In this study, we tackle the challenge of identifying plant species from ultra high resolution (UHR) remote sensing images. Our approach involves introducing an RGB remote sensing dataset, characterized by millimeter-level spatial resolution, meticulously curated through several field expeditions across a mountainous region in France covering various landscapes. The task of plant species identification is framed as a semantic segmentation problem for its practical and efficient implementation across vast geographical areas. However, when dealing with segmentation masks, we confront instances where distinguishing boundaries between plant species and their background is challenging. We tackle this issue by introducing a fuzzy loss within the segmentation model. Instead of utilizing one-hot encoded ground truth (GT), our model incorporates Gaussian filter refined GT, introducing stochasticity during training. First experimental results obtained on both our UHR dataset and a public dataset are presented, showing the relevance of the proposed methodology, as well as the need for future improvement.
Department of Electrical Engineering, Jadavpur University（贾达普大学电气工程系）
One of the most straightforward, direct and efficient approaches to Image Segmentation is Image Thresholding. Multi-level Image Thresholding is an essential viewpoint in many image processing and Pattern Recognition based real-time applications which can effectively and efficiently classify the pixels into various groups denoting multiple regions in an Image. Thresholding based Image Segmentation using fuzzy entropy combined with intelligent optimization approaches are commonly used direct methods to properly identify the thresholds so that they can be used to segment an Image accurately. In t…
Institute of Electronics, Chinese Academy of Sciences（中国科学院电子学研究所）；University of Chinese Academy of Sciences（中国科学院大学）
Deep learning based object detection has achieved great success. However, these supervised learning methods are data-hungry and time-consuming. This restriction makes them unsuitable for limited data and urgent tasks, especially in the applications of remote sensing. Inspired by the ability of humans to quickly learn new visual concepts from very few examples, we propose a training-free, one-shot geospatial object detection framework for remote sensing images. It consists of (1) a feature extractor with remote sensing domain knowledge, (2) a multi-level feature fusion method, (3) a novel simil…
In present days remote sensing is most used application in many sectors. This remote sensing uses different images like multispectral, hyper spectral or ultra spectral. The remote sensing image classification is one of the significant method to classify image. In this state we classify the maximum likelihood classification with fuzzy logic. In this we experimenting fuzzy logic like spatial, spectral texture methods in that different sub methods to be used for image classification.
Satellite remote imaging enables the detailed study of land use patterns on a global scale. We investigate the possibility to improve the information content of traditional land use classification by identifying the nature of industrial sites from medium-resolution remote sensing images. In this work, we focus on classifying different types of power plants from Sentinel-2 imaging data. Using a ResNet-50 deep learning model, we are able to achieve a mean accuracy of 90.0% in distinguishing 10 different power plant types and a background class. Furthermore, we are able to identify the cooling me…
Recent research on the application of remote sensing and deep learning-based analysis in precision agriculture demonstrated a potential for improved crop management and reduced environmental impacts of agricultural production. Despite the promising results, the practical relevance of these technologies for field deployment requires novel algorithms that are customized for analysis of agricultural images and robust to implementation on natural field imagery. The paper presents an approach for analyzing aerial images of a potato (Solanum tuberosum L.) crop using deep neural networks. The main ob…
Texas A&M University（德克萨斯农工大学）；Mississippi State University（密西西比州立大学）；U.S.D.A. Agriculture Research Service（美国农业部农业研究局）；U.S.D.A.- APHIS PPQ S&T（美国农业部动植物卫生检验局植物保护与检疫科学技术处）；Utah State University（犹他州立大学）；Texas A&M AgriLife Research & Extension Center（德克萨斯农工大学生命科学研究与推广中心）
