Progress and Challenges in Intelligent Remote Sensing Satellite Systems

Zhang, Bing; Wu, Yuanfeng; Zhao, Boya; Chanussot, Jocelyn; Hong, Danfeng; Yao, Jing; Gao, Lianru

doi:10.1109/jstars.2022.3148139

Cited by 156 publications

(76 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…HS images can be compressed first to reduce the pressure on channel transmission. Therefore, the HS CS technique is conducive to onboard burst transmission and real-time processing in RS [152].…”

Section: Hs Csmentioning

confidence: 99%

Tensor Decompositions for Hyperspectral Data Processing in Remote Sensing: A Comprehensive Review

Wang¹,

Hong²,

Han³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

Owing to the rapid development of sensor technology, hyperspectral (HS) remote sensing (RS) imaging has provided a significant amount of spatial and spectral information for the observation and analysis of the Earth's surface at a distance of data acquisition devices, such as aircraft, spacecraft, and satellite. The recent advancement and even revolution of the HS RS technique offer opportunities to realize the full potential of various applications, while confronting new challenges for efficiently processing and analyzing the enormous HS acquisition data. Due to the maintenance of the 3-D HS inherent structure, tensor decomposition has aroused widespread concern and research in HS data processing tasks over the past decades. In this article, we aim at presenting a comprehensive overview of tensor decomposition, specifically contextualizing the five broad topics in HS data processing, and they are HS restoration, compressed sensing, anomaly detection, superresolution, and spectral unmixing. For each topic, we elaborate on the remarkable achievements of tensor decomposition models for HS RS with a pivotal description of the existing methodologies and a representative exhibition on the experimental results. As a result, the remaining challenges of the follow-up research directions are outlined and discussed from the perspective of the real HS RS practices and tensor decomposition merged with advanced priors and even with deep neural networks. This article summarizes different tensor decomposition-based HS data processing methods and categorizes them into different classes from simple adoptions to complex combinations with other Manuscript received XX, 2022; revised XX, 2022.

show abstract

Section: Hs Csmentioning

confidence: 99%

Tensor Decompositions for Hyperspectral Data Processing in Remote Sensing: A Comprehensive Review

Wang¹,

Hong²,

Han³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…With the development of aerospace technology, the payloads of satellites are pursuing low power consumption, small mass and small size, which impose power limitations and resources limitations on spaceborne computation devices [20]. In particular, micro-satellites (MicroSats) [21] and nano-satellites (NanoSats) [22] are strictly constrained in terms of cost, power, and resources due to their extremely small sizes and light weights.…”

Section: Introductionmentioning

confidence: 99%

Automatic Deployment of Convolutional Neural Networks on FPGA for Spaceborne Remote Sensing Application

Yan

Zhang

et al. 2022

Remote Sensing

View full text Add to dashboard Cite

In recent years, convolutional neural network (CNN)-based algorithms have been widely used in remote sensing image processing and show tremendous performance in a variety of application fields. However, large amounts of data and intensive computations make the deployment of CNN-based algorithms a challenging problem, especially for the spaceborne scenario where resources and power consumption are limited. To tackle this problem, this paper proposes an automatic CNN deployment solution on resource-limited field-programmable gate arrays (FPGAs) for spaceborne remote sensing applications. Firstly, a series of hardware-oriented optimization methods are proposed to reduce the complexity of the CNNs. Secondly, a hardware accelerator is designed. In this accelerator, a reconfigurable processing engine array with efficient convolutional computation architecture is used to accelerate CNN-based algorithms. Thirdly, to bridge the optimized CNNs and hardware accelerator, a compilation toolchain is introduced into the deployment solution. Through the automatic conversion from CNN models to hardware instructions, various networks can be deployed on hardware in real-time. Finally, we deployed an improved VGG16 network and an improved YOLOv2 network on Xilinx AC701 to evaluate the effectiveness of the proposed deployment solution. The experiments show that with only 3.407 W power consumption and 94 DSP consumption, our solution achieves 23.06 giga operations per second (GOPS) throughput in the improved VGG16 and 22.17 GOPS throughput in the improved YOLOv2. Compared to the related works, the DSP efficiency of our solution is improved by 1.3–2.7×.

show abstract

“…The images thus obtained are usually generated by a pre-configured HSI camera installed on either mobile (e.g. satellites, drones, air-crafts) or static (e.g., indoor, rooms, labs) setups depending upon the problem at hand [2]- [11]. Thereby, HSI sensors gather a huge amount of data from hundreds of contiguous spectral bands [12], [13].…”

Section: Introductionmentioning

confidence: 99%

Hybrid Dense Network With Attention Mechanism for Hyperspectral Image Classification

Khan

Mazzara

Distefano

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

View full text Add to dashboard Cite

The nonlinear relation between the spectral information and the corresponding objects (complex physiognomies) makes pixel-wise classification challenging for conventional methods. To deal with nonlinearity issues in Hyperspectral Image Classification (HSIC), Convolutional Neural Networks (CNN) are more suitable, indeed. However, fixed kernel sizes make traditional CNN too specific, neither flexible nor conducive to feature learning, thus impacting on the classification accuracy. The convolution of different kernel size networks may overcome this problem by capturing more discriminating and relevant information. In light of this, the proposed solution aims at combining the core idea of 3D and 2D Inception net with the Attention mechanism to boost the HSIC CNN performance in a hybrid scenario. The resulting Attention-fused Hybrid Network (AfNet) is based on three attention-fused parallel hybrid subnets with different kernels in each block repeatedly using highlevel features to enhance the final ground-truth maps. In short, AfNet is able to selectively filter out the discriminative features critical for classification. Several tests on HSI datasets provided competitive results for AfNet compared to state-of-the-art models.

show abstract

Progress and Challenges in Intelligent Remote Sensing Satellite Systems

Cited by 156 publications

References 32 publications

Tensor Decompositions for Hyperspectral Data Processing in Remote Sensing: A Comprehensive Review

Tensor Decompositions for Hyperspectral Data Processing in Remote Sensing: A Comprehensive Review

Automatic Deployment of Convolutional Neural Networks on FPGA for Spaceborne Remote Sensing Application

Hybrid Dense Network With Attention Mechanism for Hyperspectral Image Classification

Contact Info

Product

Resources

About