scholarly journals PVANET-HOUGH: DETECTION AND LOCATION OF CENTER PIVOT IRRIGATION SYSTEMS FROM SENTINEL-2 IMAGES

2020 ◽  
Vol V-3-2020 ◽  
pp. 559-564
Author(s):  
J. W. Tang ◽  
D. Arvor ◽  
T. Corpetti ◽  
P. Tang
Keyword(s):  
Deep Learning ◽  
Object Detection ◽  
Water Consumption ◽  
Hough Transform ◽  
False Alarms ◽  
Irrigation Systems ◽  
Mato Grosso ◽  
Center Pivot ◽  
Precise Shape ◽  
Sentinel 2

Abstract. Irrigation systems play an important role in agriculture. As being labor-saving and water consumption efficient, center pivot irrigation systems are popular in many countries. Monitoring the distribution of center pivot irrigation systems can provide important information for agriculture production, water consumption and land use. Deep learning has become an effective method for image classification and object detection. In this paper, a new method to detect the precise shape of center pivot irrigation systems, PVANET-Hough, is proposed. The proposed method combines a lightweight real-time object detection network PVANET based on deep learning and accurate shape detection Hough transform to detect and accurately locate center pivot irrigation systems. The method proposed in this paper does not need any preprocessing, PVANET is lightweight and fast, Hough transform can accurately detect the shape of center pivot irrigation systems, and reduce the false alarms of PVANET at the mean time. Experiments with the Sentinel-2 images in Mato Grosso demonstrated the effectiveness of the proposed method.

scholarly journals Mapping Center Pivot Irrigation Systems in the Southern Amazon from Sentinel-2 Images

Water ◽  
2021 ◽  
Vol 13 (3) ◽  
pp. 298
Author(s):  
Jiwen Tang ◽  
Damien Arvor ◽  
Thomas Corpetti ◽  
Ping Tang
Keyword(s):  
Deep Learning ◽  
Object Detection ◽  
Image Classification ◽  
Water Consumption ◽  
Hough Transform ◽  
Classification Model ◽  
Irrigation Systems ◽  
Mato Grosso ◽  
Center Pivot ◽  
Sentinel 2

Irrigation systems play an important role in agriculture. Center pivot irrigation systems are popular in many countries as they are labor-saving and water consumption efficient. Monitoring the distribution of center pivot irrigation systems can provide important information for agricultural production, water consumption and land use. Deep learning has become an effective method for image classification and object detection. In this paper, a new method to detect the precise shape of center pivot irrigation systems is proposed. The proposed method combines a lightweight real-time object detection network (PVANET) based on deep learning, an image classification model (GoogLeNet) and accurate shape detection (Hough transform) to detect and accurately delineate center pivot irrigation systems and their associated circular shape. PVANET is lightweight and fast and GoogLeNet can reduce the false detections associated with PVANET, while Hough transform can accurately detect the shape of center pivot irrigation systems. Experiments with Sentinel-2 images in Mato Grosso achieved a precision of 95% and a recall of 95.5%, which demonstrated the effectiveness of the proposed method. Finally, with the accurate shape of center pivot irrigation systems detected, the area of irrigation in the region was estimated.

scholarly journals Increasing Shape Bias to Improve the Precision of Center Pivot Irrigation System Detection

2021 ◽  
Vol 13 (4) ◽  
pp. 612
Author(s):  
Jiwen Tang ◽  
Zheng Zhang ◽  
Lijun Zhao ◽  
Ping Tang
Keyword(s):  
Object Detection ◽  
Irrigation System ◽  
Semantic Segmentation ◽  
Training Data ◽  
Irrigation Systems ◽  
Mato Grosso ◽  
Training Scheme ◽  
Shape Bias ◽  
Center Pivot ◽  
Improved Performance