The cotton boll weevil, Anthonomus grandis Boheman is a serious pest to the U.S. cotton industry that has cost more than 16 billion USD in damages since it entered the United States from Mexico in the late 1800s. This pest has been nearly eradicated; however, southern part of Texas still faces this issue and is always prone to the pest reinfestation each year due to its sub-tropical climate where cotton plants can grow year-round. Volunteer cotton (VC) plants growing in the fields of inter-seasonal crops, like corn, can serve as hosts to these pests once they reach pin-head square stage (5-6 l…
This paper presents fast procedures for thermal infrared remote sensing in dark, GPS-denied environments, such as those found in industrial plants such as in High-Voltage Direct Current (HVDC) converter stations. These procedures are based on the combination of the depth estimation obtained from either a 1-Dimensional LIDAR laser or a 2-Dimensional Hokuyo laser or a 3D MultiSense SLB laser sensor and the visible and thermal cameras from a FLIR Duo R dual-sensor thermal camera. The combination of these sensors/cameras is suitable to be mounted on Unmanned Aerial Vehicles (UAVs) and/or robots in…
Image fusion is the process of integrating multiple images of the same scene into a single fused image to reduce uncertainty and minimizing redundancy while extracting all the useful information from the source images. Image fusion process is required for different applications like medical imaging, remote sensing, medical imaging, machine vision, biometrics and military applications where quality and critical information is required. In this paper, image fusion using fuzzy and neuro fuzzy logic approaches utilized to fuse images from different sensors, in order to enhance visualization. The p…
University of Manouba（马努巴大学）；Taibah University（塔伊巴大学）；Prince Sultan University（苏丹王子大学）；Arab Open University（阿拉伯开放大学）；University of Sfax（斯法克斯大学）
Recently, Convolutional Neural Networks (CNNs) have made a great performance for remote sensing image classification. Plant recognition using CNNs is one of the active deep learning research topics due to its added-value in different related fields, especially environmental conservation and natural areas preservation. Automatic recognition of plants in protected areas helps in the surveillance process of these zones and ensures the sustainability of their ecosystems. In this work, we propose an Enhanced Randomly Initialized Convolutional Neural Network (ERI-CNN) for the recognition of columnar…
A Hybrid Technique for Plant Disease Identification and Localisation in Real-time
Indian Institute of Technology Kanpur（印度理工学院坎普尔分校）；University of Washington（华盛顿大学）；National P.G. College（国立PG学院）；C.S.A. University of Agriculture & Technology（CSA农业与技术大学）
VNRVJIET（瓦利瓦纳蒂·纳加尔朱纳·拉奥·维贾雅·贾瓦哈尔·工程技术学院）；GNITS（吉特工程学院）；JNTU College of Engineering（贾瓦哈拉尔·尼赫鲁科技大学工程学院）
Image fusion is to reduce uncertainty and minimize redundancy in the output while maximizing relevant information from two or more images of a scene into a single composite image that is more informative and is more suitable for visual perception or processing tasks like medical imaging, remote sensing, concealed weapon detection, weather forecasting, biometrics etc. Image fusion combines registered images to produce a high quality fused image with spatial and spectral information. The fused image with more information will improve the performance of image analysis algorithms used in different…
LAI (Leaf Area Index) is of great importance for crop yield estimation in agronomy. It is directly related to plant growth status, net assimilation rate, plant photosynthesis, and carbon dioxide in the environment. How to measure LAI accurately and efficiently is the key to the crop yield estimation problem. Manual measurement consumes a lot of human resources and material resources. Remote sensing technology is not suitable for near-Earth LAI measurement. Besides, methods based on traditional digital image processing are greatly affected by environmental noise and image exposure. Nowadays, de…
FTCFormer: Fuzzy Token Clustering Transformer for Image Classification
School of Advanced Technology, Xi'an Jiaotong-Liverpool University, Suzhou, China（西安交通大学利物浦大学先进技术学院）；Department of Mathematical Sciences, University of Liverpool, Liverpool, United Kingdom（利物浦大学数学科学系）；Department of Computer Science, University of Liverpool, Liverpool, United Kingdom（利物浦大学计算机科学系）
Early identification of COVID-19 using a deep model trained on Chest X-Ray and CT images has gained considerable attention from researchers to speed up the process of identification of active COVID-19 cases. These deep models act as an aid to hospitals that suffer from the unavailability of specialists or radiologists, specifically in remote areas. Various deep models have been proposed to detect the COVID-19 cases, but few works have been performed to prevent the deep models against adversarial attacks capable of fooling the deep model by using a small perturbation in image pixels. This paper…
