The article discusses the use of low cost System-On-Module (SOM) platforms for the implementation of efficient hyperspectral imaging (HSI) processors for application in autonomous driving. The work addresses the challenges of shaping and deploying multiple layer fully convolutional networks (FCN) for low-latency, on-board image semantic segmentation using resource- and power-constrained processing devices. The paper describes in detail the steps followed to redesign and customize a successfully trained HSI segmentation lightweight FCN that was previously tested on a high-end heterogeneous multiprocessing system-on-chip (MPSoC) to accommodate it to the constraints imposed by a low-cost SOM. This SOM features a lower-end but much cheaper MPSoC suitable for the deployment of automatic driving systems (ADS). In particular the article reports the data- and hardware-specific quantization techniques utilized to fit the FCN into a commercial fixed-point programmable AI coprocessor IP, and proposes a full customized post-training quantization scheme to reduce computation and storage costs without compromising segmentation accuracy.
University of Southern California（南加利福尼亚大学）；Information Sciences Institute（信息科学研究所）
Hyperspectral cameras generate a large amount of data due to the presence of hundreds of spectral bands as opposed to only three channels (red, green, and blue) in traditional cameras. This requires a significant amount of data transmission between the hyperspectral image sensor and a processor used to classify/detect/track the images, frame by frame, expending high energy and causing bandwidth and security bottlenecks. To mitigate this problem, we propose a form of processing-in-pixel (PIP) that leverages advanced CMOS technologies to enable the pixel array to perform a wide range of complex …
Silesian University of Technology（西里西亚工业大学）；KP Labs（KP实验室）
Hyperspectral satellite imaging attracts enormous research attention in the remote sensing community, hence automated approaches for precise segmentation of such imagery are being rapidly developed. In this letter, we share our observations on the strategy for validating hyperspectral image segmentation algorithms currently followed in the literature, and show that it can lead to over-optimistic experimental insights. We introduce a new routine for generating segmentation benchmarks, and use it to elaborate ready-to-use hyperspectral training-test data partitions. They can be utilized for fair…
Satellite imagery allows a plethora of applications ranging from weather forecasting to land surveying. The rapid development of computer vision systems could open new horizons to the utilization of satellite data due to the abundance of large volumes of data. However, current state-of-the-art computer vision systems mainly cater to applications that mainly involve natural images. While useful, those images exhibit a different distribution from satellite images in addition to having more spectral channels. This allows the use of pretrained deep learning models only in a subset of spectral chan…
Hamlyn Centre for Robotic Surgery, Imperial College London（伦敦帝国理工学院哈姆林机器人手术中心）；Department of Computing, Imperial College London（伦敦帝国理工学院计算系）；Department of Surgery and Cancer, Imperial College London（伦敦帝国理工学院外科与肿瘤学系）；Centre for Medical Image Computing, University College London（伦敦大学学院医学影像计算中心）；Department of Computer Science, University College London（伦敦大学学院计算机科学系）
Tissue surface shape and reflectance spectra provide rich intra-operative information useful in surgical guidance. We propose a hybrid system which displays an endoscopic image with a fast joint inspection of tissue surface shape using structured light (SL) and hyperspectral imaging (HSI). For SL a miniature fibre probe is used to project a coloured spot pattern onto the tissue surface. In HSI mode standard endoscopic illumination is used, with the fibre probe collecting reflected light and encoding the spatial information into a linear format that can be imaged onto the slit of a spectrograph…
Jai Shriram Engineering College（贾伊·施里拉姆工程学院）；Kongu Engineering College（孔古工程学院）
Hyperspectral image (HSI) classification plays a significant in the field of remote sensing due to its ability to provide spatial and spectral information. Due to the rapid development and increasing of hyperspectral remote sensing technology, many methods have been developed for HSI classification but still a lack of achieving the better performance. A Frost Filtered Scale-Invariant Feature Transformation based MultiLayer Perceptron Classification (FFSIFT-MLPC) technique is introduced for classifying the hyperspectral image with higher accuracy and minimum time consumption. The FFSIFT-MLPC te…
Fraunhofer Heinrich Hertz Institute（弗劳恩霍夫海因里希·赫兹研究所）；Humboldt-University（洪堡大学）