Irrigation is indispensable in agriculture. Center pivot irrigation systems are popular means of irrigation since they are water-efficient and labor-saving. Monitoring center pivot irrigation systems provides important information for the understanding of agricultural production, water resources consumption and environmental change. Deep learning has become an effective approach for object detection and semantic segmentation. Recent studies have shown that convolutional neural networks (CNNs) are prone to be texture-biased rather than shape-biased, and increasing shape bias can improve the robustness and performance of CNNs. In this study, a simple yet effective method was proposed to increase shape bias in object detection networks to improve the precision of center pivot irrigation system detection. We extracted edge images of training samples and integrated them into the training data to increase shape bias in the networks. With the proposed shape increasing training scheme, we evaluated and compared PVANET and YOLOv4. Experiments with the images in Mato Grosso have shown that both PVANET and YOLOv4 achieved improved performance, which demonstrated the validity of the proposed method.

scholarly journals ANÁLISE ESPAÇO-TEMPORAL DA OCORRÊNCIA DE SISTEMAS DE IRRIGAÇÃO POR PIVÔ CENTRAL EM REGIÕES AGRÍCOLAS NO ESTADO DE MATO GROSSO

Nativa ◽  
2020 ◽  
Vol 8 (3) ◽  
pp. 344-351
Author(s):  
Edgar Nogueira Demarqui ◽  
Lígia Manccini Barros Demarqui
Keyword(s):  
Agricultural Production ◽  
Irrigation System ◽  
Temporal Analysis ◽  
Aerial Images ◽  
Annual Growth Rate ◽  
Irrigation Systems ◽  
Mato Grosso ◽  
Irrigated Area ◽  
Center Pivot ◽  
Agricultural Regions

A utilização de técnicas de irrigação visando a produção agrícola tem apresentado crescimento nos últimos anos, sendo que o sistema de irrigação por pivô central (PC) também avançou no quantitativo de unidades implantadas e no total de área abrangida. Isto se deve às características desse tipo de sistema, as quais mostram-se vantajosas para o produtor rural, tais como economia de mão-de-obra e facilidade de operação, entre outras. O formato circular deste tipo de sistema facilita sua identificação através de técnicas de Geoprocessamento aplicadas nas análises de imagens orbitais ou aéreas, permitindo análises quantitativas e de espacialização. Neste contexto, o presente trabalho teve como objetivo mapear, por meio de imagens orbitais, as áreas irrigadas por PC no período de 11 anos (2004 a 2015) referente à três regiões que possuem destaque na produção agrícola no Estado de Mato Grosso: Médio Norte, Parecis e Sudeste. A região Médio Norte apresentou taxa média de crescimento anual de 10,96% na sua área irrigada, a região Sudeste 6,75% e a região dos Parecis 4,28%. A evolução média no número de pivôs nestas áreas foi da ordem de 8,07% ao ano, passando de 259 unidades em 2004, para 586 no ano de 2015, totalizando um crescimento de 226,25% no período.Palavras-chave: áreas irrigadas; produção agrícola; geoprocessamento; sensoriamento remoto. SPACE-TEMPORAL ANALYSIS OF THE OCCURRENCE OF CENTRAL PIVOT IRRIGATION SYSTEMS IN AGRICULTURAL REGIONS IN THE STATE OF MATO GROSSO ABSTRACT:The use of irrigation techniques in agricultural production has increased in the last years, and the center-pivot irrigation system has advanced in number of implanted units. This system, due to its characteristics such as labor savings and ease of operation, has proved to be advantageous for the rural producer. The circular shape of this system allows it to be identified by Geoprocessing techniques applied to the quantitative and spatial analysis of orbital or aerial images. In this context, the present work aimed to map, through orbital images, the center-pivot irrigated areas, for a period of 11 years (2004 to 2015) referring to three regions of Mato Grosso State, witch that stand out in agricultural production: Middle North, Parecis and Southeast. The Middle North region presented an average annual growth rate of 10.96% in its irrigated area, the Southeast region 6.75% and the Parecis region 4.28%. The average evolution in the number of pivots in these areas was of the order of 8.07% per year, going from 259 units in 2004, to 586 in 2015, totaling a growth of 226.25% in the period.Keywords: irrigated areas; agricultural production; geoprocessing; remote sensing.

scholarly journals BUILDING DETECTION FROM SAR IMAGES USING UNET DEEP LEARNING METHOD

