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[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - An Hybrid Recurrent Convolutional Neural Network for Crop Type Recognition Based on Multitemporal Sar Image Sequences
摘要： Agriculture monitoring is a key task for producers, governments and decision makers. The analysis of multitemporal remote sensing data provides a cost-effective way to perform this task. Recurrent Neural Networks (RNNs) have been successfully used in temporal modeling problems, while Convolutional Neural Networks (CNNS) are the state-of-the-art in image classification, mainly due to their ability to capture spatial context. In this work, we propose the use of a hybrid network architecture for crop mapping that combines RNNs and CNNs. We evaluate this architecture experimentally upon a Sentinel-1A database from a tropical region in Brazil. The ability of recurrent networks to model temporal context is compared with the conventional image stacking approach. The impact of using CNN learned features rather than context aware handcrafted features is also investigated. In our analysis the hybrid architecture achieved better average class accuracy than alternative approaches based on image stacking and GLCM features.
关键词： Crop Recognition,Convolutional Neural Networks,Recurrent Neural Networks
更新于2025-09-10 09:29:36
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An optimized two-stage spatial sampling scheme for winter wheat acreage estimation using remotely sensed imagery
摘要： Timely and reliable information on crop acreage is essential for formulating grain production policies and ensuring national food security. The combination of available satellite-based remotely sensed images and traditional sampling methods offers the possibility of improved crop acreage estimation at a regional scale. Due to the administrative convenience, reduced survey cost and workload, two-stage sampling has widely been used for crop acreage survey at the large-scale regions. However, compared with single-stage sampling, the two-stage sampling can introduce larger estimation errors, since it has multiple sampling stages. This study’s aim is to optimize the two-stage sampling scheme using satellite-based remotely sensed imagery to improve the accuracy of crop acreage estimation. Taking Mengcheng County, Anhui Province, China, as the study area, this study explored the influence of stratum boundary and sample selection method on the sampling efficiency at the first sampling stage, analysed the impact of sample size on population extrapolation accuracy and then optimized the sample size of the second sampling stage using crop thematic map retrieved by ALOS (Advanced Land Observing Satellite) AVNIR (Advanced Visible light and Near Infrared Radiometer)-2 images in 2009. The results showed that the relative error (RE), coefficient of variation (CV), standard error (SE) of population extrapolation, and sampling fraction (f) using the cumulative square root of frequency (CSRF) method is the minimum among three methods for the stratum boundary determination at the first sampling stage, followed by the equal interval (EI) and equal sample size (ESS) method. Moreover, the RE, CV, and SE of population extrapolation using the ST sampling method is the minimum, compared with simple random (SI) and systematic (SY) sampling method. Therefore, the sampling scheme of the first stage can be optimized by CSRF method for stratum boundary determination and stratified sampling (ST) sampling method for samples selection. At the second sampling stage, RE and CV values of population extrapolation decrease as the sample size increases. Comprehensively considering the accuracy, stability of population extrapolation and sampling cost, the most cost-effective sample size for estimating the winter wheat acreage of the study area is 4. From the perspective of the reasonable selection of sample selection methods, sample size and determination of stratum boundaries, this study provides an important basis for formulating a cost-effective two-stage spatial sampling scheme for crop acreage estimation.
关键词： sampling scheme optimization,remotely sensed imagery,winter wheat,crop acreage estimation,two-stage sampling
更新于2025-09-10 09:29:36
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Land Applications of Radar Remote Sensing || Combining Moderate-Resolution Time-Series RS Data from SAR and Optical Sources for Rice Crop Characterisation: Examples from Bangladesh
摘要： Operational monitoring of agricultural extent and production is part of several national and international efforts to provide transparent, rapid and accurate information related to food security and food markets. Initiatives such as the G20 Agricultural Market Information System (AMIS), the European Commission’s Monitoring Agricultural Resources mission (MARS), the Global Agricultural Monitoring (GEOGLAM) component of GEO, and the United States Department of Agriculture’s Global Agricultural Monitoring Foreign Agricultural Service (GLAMFAS), are just some examples of operational services that do, or will require remote-sensing–based information on crop status in almost any part of the world. Of the thousands of edible plants, just three—rice, wheat, and maize —provide 60% of the global population’s food energy intake, and the top 15 crops amount to 90% [1]. Seasonal or monthly estimates of production and availability of these staples form a part of many agricultural bulletins and agricultural outlook reports. These reports are used for decision-making and policies on imports, exports, subsidies, and investments, which, in turn, affect prices. Food security is fundamentally about availability and price; such reports, and the responses to these reports, affect both. International events, such as the food price crisis of 2008, are the unintended outcome of national and international policy decisions that can detrimentally affect millions of people. More accurate and timely information to better inform policymakers is one way to reduce the likelihood of similar events in the future.
