Haze removal in aerial images is a challenging problem due to considerable variation in spatial details and varying contrast. Changes in particulate matter density often lead to degradation in visibility. Therefore, several approaches utilize multi-spectral data as auxiliary information for haze removal. In this paper, we propose SkyGAN for haze removal in aerial images. SkyGAN consists of 1) a domain-aware hazy-to-hyperspectral (H2H) module, and 2) a conditional GAN (cGAN) based multi-cue image-to-image translation module (I2I) for dehazing. The proposed H2H module reconstructs several visual bands from RGB images in an unsupervised manner, which overcomes the lack of hazy hyperspectral aerial image datasets. The module utilizes task supervision and domain adaptation in order to create a "hyperspectral catalyst" for image dehazing. The I2I module uses the hyperspectral catalyst along with a 12-channel multi-cue input and performs effective image dehazing by utilizing the entire visual spectrum. In addition, this work introduces a new dataset, called Hazy Aerial-Image (HAI) dataset, that contains more than 65,000 pairs of hazy and ground truth aerial images with realistic, non-homogeneous haze of varying density. The performance of SkyGAN is evaluated on the recent SateHaze1k dataset as well as the HAI dataset. We also present a comprehensive evaluation of HAI dataset with a representative set of state-of-the-art techniques in terms of PSNR and SSIM.
HDMba: Hyperspectral Remote Sensing Imagery Dehazing with State Space Model
College of Oceanography and Space Informatics, China University of Petroleum (East China)（中国石油大学（华东）海洋与空间信息学院）；National Subsea Centre, Robert Gordon University（罗伯特戈登大学国家海底中心）；Helmholtz Institute Freiberg for Resource Technology, Helmholtz-Zentrum Dresden-Rossendorf（亥姆霍兹德累斯顿罗森多夫研究中心亥姆霍兹弗赖贝格资源技术研究所）
Rochester Institute of Technology（罗切斯特理工学院）；Chester F. Carlson Center for Imaging Science（切斯特·F·卡尔森成像科学中心）；School of Mathematical Sciences, Rochester Institute of Technology（罗切斯特理工学院数学科学学院）
Hyperspectral imaging holds enormous potential to improve the state-of-the-art in aerial vehicle tracking with low spatial and temporal resolutions. Recently, adaptive multi-modal hyperspectral sensors have attracted growing interest due to their ability to record extended data quickly from aerial platforms. In this study, we apply popular concepts from traditional object tracking, namely (1) Kernelized Correlation Filters (KCF) and (2) Deep Convolutional Neural Network (CNN) features to aerial tracking in hyperspectral domain. We propose the Deep Hyperspectral Kernelized Correlation Filter ba…
Hyperspectral cameras can provide unique spectral signatures for consistently distinguishing materials that can be used to solve surveillance tasks. In this paper, we propose a novel real-time hyperspectral likelihood maps-aided tracking method (HLT) inspired by an adaptive hyperspectral sensor. A moving object tracking system generally consists of registration, object detection, and tracking modules. We focus on the target detection part and remove the necessity to build any offline classifiers and tune a large amount of hyperparameters, instead learning a generative target model in an online…
A UAV-Based VNIR Hyperspectral Benchmark Dataset for Landmine and UXO Detection
Rochester Institute of Technology, Chester F. Carlson Center for Imaging Science（罗切斯特理工学院，切斯特·F·卡尔森成像科学中心）；Lamont Doherty Earth Observatory, Columbia University（拉蒙特-多赫蒂地球观测站，哥伦比亚大学）；Demining Research Community（排雷研究社区）
A Multi-purpose Realistic Haze Benchmark with Quantifiable Haze Levels and Ground Truth
DEVCOM Army Research Laboratory（DEVCOM陆军研究实验室）；Tulane University（杜兰大学）；The University of Texas at Austin（得克萨斯大学奥斯汀分校）；DEVCOM Chemical and Biological Center（DEVCOM化学生物中心）