2020 ◽  
Vol XLIV-4/W3-2020 ◽  
pp. 215-218
Author(s):  
R. A. Emek ◽  
N. Demir
Keyword(s):  
Deep Learning ◽  
Object Detection ◽  
Optical Image ◽  
Building Detection ◽  
Multispectral Images ◽  
Learning Method ◽  
Sar Images ◽  
Optical Images ◽  
Image Patches ◽  
Sentinel 2

Abstract. SAR images are different from the optical images in terms of image properties with the values of scattering instead of reflectance. This makes SAR images difficult to apply the traditional object detection methodologies. In recent years, deep learning models are frequently used in segmentation and object detection purposes. In this study, we have investigated the potential of U-Net models for building detection from SAR and optical image fusion. The datasets used are Sentinel 1 SAR and Sentinel-2 multispectral images, provided from ‘SpaceNet 6 Multi Sensor All-Weather Mapping’ challenge. These images cover an area of 120 km2 in Rotterdam, the Netherlands. As training datasets 20 pieces of 900 by 900 pixel sized HV polarized and optical image patches have been used together. The calculated loss value is 0.4 and the accuracy is 81%.

scholarly journals Center Pivot Irrigation Systems and Where to Find Them: A Deep Learning Approach to Provide Inputs to Hydrologic and Economic Models

2021 ◽  
Vol 3 ◽  
Author(s):  
Daniel Cooley ◽  
Reed M. Maxwell ◽  
Steven M. Smith
Keyword(s):  
Deep Learning ◽  
Coupled System ◽  
Learning Approach ◽  
Technology Choice ◽  
Hydrological Models ◽  
Irrigation Systems ◽  
Technology Center ◽  
Irrigation Technology ◽  
Center Pivot

Availability and quality of administrative data on irrigation technology varies greatly across jurisdictions. Technology choice, however, will influence the parameters of coupled human-hydrological systems. Equally, changing parameters in the coupled system may drive technology adoption. Here we develop and demonstrate a deep learning approach to locate a particularly important irrigation technology—center pivot irrigation systems—throughout the Ogallala Aquifer. The model does not rely on super computers and thus provides a model for an accessible baseline to train and deploy on other geographies. We further demonstrate that accounting for the technology can improve the insights in both economic and hydrological models.

scholarly journals Dealing with Clouds and Seasonal Changes for Center Pivot Irrigation Systems Detection Using Instance Segmentation in Sentinel-2 Time Series

2021 ◽  
pp. 1-1
Author(s):  
Anesmar Olino Albuquerque ◽  
Osmar Luiz Ferreira de Carvalho ◽  
Cristiano Silva ◽  
Argelica Saiaka Luiz ◽  
Pablo Pozzobon De Bem ◽  
...  
Keyword(s):  
Time Series ◽  
Seasonal Changes ◽  
Irrigation Systems ◽  
Center Pivot ◽  
Instance Segmentation ◽  
Sentinel 2

Saliency detection in deep learning era: trends of development

2019 ◽  
pp. 10-36 ◽  
Author(s):  
M. N. Favorskaya ◽  
L. C. Jain
Keyword(s):  
Deep Learning ◽  
Object Detection ◽  
Event Detection ◽  
Visual Analysis ◽  
Saliency Detection ◽  
Salient Object Detection ◽  
Public Image ◽  
Detection Methods ◽  
Salient Object ◽  
Salient Event

Introduction:Saliency detection is a fundamental task of computer vision. Its ultimate aim is to localize the objects of interest that grab human visual attention with respect to the rest of the image. A great variety of saliency models based on different approaches was developed since 1990s. In recent years, the saliency detection has become one of actively studied topic in the theory of Convolutional Neural Network (CNN). Many original decisions using CNNs were proposed for salient object detection and, even, event detection.Purpose:A detailed survey of saliency detection methods in deep learning era allows to understand the current possibilities of CNN approach for visual analysis conducted by the human eyes’ tracking and digital image processing.Results:A survey reflects the recent advances in saliency detection using CNNs. Different models available in literature, such as static and dynamic 2D CNNs for salient object detection and 3D CNNs for salient event detection are discussed in the chronological order. It is worth noting that automatic salient event detection in durable videos became possible using the recently appeared 3D CNN combining with 2D CNN for salient audio detection. Also in this article, we have presented a short description of public image and video datasets with annotated salient objects or events, as well as the often used metrics for the results’ evaluation.Practical relevance:This survey is considered as a contribution in the study of rapidly developed deep learning methods with respect to the saliency detection in the images and videos.