关键词： SAR,remote sensing,Bangladesh,optical data,ENVISAT ASAR,rice crop,MODIS
更新于2025-09-10 09:29:36
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[Lecture Notes in Electrical Engineering] Microelectronics, Electromagnetics and Telecommunications Volume 521 (Proceedings of the Fourth ICMEET 2018) || Estimation of Water Contents from Vegetation Using Hyperspectral Indices
摘要： This paper outlines the research objectives to investigate the approaches for assessment of vegetation water contents using hyperspectral remote sensing and moisture sensor. Water contents of crops monitor crop health for precision farming and monitoring. In the present research, spectral indices with some chemical extraction procedures were identi?ed for estimation of water contents of crops. The investigated crop species, namely Vigna Radiata, Vigna Mungo, Pearl Millet, and Sorghum were collected from Aurangabad region of Maharashtra, India. Spectral re?ectance curve of crop growth patterns was measured using ASD ?eld Spec 4 Spectroradiometer and 150 Soil moisture sensor including healthy, diseased, and dry leaves with standard laboratory environment. It is found that there was a positive correlation between WI and Soil moisture sensor with 0.99, 0.76, and 0.97 accuracy.
关键词： Coef?cient of correlation,Crop analysis,Spectral indices,Spectral re?ectance
更新于2025-09-10 09:29:36
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Automated leaf disease detection in different crop species through image features analysis and One Class Classifiers
摘要： The presented approach demonstrates an automated way of crop disease identification on various leaf sample images corresponding to different crop species employing Local Binary Patterns (LBPs) for feature extraction and One Class Classification for classification. The proposed methodology uses a dedicated One Class Classifier for each plant health condition including, healthy, downy mildew, powdery mildew and black rot. The algorithms trained on vine leaves have been tested in a variety of crops achieving a very high generalization behavior when tested in other crops. An original algorithm proposing conflict resolution between One Class Classifiers provides the correct identification when ambivalent data examples possibly belong to one or more conditions. A total success rate of 95% is achieved for the total for the 46 plant-condition combinations tested.
关键词： Computer vision,Machine learning,Local descriptors,Crop health status,Precision agriculture
更新于2025-09-09 09:28:46
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Leaf Area Estimation of Reconstructed Maize Plants Using a Time-of-Flight Camera Based on Different Scan Directions
摘要： The leaf area is an important plant parameter for plant status and crop yield. In this paper, a low-cost time-of-flight camera, the Kinect v2, was mounted on a robotic platform to acquire 3-D data of maize plants in a greenhouse. The robotic platform drove through the maize rows and acquired 3-D images that were later registered and stitched. Three different maize row reconstruction approaches were compared: reconstruct a crop row by merging point clouds generated from both sides of the row in both directions, merging point clouds scanned just from one side, and merging point clouds scanned from opposite directions of the row. The resulted point cloud was subsampled and rasterized, the normals were computed and re-oriented with a Fast Marching algorithm. The Poisson surface reconstruction was applied to the point cloud, and new vertices and faces generated by the algorithm were removed. The results showed that the approach of aligning and merging four point clouds per row and two point clouds scanned from the same side generated very similar average mean absolute percentage error of 8.8% and 7.8%, respectively. The worst error resulted from the two point clouds scanned from both sides in opposite directions with 32.3%.
关键词： crop characterization,precision farming,3-D sensors,agricultural robotics,plant phenotyping
更新于2025-09-09 09:28:46
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A Novel Automatic Method for Alfalfa Mapping Using Time Series of Landsat-8 OLI Data
摘要： Remote sensing (RS) data have been utilized increasingly for mapping various crops at local and regional scales using various techniques. However, training data collection of these methods is costly and time consuming. On the other hand, time series of RS data have provided valuable information about crop phenological patterns, which can be utilized for automatic crop mapping independent of training data. Hence, the aim of this research is to develop a new automatic method to map alfalfa by identification of specific characteristics of alfalfa based on time series of Landsat 8 OLI images in four study sites in Iran and the United States. Alfalfa fields are usually harvested periodically and two neighboring farms may not be harvested simultaneously. To address this challenge, the alfalfa spectral reflectance values in various bands were compared with those of other crops during the growing season. In the following, three assumptions were made to find suitable relationships for demonstrating alfalfa characteristics as well as separating it from other crops. The results indicated that the summation of differences between the red and NIR reflectance values of alfalfa in the time series of Landsat images is significant; and also, the average values of the NIR and red bands during the growing season are remarkably higher and lower than those of other crops, respectively. Hence, based on these findings, a new specific feature was developed to detect alfalfa with the overall accuracy of 93%, 90%, 94%, and 90% in Moghan, Qazvin, Razan, and Parker Valley, respectively.
