The transition from 30m to 10m resolution in the annual Cropland Data Layers (CDL) is imperative for enhanced national-scale assessments. This paper addresses the computational challenges posed by this transition, emphasizing the need for increased efficiency in data preparation, image processing and improved crop classification accuracy. Leveraging Google Earth Engine (GEE) cloud-based platform and advanced machine learning algorithms, this paper introduces a novel operational workflow to swiftly generate a 10-meter resolution CDL for 2022 across the conterminous United States (CONUS). The workflow includes the development and use of Sentinel-2 and Landsat 8/9 derived multi-sensor gap-filled 10-day image composites, additional ancillary variables, tile-based localized analysis, and stratified random sampling strategy, leading to improved accuracy for major and specialty crops. This workflow not only reduces labor requirements but also enhances decision-making processes for agriculture and policymaking stakeholders.

This paper aims to examine the impact of SMAP derived soil moisture data on the crop yield prediction of the deep learning neural network models. In this study, the crop yield model based on Bayesian neural network (BNN) is used. Corn crop yield prediction experimental results with different type of SMAP Level 4 soil moisture data as inputs are compared to illustrate the impact of the soil moisture data on deep learning crop yield prediction models. The preliminary results show that either rootzone or surface, or rootzone plus surface soil moisture data can improve the BNN model crop yield prediction or produce comparable results though which type of soil moisture data (surface or root zoon) will help improve the prediction accuracy is uncertain. Soil moisture data and precipitation plus NWDI data can produce comparable yield prediction results due to high correlation between soil moisture data and precipitation along with NWDI.

Mapping nationwide in-season crop-type data is a significant and challenging task in agriculture remote sensing. The existing data product for U.S. crop-type planting, such as the Cropland Data Layer (CDL), falls short in facilitating near-real-time applications. This paper designed a workflow aimed at automating the generation of in-season CDL-like products for the U.S. We methodically extracted trusted pixels as land cover labels from historical CDL datasets, employing Sentinel-2, Landsat 8, and Landsat-9 as sources for spectrum data, using the random forest classifier to conduct nationwide crop-type classifications. These classifications were integrated into the In-Season Crop Data Layer (ICDL) covering the entire Conterminous United States (CONUS). This approach facilitated the efficient generation of ICDLs for May, June, and July 2022, achieving satisfactory accuracy in July. Compared to Nebraska and Iowa ground truth data, ICDL achieved F1 scores of (0.911, 0.845) for corn and (0.959, 0.969) for soybean. Furthermore, ICDL’s regional acreage estimates for major crops (corn, soybean, spring wheat, cotton, winter wheat, and rice) closely align with USDA NASS figures, showing minimal variances as low as (0.01%, -0.68%, 0.19%, -4.39%, -5.78%, -1.28%). Notably, ICDL outperforms CDL in most assessments. This research consistently produces annual ICDLs from May to July that are readily accessible to the public in the iCrop system. Simultaneously, it presents an alternative technique for nationwide, in-season mapping of crop types.

The field level soil moisture information is critical for crop irrigation, water resource and crop management, crop modeling, and crop yield estimation. USDA National Agricultural Statistics Services (NASS) uses SMAP derived soil moisture data for cropland soil moisture condition monitoring and assessment. However, spatial resolutions of Crop-CASMA soil moisture data are too coarse for field level assessment. Therefore, this paper proposes to use machine learning based high-resolution soil moisture (ML-HRSM) data for field level drought and irrigation monitoring. The preliminary study results show that the ML-HRSM data can accurately delineate field-level soil moisture variations at high spatial (30-m) and temporal (daily) resolutions. It can accurately capture field level soil moisture changes caused by the irrigation activities and vegetation ET processes. The irrigation activities are indirectly confirmed by weekly MODIS NDVI maps. The preliminary results indicate that the surface ML-HRSM is capable for irrigation activity and drought monitoring at field level.

Recently, with the advent of satellite missions and artificial intelligence techniques, supervised machine learning (ML) methods have been more and more used for analyzing remote sensing (RS) observation data for crop yield prediction. However, due to the domain shift between heterogeneous regions, supervised ML models tend to have poor spatial transferability. As a result, models trained with labeled data from one spatial region (i.e., source domain) often lose their validity when directly applied to another region (i.e., target domain). To address this issue, we proposed a multisource maximum predictor discrepancy (MMPD) neural network that is an unsupervised domain adaptation (UDA) approach for corn yield prediction at the county level. The novelties of this study include that: 1) we proposed to maximize the discrepancy between two source-specific yield predictors and align source and target domains by considering crop yield response in the target domain and 2) we adopted the strategy of multisource UDA to avoid negative interference between labeled samples from different sources. Case studies in the U.S. corn belt and Argentina demonstrated that the proposed MMPD model had effectively reduced domain shifts and outperformed several other state-of-the-art deep learning (DL) and UDA methods.