Inferring spectral signatures from ground based natural images has acquired a lot of interest in applied deep learning. In contrast to the spectra of ground based images, aerial spectral images have low spatial resolution and suffer from higher noise interference. In this paper, we train a conditional adversarial network to learn an inverse mapping from a trichromatic space to 31 spectral bands within 400 to 700 nm. The network is trained on AeroCampus, a first of its kind aerial hyperspectral dataset. AeroCampus consists of high spatial resolution color images and low spatial resolution hyper…
University of Minnesota（明尼苏达大学）；University of California-Davis（加州大学戴维斯分校）
Crop production needs to increase in a sustainable manner to meet the growing global demand for food. To identify crop varieties with high yield potential, plant scientists and breeders evaluate the performance of hundreds of lines in multiple locations over several years. To facilitate the process of selecting advanced varieties, an automated framework was developed in this study. A hyperspectral camera was mounted on an unmanned aerial vehicle to collect aerial imagery with high spatial and spectral resolution. Aerial images were captured in two consecutive growing seasons from three experim…
Manchester Metropolitan University（曼彻斯特城市大学）；Hebei Agriculture University（河北农业大学）
Late blight disease is one of the most destructive diseases in potato crop, leading to serious yield losses globally. Accurate diagnosis of the disease at early stage is critical for precision disease control and management. Current farm practices in crop disease diagnosis are based on manual visual inspection, which is costly, time consuming, subject to individual bias. Recent advances in imaging sensors (e.g. RGB, multiple spectral and hyperspectral cameras), remote sensing and machine learning offer the opportunity to address this challenge. Particularly, hyperspectral imagery (HSI) combini…
Rochester Institute of Technology（罗切斯特理工学院）；ON Semiconductor（安森美半导体）
We investigate applying convolutional neural network (CNN) architecture to facilitate aerial hyperspectral scene understanding and present a new hyperspectral dataset-AeroRIT-that is large enough for CNN training. To date the majority of hyperspectral airborne have been confined to various sub-categories of vegetation and roads and this scene introduces two new categories: buildings and cars. To the best of our knowledge, this is the first comprehensive large-scale hyperspectral scene with nearly seven million pixel annotations for identifying cars, roads, and buildings. We compare the perform…
Evaluation of Deep Learning Semantic Segmentation for Land Cover Mapping on Multispectral, Hyperspectral and High Spatial Aerial Imagery
Center for Space and Remote Sensing Research, National Central University（国立中央大学太空及遥测研究中心）；Department of Computer Science and Information Engineering, National Central University（国立中央大学资讯工程学系）
Self-supervised Learning for Hyperspectral Images of Trees
1 Department of Computer Science, University of Texas at El Paso, El Paso, TX 79968, USA 2 School of Sustainable Engineering；Built Environment, Arizona State University, Tempe, AZ 85281, USA 3 Texas A\&M AgriLife Research, Texas A\&M University, El Paso, TX 79927, USA；Texas A\&M AgriLife Research Texas A\&M University；Department of Computer Science University of Texas at El Paso；1 Department of Computer Science, University of Texas at El Paso, El Paso, TX 79968, USA；2 School of Sustainable Engineering；Built Environment, Arizona State University, Tempe, AZ 85281, USA；3 Texas A\&M AgriLife Research, Texas A\&M University, El Paso, TX 79927, USA；Texas A\&M AgriLife Research；Texas A\&M University；Department of Computer Science；University of Texas at El Paso
Power Line Aerial Image Restoration under dverse Weather: Datasets and Baselines
School of Electrical Engineering and Automation, Nantong University（南通大学电气工程及其自动化学院）；School of Artificial Intelligence and Computer Science, Nantong University（南通大学人工智能与计算机科学学院）；School of Information Science and Technology, Nantong University（南通大学信息科学与技术学院）；College of Computer and Software Engineering, Hohai University（河海大学计算机与软件学院）；School of Electronic and Information Engineering, Suzhou University of Science and Technology（苏州科技大学电子与信息工程学院）；School of Information and Communication Technology, Griffith University（格里菲斯大学信息与通信技术学院）