关键词： phenology,Landsat time series,spectral feature,Alfalfa,automatic crop mapping
更新于2025-09-09 09:28:46
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Sentinel-2 Based Temporal Detection of Agricultural Land Use Anomalies in Support of Common Agricultural Policy Monitoring
摘要： The European Common Agricultural Policy (CAP) post-2020 timeframe reform will reshape the agriculture land use control procedures from a selected risk fields-based approach into an all-inclusive one. The reform fosters the use of Sentinel data with the objective of enabling greater transparency and comparability of CAP results in different Member States. In this paper, we investigate the analysis of a time series approach using Sentinel-2 images and the suitability of the BFAST (Breaks for Additive Season and Trend) Monitor method to detect changes that correspond to land use anomaly observations in the assessment of agricultural parcel management activities. We focus on identifying certain signs of ineligible (inconsistent) use in permanent meadows and crop fields in one growing season, and in particular those that can be associated with time-defined greenness (vegetation vigor). Depending on the requirements of the BFAST Monitor method and currently time-limited Sentinel-2 dataset for the reliable anomaly study, we introduce customized procedures to support and verify the BFAST Monitor anomaly detection results using the analysis of NDVI (Normalized Difference Vegetation Index) object-based temporal profiles and time-series standard deviation output, where geographical objects of interest are parcels of particular land use. The validation of land use candidate anomalies in view of land use ineligibilities was performed with the information on declared land annual use and field controls, as obtained in the framework of subsidy granting in Slovenia. The results confirm that the proposed combined approach proves efficient to deal with short time series and yields high accuracy rates in monitoring agricultural parcel greenness. As such it can already be introduced to help the process of agricultural land use control within certain CAP activities in the preparation and adaptation phase.
关键词： permanent meadows,change detection,crop monitoring,arable fields,NDVI object-based temporal profiles,GEOBIA,time series analysis
更新于2025-09-09 09:28:46
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Using Hyperspectral Data to Identify Crops in a Cultivated Agricultural Landscape - A Case Study of Taita Hills, Kenya
摘要： Recent advances in hyperspectral remote sensing techniques and technologies allow us to more accurately identify larger range of crop species from airborne measurements. This study employs hyperspectral AISA Eagle VNIR imagery acquired with 9 nm spectral and 0.6 m spatial resolutions over a spectral range of 400 nm to 1000 nm. The area of study is the Taita hills in Kenya. Various crops are grown in this region basically for food and as an economic activity. The crops addressed are: maize, bananas, avocados, and sugarcane and mango trees. The main objectives of this study were to study what crop species can be distinguished from the cultivated population crops in the agricultural landscape and what feature space discriminates most effectively the spectral signatures of different species. Spectral Angle Mapper (SAM) algorithm together with some dissimilarity concepts was applied in this work. The spectral signatures for crops were collected using accurate field plot maps. Accuracy assessment was done using independent training vector data. We achieved an overall accuracy of 77% with a kappa value of 0.67. Various crops in different locations were identified and shown.
关键词： Spectral angle mapper,Hyperspectral imaging,Spectral signatures,Spectral variation,Crop identification
更新于2025-09-09 09:28:46
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Separating Crop Species in Northeastern Ontario Using Hyperspectral Data
摘要： The purpose of this study was to examine the capability of hyperspectral narrow wavebands within the 400–900 nm range for distinguishing five cash crops commonly grown in Northeastern Ontario, Canada. Data were collected from ten different fields in the West Nipissing agricultural zone (46°24'N lat., 80°07'W long.) and included two of each of the following crop types; soybean (Glycine max), canola (Brassica napus L.), wheat (Triticum spp.), oat (Avena sativa), and barley (Hordeum vulgare). Stepwise discriminant analysis was used to assess the spectral separability of the various crop types under two scenarios; Scenario 1 involved testing separability of crops based on number of days after planting and Scenario 2 involved testing crop separability at specific dates across the growing season. The results indicate that select hyperspectral bands in the visual and near infrared (NIR) regions (400–900 nm) can be used to effectively distinguish the five crop species under investigation. These bands, which were used in a variety of combinations include B465, B485, B495, B515, B525, B535, B545, B625, B645, B665, B675, B695, B705, B715, B725, B735, B745, B755, B765, B815, B825, B885, and B895. In addition, although species classification could be achieved at any point during the growing season, the optimal time for satellite image acquisition was determined to be in late July or approximately 75–79 days after planting with the optimal wavebands located in the red-edge, green, and NIR regions of the spectrum.
关键词： soybean,wheat,barley,canola,oat,crop separability,hyperspectral remote sensing,optimal timing,precision agriculture
更新于2025-09-09 09:28:46