Timely, frequent, and complete cropland soil moisture information acquired throughout the growing season is critical for agricultural policy, production, food security, and food prices. The NASA Soil Moisture Active and Passive (SMAP) mission provides a reliable data source for cropland soil moisture assessment. This paper presents Crop-CASMA - a web GIS application tool for cropland soil moisture monitoring and assessment based on SMAP data. This interactive Web service-based GIS application tool enables CONUS SMAP derived soil moisture data visualization, dissemination, and analytics. In this paper, we describe the Crop-CASMA application system architecture, the application implementation, and the data it serves. In addition, we also present a few snapshots of the Crop-CASMA data for cropland soil moisture monitoring. The release of Crop-CASMA greatly enhances the user experience and facilitates using soil moisture data products for crop condition monitoring and decision support.

The Cropland Data Layer (CDL) is currently the only subfield level high resolution crop-specific land cover data product over the entire conterminous United States (CONUS). It has been widely used in agricultural industry, business decision support, research, and education worldwide. However, CDL data has its limitations. It is an end-of-season land cover map which is not available within growing season. Moreover, CDLs in early years have many misclassified pixels (rel-atively low accuracy) due to cloud cover and lack of satellite images. This paper will present the studies of using machine learning technique to address these issues in CDL data. Specifically, we will present the design and implementation of a machine learning model for agro-geoinformation discovery from CDL. Several application scenarios of the proposed model, including prediction of crop cover, crop acreage estimation’ in-season crop mapping, and refinement of the early-year CDL data, are demonstrated and discussed.

This study presents a variational approach for optimized estimation of specific differential phase ( KDP) for polarimetric radars using a linear forward operator. A cubic B-spline interpolating filter is included to mitigate the impact of measurement error in the total differential phase and ensure the spatial continuity of KDP. For rain, non-negative constraints are introduced to ensure that the KDPestimates are within the physical bounds. The variational approach is flexible to incorporate the background information constructed from the measurements of horizontal reflectivity factor ( ZH) and differential reflectivity ( ZDR) based on the self-consistent relationship of polarimetric variables. The variational approach is evaluated using simulated experiments, as well as real observations from an S-band operational weather radar. Without including background information, the variational approach has slightly better performance compared to the approach based on linear programming (LP), and the background information helps to further improve the performance. In addition, the linear forward operator makes this variational approach computationally efficient. It needs less than 3% computational power required by the approach based on LP, making it more suitable for real-time operational applications.

Early season crop identification is important for food security and economic stability. The USDA NASS uses optical data to provide acreage estimates, each June, to the NASS Agricultural Statistics Board. However, early season crop identification is difficult using optical data alone, because imagery is frequently cloudy during the spring. The purpose of this study is to determine whether using SAR and SAR texture can improve early season winter wheat identification compared to optical data alone. Study areas in the Missouri "Bootheel" (2017 growing season) and Northwest Texas (2018 growing season), United States (U.S.) are selected. The SAR data used in this study are Sentinel-1. Optical data include: Landsat 8, Disaster Monitoring Constellation, and Sentinel-2. Study results show that optical data with SAR achieved the highest winter wheat accuracies, 7.7% higher than optical data alone, in Missouri. Optical with SAR and SAR texture resulted in improved accuracies over optical alone, but only marginally, in Texas. These results indicate that optical and SAR, used together, can potentially improve early season crop identification.

The accuracy of a supervised classification is highly dependent upon the training samples. This paper is concerned with the impact of non-proportional training data sampling of imbalanced classes on the land cover classification accuracy, using the See5 decision tree classifier. The purpose of this paper is 1) to examine experimentally how the training sampling ratio affects classification accuracy in the imbalanced class scenario; and 2) to determine the best training data sampling ratio for optimal classification performance using a See5 decision tree classifier. To better measure classification accuracy, we propose a balanced accuracy measure of a targeted class, which incorporates both False Positive and False Negative errors to truthfully reflect the accuracy of a targeted class. The study result indicates that balancing the training sample between classes does not necessarily improve the classification accuracy. Instead, selecting a training sample ratio which equals the actual ratio of the coverages of the imbalanced classes will yield the best classification performance.