Spectral imaging technologies have rapidly evolved during the past decades. The recent development of single-camera-one-shot techniques for hyperspectral imaging allows multiple spectral bands to be captured simultaneously (3x3, 4x4 or 5x5 mosaic), opening up a wide range of applications. Examples include intraoperative imaging, agricultural field inspection and food quality assessment. To capture images across a wide spectrum range, i.e. to achieve high spectral resolution, the sensor design sacrifices spatial resolution. With increasing mosaic size, this effect becomes increasingly detriment…
Hyperspectral Imaging-Based Grain Quality Assessment With Limited Labelled Data
School of Information Technology, Deakin University（迪肯大学信息技术学院）；Institute of Innovation, Science and Sustainability, Federation University（联邦大学创新、科学与可持续发展研究所）
Real-Time Compressed Sensing for Joint Hyperspectral Image Transmission and Restoration for CubeSat
National Cheng Kung University（成功大学）；Institute of Data Science（数据科学研究所）；Miin Wu School of Computing（米润计算机学院）；Department of Statistics（统计学系）；Department of Computer Science（计算机科学系）
College of Computer Science and Software Engineering, Shenzhen University（深圳大学计算机与软件学院）；SZU Branch, Shenzhen Institute of Artificial Intelligence and Robotics for Society（深圳市人工智能与机器人研究院深圳大学分部）；Department of Computer Science and Engineering, Southern University of Science and Technology（南方科技大学计算机科学与工程系）
With the rapid development of deep learning technology and improvement in computing capability, deep learning has been widely used in the field of hyperspectral image (HSI) classification. In general, deep learning models often contain many trainable parameters and require a massive number of labeled samples to achieve optimal performance. However, in regard to HSI classification, a large number of labeled samples is generally difficult to acquire due to the difficulty and time-consuming nature of manual labeling. Therefore, many research works focus on building a deep learning model for HSI c…
SLIMBRAIN: Augmented Reality Real-Time Acquisition and Processing System For Hyperspectral Classification Mapping with Depth Information for In-Vivo Surgical Procedures
Research Center on Software Technologies and Multimedia Systems, Universidad Politécnica de Madrid（马德里理工大学软件技术与多媒体系统研究中心）；Department of Neurosurgery, Hospital Universitario 12 de Octubre, Universidad Complutense de Madrid（马德里康普顿斯大学十月十二日大学医院神经外科）
Nanjing Forestry University（南京林业大学）；University of Michigan（密歇根大学）；FXPAL inc.（FX帕洛阿尔托实验室公司）；Zhejiang A&F University（浙江农林大学）；University of Calgary（卡尔加里大学）；Chinese Academy of Sciences（中国科学院）；University of Chinese Academy of Sciences（中国科学院大学）；FX Palo Alto Laboratory Inc.（FX帕洛阿尔托实验室公司）
Sliding-window based low-rank matrix approximation (LRMA) is a technique widely used in hyperspectral images (HSIs) denoising or completion. However, the uncertainty quantification of the restored HSI has not been addressed to date. Accurate uncertainty quantification of the denoised HSI facilitates to applications such as multi-source or multi-scale data fusion, data assimilation, and product uncertainty quantification, since these applications require an accurate approach to describe the statistical distributions of the input data. Therefore, we propose a prior-free closed-form element-wise …
AI-driven Web Application for Early Detection of Sudden Death Syndrome (SDS) in Soybean Leaves Using Hyperspectral Images and Genetic Algorithm
Machine Vision and Optical Sensor (MVOS) Lab, Dept. of Agricultural and Biosystems Engineering, South Dakota State University, Brookings, SD, USA（南达科他州立大学农业与生物系统工程系机器视觉与光学传感器实验室）；Department of Agronomy, Horticulture and Plant Science, South Dakota State University, Brookings, SD, USA（南达科他州立大学园艺与植物科学系）；Department of Agricultural and Biological Engineering, University of Florida, Gainesville, FL, USA（佛罗里达大学农业与生物工程系）；USDA-ARS Environmental Microbial and Food Safety Lab, Beltsville Agricultural Research Center, MD, USA（美国农业部ARS环境微生物与食品安全实验室，贝尔斯维尔农业研究中心）
Hyperspectral Image Classification Based on Faster Residual Multi-branch Spiking Neural Network
Henan University（河南大学）；Henan Key Laboratory of Big Data Analysis and Processing（河南省大数据分析与处理重点实验室）；School of Computer and Information Engineering, Henan University（河南大学计算机与信息工程学院）；Department of Clinical Laboratory, Huaihe Hospital of Henan University（河南大学淮河医院检验科）
driveU / Institute of Measurement, Control and Microtechnology, Ulm University（乌尔姆大学测量、控制与微技术研究所 / driveU）；Daimler AG（戴姆勒股份公司）
A rigorous formulation of the dynamics of a signal processing scheme aimed at dense signal scanning without any loss in accuracy is introduced and analyzed. Related methods proposed in the recent past lack a satisfactory analysis of whether they actually fulfill any exactness constraints. This is improved through an exact characterization of the requirements for a sound sliding window approach. The tools developed in this paper are especially beneficial if Convolutional Neural Networks are employed, but can also be used as a more general framework to validate related approaches to signal scann…
