameters are used to make the error converge to a low value in back propagation. Finally, the activation function is used to reduce the dimension of the hidden layer of the model, and the output vector is used as the global feature representation of the image. Running DBN to complete the classification of LANDSAT8 remote sensing image in the study area, the experimental results show that the model has completed the classification and achieved high user accuracy and reliability in the case of that all types of the wetlands have similar spectral characteristics. The conclusion is that the pretrained features can be well applied to the LANDSAT8 remote sensing datasets. The accuracy is improved within the scope of cost calculation, and the local minina problem will also occur in deepening model. © 2019 Published under licence by IOP Publishing Ltd.

关键词： remote sensing

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Road Segmentation in Aerial imagery by Deep Neural Networks with 4-Channel Inputs

Road Segmentation in Aerial Imagery by Deep Neural Networks ...

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International conference on Wireless Communications, signal processing and Networking (WiSPNET)

作者： Sushma B Binish Fatimah Priyanshu Raj Electronics and Communication Engineering CMR institute of Technology Bengaluru

ISBN: (纸本)9781665440868;9781665431309

Roads are the essential modules in various applications. Road segmentation from aerial imagery is a significant research topic in the area of remote sensing. Extracting road information with high quality is very significant for many applications. Deep learning framework has been proved to be an effective approach in many image analysis and detail extraction techniques. In this paper, images with multiple colour channels are used to train the low complexity deep Res-UNet model architecture. Res-UNet model is trained with combination of boundary and Lovasz softmax loss functions. Experimental results on the public dataset indicates that proposed method with 4-channel inputs provides better results.

关键词： Wireless communication Training image segmentation image analysis image color analysis Roads Neural networks

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A Novel Actor Dual-Critic Model for remote sensing image Captioning

A Novel Actor Dual-Critic Model for Remote Sensing Image Cap...

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International conference on Pattern Recognition

作者： Ruchika Chavhan Biplab Banerjee Xiao Xiang Zhu Subhasis Chaudhuri Indian Institute of Technology Bombay India Signal Processing in Earth Observation Technical University of Munich Germany

ISBN: (纸本)9781728188089;9781728188096

We deal with the problem of generating textual captions from optical remote sensing (RS) images using the notion of deep reinforcement learning. Due to the high inter-class similarity in reference sentences describing remote sensing data, jointly encoding the sentences and images encourages prediction of captions that are semantically more precise than the ground truth in many cases. To this end, we introduce an Actor Dual-Critic training strategy where a second critic model is deployed in the form of an encoder-decoder RNN to encode the latent information corresponding to the original and generated captions. While all actor-critic methods use an actor to predict sentences for an image and a critic to provide rewards, our proposed encoder-decoder RNN guarantees high-level comprehension of images by sentence-to-image translation. We observe that the proposed model generates sentences on the test data highly similar to the ground truth and is successful in generating even better captions in many critical cases. Extensive experiments on the benchmark remote sensing image Captioning Dataset (RSICD) and the UCM-captions dataset confirm the superiority of the proposed approach in comparison to the previous state-of-the-art where we obtain a gain of sharp increments in both the ROUGE-L and CIDEr measures.

关键词： Training image coding Reinforcement learning Gain measurement Benchmark testing Optical imaging Data models

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