Optical based remote sensing plays an important role in citrus crop change monitoring in Florida, United States (U.S). However, persistent cloud cover during the summer growing season in Florida often limits the application of optical sensors. Synthetic Aperture Radar (SAR) has the advantage over optical data by operating at wavelengths not impeded by cloud cover, rain or a lack of illumination. The objective of this study is to assess the effectiveness of using Sentinel-1A C-band SAR data for classifying citrus in Florida. Twelve individual citrus classifications produced using single date optical or SAR data, as well as multi-date optical and SAR data fusion, are designed and tested. It is found that the classification accuracies of Sentinel-l C-band SAR data are slightly lower than those of multi-temporal cloud free optical data (approximately 2.5% difference). However, the relatively comparable classification accuracy results indicate that the Sentinel-1 SAR is a useful alternative imagery source particularly in regions with persistent cloud cover.

Agricultural flood monitoring is important for food security and economic stability. Synthetic Aperture Radar (SAR) has the advantage over optical data by operating at wavelengths not impeded by cloud cover or a lack of illumination. This characteristic makes SAR a potential alternative to optical sensors for agricultural flood monitoring during disasters. The purpose of this study is to assess the effectiveness of using freely available Copernicus Sentinel-1 SAR data for operational agricultural flood monitoring in the United States (U.S.). The operational detection of flood inundation was tested during Hurricane Harvey in 2017, which resulted in significant flooding over Texas and Louisiana, U.S. This paper presents 1) the agricultural flood monitoring method that utilizes Sentinel-1 SAR, the NASS 2016 Cultivated Layer, and the NASS 2016 and 2017 Cropland Data Layers; 2) flood detection validation results and 3) inundated cropland and pasture acreage estimates. The study shows that Sentinel-1 SAR is an effective and valuable data source for operational disaster assessment of agriculture.

Area Sampling Frames are used for surveys including crop acreage and yield, forests, and natural resource inventories and are the foundation of the statistical program of the USDA National Agricultural Statistics Service (NASS) and many statistical survey programs around the world. An automated area frame stratification method was recently implemented into NASS operations, which is based on the objective calculation of percent cultivation derived from the NASS geospatial Cropland Data Layers (CDLs). While autostratification consistently outperforms manual stratification in cultivated areas, we found that CDL-based pixel counting estimation consistently underestimated crop acreage. Previous research indicates that CDL classification accuracy is affected by training data pixel level buffering. We hypothesize that training data pixel level buffering will also affect the CDL based auto-stratification results and crop acreage estimation. This paper evaluates the impact of training data buffering on area frame stratification results and crop estimates. Preliminary results indicate that the crop acreage underestimation can be directly attributed to the training data pixel level buffering procedure.

Timely, frequent, and complete cropland soil moisture information, acquired throughout the growing season is critical for agricultural policy, production, food security, and food prices. The NASA Soil Moisture Active and Passive (SMAP) mission provides a reliable data source for cropland soil moisture assessment. This paper presents a case study of using SMAP soil moisture data products for agriculture land soil moisture assessment. A prototype application of interactive Web service based SMAP soil moisture data visualization, dissemination and analytics based on VegScape is used. In the study, we propose to temporally aggregate SMAP data for a better crop soil moisture assessment. The case study assesses Iowa's soil moisture status using SMAP data and compares it with the NOAA precipitation record and NASS published soil moisture survey results for a late September period in Iowa. The high correlation between the SMAP and NOAA observations is found. Moreover, we find that the SMAP results are generally consistent with NASS published survey results. The preliminary results of the study indicate the SMAP data have great potential for agricultural soil moisture assessment applications.

Area Sampling Frames (ASFs) are the foundation of the agricultural statistics program of USDA National Agricultural Statistics Service (NASS). A geospatial Cropland Data Layer (CDL) based automated stratification (AS) method was recently implemented to achieve higher accuracies than traditional stratification (TS), based on visual interpretation, in cultivated areas. This paper extends the AS assessment to the post stratification estimates. South Dakota (SD) US 2013 post stratification estimates, based on AS, are compared with the SD 2013 June Agricultural Survey estimates based on TS. Post stratification estimates obtained using AS are comparable, to the TS estimates, based on estimate percent differences. Considering the significant improvement in accuracy using AS in cultivated strata in five test states, improved accuracy in the highly cultivated stratum and improved stratum homogeneity in this study, it is concluded that the CDL based AS method generates ASFs that are more objective, efficient, accurate, and homogeneous and reduces labor costs.