scholarly journals Design of Desktop Audiovisual Entertainment System with Deep Learning and Haptic Sensations

Symmetry ◽  
2020 ◽  
Vol 12 (10) ◽  
pp. 1718
Author(s):  
Chien-Hsing Chou ◽  
Yu-Sheng Su ◽  
Che-Ju Hsu ◽  
Kong-Chang Lee ◽  
Ping-Hsuan Han
Keyword(s):  
Deep Learning ◽  
Object Detection ◽  
User Experience ◽  
Recognition System ◽  
Scene Recognition ◽  
Single Shot ◽  
Auditory Signals ◽  
Hot Weather ◽  
Viewing Experience ◽  
At Home

In this study, we designed a four-dimensional (4D) audiovisual entertainment system called Sense. This system comprises a scene recognition system and hardware modules that provide haptic sensations for users when they watch movies and animations at home. In the scene recognition system, we used Google Cloud Vision to detect common scene elements in a video, such as fire, explosions, wind, and rain, and further determine whether the scene depicts hot weather, rain, or snow. Additionally, for animated videos, we applied deep learning with a single shot multibox detector to detect whether the animated video contained scenes of fire-related objects. The hardware module was designed to provide six types of haptic sensations set as line-symmetry to provide a better user experience. After the system considers the results of object detection via the scene recognition system, the system generates corresponding haptic sensations. The system integrates deep learning, auditory signals, and haptic sensations to provide an enhanced viewing experience.

scholarly journals Uni-Temporal Multispectral Imagery for Burned Area Mapping with Deep Learning

2021 ◽  
Vol 13 (8) ◽  
pp. 1509
Author(s):  
Xikun Hu ◽  
Yifang Ban ◽  
Andrea Nascetti
Keyword(s):  
Deep Learning ◽  
Large Scale ◽  
Multiple Scales ◽  
Detection Methods ◽  
Burned Area ◽  
Landsat 8 ◽  
Multispectral Imagery ◽  
Burned Areas ◽  
Local Climate Zones ◽  
Sentinel 2

Accurate burned area information is needed to assess the impacts of wildfires on people, communities, and natural ecosystems. Various burned area detection methods have been developed using satellite remote sensing measurements with wide coverage and frequent revisits. Our study aims to expound on the capability of deep learning (DL) models for automatically mapping burned areas from uni-temporal multispectral imagery. Specifically, several semantic segmentation network architectures, i.e., U-Net, HRNet, Fast-SCNN, and DeepLabv3+, and machine learning (ML) algorithms were applied to Sentinel-2 imagery and Landsat-8 imagery in three wildfire sites in two different local climate zones. The validation results show that the DL algorithms outperform the ML methods in two of the three cases with the compact burned scars, while ML methods seem to be more suitable for mapping dispersed burn in boreal forests. Using Sentinel-2 images, U-Net and HRNet exhibit comparatively identical performance with higher kappa (around 0.9) in one heterogeneous Mediterranean fire site in Greece; Fast-SCNN performs better than others with kappa over 0.79 in one compact boreal forest fire with various burn severity in Sweden. Furthermore, directly transferring the trained models to corresponding Landsat-8 data, HRNet dominates in the three test sites among DL models and can preserve the high accuracy. The results demonstrated that DL models can make full use of contextual information and capture spatial details in multiple scales from fire-sensitive spectral bands to map burned areas. Using only a post-fire image, the DL methods not only provide automatic, accurate, and bias-free large-scale mapping option with cross-sensor applicability, but also have potential to be used for onboard processing in the next Earth observation satellites.

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