Edinburgh Napier University（爱丁堡龙比亚大学）；University of Edinburgh（爱丁堡大学）；The Alan Turing Institute（艾伦·图灵研究所）
Several imaging applications (vessels, retina, plant roots, road networks from satellites) require the accurate segmentation of thin structures for subsequent analysis. Discontinuities (gaps) in the extracted foreground may hinder down-stream image-based analysis of biomarkers, organ structure and topology. In this paper, we propose a general post-processing technique to recover such gaps in large-scale segmentation masks. We cast this problem as a blind inpainting task, where the regions of missing lines in the segmentation masks are not known to the algorithm, which we solve with an adversar…
University of St. Gallen（圣加仑大学）；Sociovestix Labs Ltd.（Sociovestix实验室有限公司）；ISS Inc.（ISS公司）
The major driver of global warming has been identified as the anthropogenic release of greenhouse gas (GHG) emissions from industrial activities. The quantitative monitoring of these emissions is mandatory to fully understand their effect on the Earth's climate and to enforce emission regulations on a large scale. In this work, we investigate the possibility to detect and quantify industrial smoke plumes from globally and freely available multi-band image data from ESA's Sentinel-2 satellites. Using a modified ResNet-50, we can detect smoke plumes of different sizes with an accuracy of 94.3%. …
Technische Universität Berlin（柏林工业大学）；Bundesanstalt für Geowissenschaften und Rohstoffe（德国联邦地球科学与自然资源研究所）
Subsurface tile drainage pipes provide agronomic, economic and environmental benefits. By lowering the water table of wet soils, they improve the aeration of plant roots and ultimately increase the productivity of farmland. They do however also provide an entryway of agrochemicals into subsurface water bodies and increase nutrition loss in soils. For maintenance and infrastructural development, accurate maps of tile drainage pipe locations and drained agricultural land are needed. However, these maps are often outdated or not present. Different remote sensing (RS) image processing techniques h…
Tracking Phenological Status and Ecological Interactions in a Hawaiian Cloud Forest Understory using Low-Cost Camera Traps and Visual Foundation Models
Computer Science, The Ohio State University, Columbus, Ohio, USA（俄亥俄州立大学计算机科学系）；Computer Science, The University of Puerto Rico Rio Piedras, San Juan, Puerto Rico（波多黎各大学里奥皮埃拉斯分校计算机科学系）；Computer Science, McGill University, Montreal, Quebec, Canada（麦吉尔大学计算机科学系）；Battele Ecology, Neon Domain 20, Hilo, Hawaii（Battele生态研究所）；Ecology, Princeton University, Princeton, New Jersey, USA（普林斯顿大学生态学系）
Remote Sensing Based Crop Health Classification Using NDVI and Fully Connected Neural Networks
Sethu Institute of Technology（塞图理工学院）；College of Engineering and IT, University of Dubai（迪拜大学工程与信息技术学院）；Mohammed Bin Rashid Space Centre (MBRSC)（穆罕默德·本·拉希德航天中心）
A Fast Fourier Convolutional Deep Neural Network For Accurate and Explainable Discrimination Of Wheat Yellow Rust And Nitrogen Deficiency From Sentinel-2 Time-Series Data
The University of Leeds（利兹大学）；Manchester Metropolitan University（曼彻斯特城市大学）；University of Córdoba（科尔多瓦大学）；The University of Manchester（曼彻斯特大学）；Beijing University of Technology（北京工业大学）；Yangzhou University（扬州大学）
GreenHyperSpectra: A multi-source hyperspectral dataset for global vegetation trait prediction
Center for Scalable Data Analytics and Artificial Intelligence (ScaDS.AI)（可扩展数据与人工智能研究中心）；Leipzig University（莱比锡大学）；Mila – Québec AI Institute（魁北克人工智能研究所）；McGill University（麦吉尔大学）；University of Salford（萨尔福德大学）；Colorado State University（科罗拉多州立大学）；University of Wisconsin（威斯康星大学）；Simon Fraser University（西蒙弗雷泽大学）；German Centre for Integrative Biodiversity Research (iDiv)（整合生物多样性研究中心（iDiv））；Helmholtz-Centre for Environmental Research (UFZ)（环境研究中心（UFZ））；University of Freiburg（弗赖堡大学）
Hyperspectral imaging is a cutting-edge type of remote sensing used for mapping vegetation properties, rock minerals and other materials. A major drawback of hyperspectral imaging devices is their intrinsic low spatial resolution. In this paper, we propose a method for increasing the spatial resolution of a hyperspectral image by fusing it with an image of higher spatial resolution that was obtained with a different imaging modality. This is accomplished by solving a variational problem in which the regularization functional is the directional total variation. To accommodate for possible mis-r…