Timely, frequent, crop vegetation condition information, with complete geospatial coverage acquired throughout the growing season is critical for public and private sector decision making that concerns agricultural policy, production, food security, and food prices. The NASA Soil Moisture Active and Passive (SMAP) mission provides such a reliable data source for cropland soil moisture assessment. This paper presents a prototype of an interactive Web service based SMAP soil moisture visualization, dissemination and analytics system for US soil moisture monitoring based on the VegScape framework. This system automatically retrieves and preprocesses SMAP soil moisture data for US cropland soil moisture condition monitoring and assessment. The prototype takes advantage of the VegScape's service oriented architecture and adds a new component for SMAP soil moisture. It reuses existing VegScape visualization, dissemination and analytical functionalities and tools. The prototype inherits the capabilities of interactive map operations, data dissemination, statistical tabulating and charting, comparison analysis, and various Web services.

Remotely sensed soil moisture data can provide timely, objective and quantitative crop soil moisture information with broad geospatial coverage and sufficiently high resolution observations collected throughout the growing season. This paper evaluates the feasibility of using the assimilated ESA Soil Moisture Ocean Salinity (SMOS) Mission L-band passive microwave data for operational US cropland soil surface moisture monitoring. The assimilated SMOS soil moisture data are first categorized to match with the United States Department of Agriculture (USDA) National Agricultural Statistics Service (NASS) survey-based weekly soil moisture observation data, which are ordinal. The categorized assimilated SMOS soil moisture data are compared with NASS's survey-based weekly soil moisture data for consistency and robustness using visual assessment and rank correlation. Preliminary results indicate that the assimilated SMOS soil moisture data highly co-vary with NASS field observations across a large geographic area. Therefore, SMOS data have great potential for US operational cropland soil moisture monitoring.

This paper proposes a novel method for land cover area frame stratification based on corn planting frequency and percent cultivation. South Dakota U.S. geospatial crop frequency (2008-2013) and cultivation (2013) data layers created from NASS Cropland Data Layers are utilized to develop a novel area sampling frame (ASF) stratification design. Eight corn planting frequency strata are derived using a k-means clustering method based on mean corn planting frequency calculated at the NASS ASF primary sampling unit level. The corn planting frequency strata are then sub stratified based on percent cultivation, which, together, provide more crop specific information than the current NASS ASF based on percent cultivation alone. Using 2014 Farm Service Agency Common Land Unit Data as in situ validation, it is found that this novel ASF design predicts crop specific planting patterns well. These results indicate that the new stratification method has potential to improve ASF accuracy, efficiency and crop estimates.

Crop growth stages are important factors for segmenting the crop growing seasons and analyzing their growth conditions against normal conditions by periods. Time series of high temporal resolution, up to daily, satellite remotely sensed data are used in establishing crop growth estimation model and estimate the growth stages. The daily surface reflectance data from Moderate Resolution Imaging Spectroradiometer (MODIS) is used as the base data to calculate indices, form condition profiles, construct crop growth model, and estimate crop growth stage. Different crops have different condition profiles. To take into consideration of crop differences, models are built on each crop type. In the United States, ten major crops have been chosen to build crop growth stage estimation models using historical date tracing back to 2000 when MODIS launched. A kernel, double sigmoid model, is used to model the single mode crop growth season. The basic core model is double sigmoid model. The Best Index Slope Extraction (BISE) is applied to pre-filter the daily crop condition index. Estimated results have reasonably high accuracy, with root mean square error less than 10% on the state level evaluation.

A new automatic stratification method utilizing USDA National Agricultural Statistics Service (NASS) geospatial Cropland Data Layers (CDLs) was recently implemented in NASS operations. Recent research findings indicated that using the CDL stratification method rather than visual interpretation of satellite imagery and aerial photography (traditional method) to define percent cultivation of land areas resulted in Area Sampling Frames (ASF) constructed with improved accuracy, objectivity and efficiency at reduced cost [3]. This paper describes an operational ASF construction process that integrates the automated CDL stratification results with traditional editing/review procedures, a hybrid approach. New 2013/2014 ASFs for South Dakota and Oklahoma were successfully built using the new operational process and illustrated significant improvements in frame accuracy, operational efficiency, and cost.

The National Agricultural Statistics Service (NASS) Area Sampling Frames (ASFs) are based on the stratification of US land cover by percent cultivation. Recently, an automated stratification method based on the NASS Cropland Data Layer (CDL) was developed to efficiently and objectively stratify US land cover. This method achieved higher accuracies in all cultivated strata with statistical significance at a 95% confidence level. This paper proposed to develop crop specific covariate data based on 2007 - 2010 CDLs. Crop (corn, soybeans, wheat and cotton) and non crop (forest, urban and water) covariate data were derived and validated for six states. Producer and user accuracies for the covariate data sets were based on independent 2011 Farm Service Agency Common Land Unit data and 2011 CDLs. Non crop covariate data were validated using the National Land Cover Data 2006. Covariate data were used within NASS to conduct substratification of the 2013 Oklahoma ASF.

This paper investigates at the pre-launch stage the feasibility of using NASA SMAP mission results for US national operational crop soil moisture monitoring. The purpose of using remote sensed SMAP data for crop soil moisture monitoring is to eliminate data collection subjectivity, reduce cost, increase cropland soil moisture monitoring data consistency, and operational efficiency. In this paper, the SMAP simulated data product time series, such as L2_SM_A, L3_SM_A/P, L4_SM, L1C-S0_HiRes are first evaluated for their suitability for NASS operational cropland soil moisture monitoring by comparing SMAP results with the NASS' survey based weekly soil moisture observation data for their consistency and robustness. The preliminary results illustrate that SMAP products have the potential for NASS operational use at least for county level soil moisture statistics. This paper also explores a technical route to build a Web-service based interactive soil moisture monitoring system for map visualization, dissemination, and analysis based on SMAP results.

Timely, frequent, high resolution, fully geospatial covered crop vegetation condition information throughout the season is critical to decision making in both public and private sectors that concern agricultural policy, production, food security, and food prices. This paper presents a new interactive Web service-based vegetation condition monitoring system - VegScape. This system automatically obtains and preprocesses near real-time 250m MODIS daily surface reflectance data for better spatial and temporal resolutions, and generates geospatially various vegetation condition indices for timely crop condition. The VegScape not only offers the online interactive map operations, data dissemination, crop condition statistics, charting and graphing, and comparison analysis, but also provides Web services such on-demand vegetation condition maps and statistics for uses in other applications. This system delivers dynamic user experiences and geospatial crop condition information for decision support with its comprehensive capabilities through standard geospatial Web services in a publicly accessible online environment.

This paper describes the method used to derive 30 meter resolution 2011 US cultivated data sets based on multi-year National Agricultural Statistics Service (NASS) Cropland Data Layer (CDL) data. This paper presents different sets of rules (models) to build the cultivated data sets, and a comparison of the resulting cultivated data set accuracies to the accuracies of the original CDL input data. Nine models to create 2011 cultivated data sets for nine US states are tested. Each model provides a set of rules for merging pixels of multi-year (2007-2011) CDL data. The cultivated data accuracy was assessed against in situ 2011 Farm Service Agency (FSA) Common Land Unit (CLU) data. It was found that accuracies were close among the cultivated data generated using the different models. The strongest models for all states achieved overall (producer and user) accuracies greater than 94% for cultivated and non cultivated categories.

NDVI maps have been proven valuable in providing a spatially complete view of crop's vegetation condition, which manifests disastrous events such as massive flood and drought. It is virtually impossible to obtain from ground survey data. This paper uses NASA MODIS 250m resolution, daily surface reflectance data for crop condition monitoring. The NDVI provides an absolute metrics for vegetation condition. However, a relative measurement of the current vegetation condition against a reference vegetation condition is critical for understanding, interpreting and quantifying the current vegetation condition. In this paper, a new NDVI based vegetation condition index is presented to measure the vegetation condition with respect to the "normal condition", which is characterized by historical average. The proposed new vegetation condition index is empirically compared with several other vegetation indices to evaluate its effectiveness. Its advantages and utility for crop vegetation condition measurement are evidenced by the preliminary results.

The continuous crop condition monitoring with a full geospatial coverage and sufficient granularity throughout the season is critical to decision making in agricultural policy, production, and food prices. The USDA NASS currently uses bi-weekly 1km AVHRR NDVI composited data to monitor the US crop condition in the growing season. To improve both temporal and spatial resolution, 250m daily MODIS surface reflectance data is used and invalid, cloud pixels are reconstructed. Moreover, to compensate the sensitivity to low vegetation area and saturation at the high vegetation of NDVI, this paper proposes a new vegetation index based on NDVI and simple ratio vegetation index as an alternative for crop vegetation condition monitoring. The initial experiments indicate the proposed vegetation index is robust to the low vegetation and sensitive to high vegetation, and has potential to be an alternative to NDVI for crop condition monitoring.

Crop development progress information is critical to decision makings in National Agriculture Statistics Services (NASS) of United State Department of Agriculture (USDA), other federal agencies, and public users. NASS currently compiles and issues the weekly national crop progress report based on crop conditions reported by state and county agricultural officials. The report is point-wise sampled, subjectively estimated by field enumerators and lacks spatially distribution information. The applications of remote sensing technologies have been limited to a low-resolution static image posted on the web weekly during the growing season, without enough quantitative information for NASS customers to fully support their decision makings. This study designs a service-oriented architecture by leveraging open geospatial standards and specifications, especially those on Sensor Web technology. The new monitoring system aims at overcoming the shortcomings of existing crop progress systems/procedures by utilizing NASA satellite-based vegetation index, leaf area index, land surface temperature, precipitation, and soil moisture, and model-based longterm weather forecast and by integrating these products with USDA's crop area and other data. Advanced workflow for composing Web services was used in automatically producing and disseminating an objective and quantitative national crop progress map and associated decision support data at high-spatial resolution, as well as summary reports. Standard-compliance enables the reusability and multiple-purpose of functionalities provided by individual geospatial Web services and their composed workflows.

A framework for deriving a class of new global affine invariants for both object matching and positioning based on a novel concept of cross-weighted moments with fractional weights is presented. The fractional weight factor allows for a more flexible range to balance between the capability to discriminate between objects that differ only in small shape details and the sensitivity of small shape details to the presence of the noise. Moreover, it makes it possible to arrive at low order (zero order) affine invariants that are more robust than those derived from higher order regular moments. The affine transformation parameters are recovered from the zero and the first order cross-weighted moments without requiring any feature point correspondence information. The equations used to find the affine transformation parameters are linear algebraic. The sensitivity of the cross-weighted moment invariants to noise, missing data, and perspective effects is shown on real images.

This paper is concerned with the problem of feature point registration and scene recognition from images under weak perspective transformations which are well approximated by affine transformations and under possible occlusion and/or appearance of new objects. It presents a set of local absolute affine invariants derived from the convex hull of scattered feature points (e.g., fiducial or marking points, corner points, inflection points, etc.) extracted from the image. The affine invariants are constructed from the areas of the triangles formed by connecting three vertices among a set of four consecutive vertices (quadruplets) of the convex hull, and hence do make direct use of the area invariance property associated with the affine transformation. Because they are locally constructed, they are very well suited to handle the occlusion and/or appearance of new objects. These invariants are used to establish the correspondences between the convex hull vertices of a test image with a reference image in order to undo the affine transformation between them. A point matching approach for recognition follows this. The time complexity for registering L feature points on the test image with N feature points of the reference image is of order O(N x L). The method has been tested on real indoor and outdoor images and performs well.

There have been many techniques for curve shape representation and analysis, ranging from Fourier descriptors, to moments, to implicit polynomials, to differential geometry features, to time series models, to B-splines, etc. The B-splines stand as one of the most efficient curve (surface) representations and possess very attractive properties such as spatial uniqueness, boundedness and continuity, local shape controllability, and invariance to affine transformations. These properties made them very attractive for curve representation, and consequently, they have been extensively used in computer-aided design and computer graphics. Very little work, however, has been devoted to them for recognition purposes. One possible reason might be due to the fact that the B-spline curve is not uniquely described by a single set of parameters (control points), which made the curve matching (recognition) process difficult when comparing the respective parameters of the curves to be matched. This paper is an attempt to find matching solutions despite this limitation, and as such, it deals the problem of using B-splines for shape recognition and identification from curves, with an emphasis on the following applications: affine invariant matching and classification of 2-D curves with applications in identification of aircraft types based on image silhouettes and writer-identification based on handwritten text.

The B-spline stands as one of the most efficient curve (surface) representation, and possesses very attractive properties such as spatial uniqueness, boundedness and continuity, local shape controllability, and invariance to affine transformations. These properties made them very attractive for curve representation in computer aided design and computer graphics. Very little work, however, has been devoted to them for recognition purpose. One possible reason might be due to the fact that the B-spline curve is not uniquely described by a single set of control points, which make the curve matching (recognition) process not a simple comparison between the respective parameters of the curves to be matched. The paper is an attempt to find matching solutions despite this limitation and addresses the problems of invariant matching and classification of 2D closed curves with application in identification of aircraft types based on image silhouettes, and writer-identification based on hand written text.>