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1.
The iterative and convergent nature of ensemble learning algorithms provides potential for improving classification of complex landscapes. This study performs land-cover classification in a heterogeneous Massachusetts landscape by comparing three ensemble learning techniques (bagging, boosting, and random forests) and a non-ensemble learning algorithm (classification trees) using multiple criteria related to algorithm and training data characteristics. The ensemble learning algorithms had comparably high accuracy (Kappa range: 0.76-0.78), which was 11% higher than that of classification trees. Ensemble learning techniques were not influenced by calibration data variability, were robust to one-fifth calibration data noise, and insensitive to a 50% reduction in calibration data size.  相似文献   

2.
估算森林地上生物量(AGB)对于全球实现碳中和目标至关重要。本文以美国缅因州Howland森林为研究区域,借助地面实测样地数据,对比分析协同不同数据源(高光谱和LiDAR)和机器学习算法(随机森林、支持向量机、梯度提升决策树和K最邻近回归)的研究,以改善Howland森林的生物量估计精度。结果表明,采用LiDAR和高光谱植被指数变量模型的最佳精度分别为0.874和0.868,协同高光谱和LiDAR变量并采用梯度提升决策树回归模型的精度为0.927,即多源遥感数据要优于单一数据源。高光谱和LiDAR数据的协同使用对于提高类似于Howland地区或更广泛区域的生物量估计的准确性,具有普遍的适用性与一定的应用前景。  相似文献   

3.
Water Utilisation Index (WUI) defined as area irrigated per unit volume is a measure of water delivery performance and constitutes one of the important spatial performance indicators of an irrigation system. WUI also forms basis for evaluating the adequacy of seasonal irrigation supplies in an irrigation system (inverse of WUI is delta, i.e. depth of water supplied to a given irrigation unit). In the present study WUI and adequacy indicators were used in benchmarking the performance of Nagarjunasagar Left Canal Command (NSLC) in Andhra Pradesh. Optimised temporal satellite data of rabi season during the years 1990–91 and 1998–99 was used in deriving irrigated crop areas adopting hierarchical classification approach. Paddy is the predominant crop grown and cotton, chillies, sugarcane etc. are the other crops grown in the study area. Equivalent wet area (paddy crop area) was estimated using the operationally used project specific conversion factors. WUI was estimated at disaggregated level viz., distributary, irrigation block, irrigation zone level using the canal discharge data. At project level, WUI estimated to be 65 ha/MCM and 92 ha/MCM during rabi season of 1990–91 and 1998–99 years respectively. A comparison of total irrigated area and discharges corresponding to both the years indicate that irrigation service is extensive and sub optimal during 1998–99 and it is intensive and optimal in 1990–91. It was also observed that WUI is lesser in blocks of with higher Culturable Command Area (CCA) compared to the blocks of lower CCA. All the disaggregated units were ranked into various groups of different levels of water distribution performance. The study demonstrates the utility of WUI as spatial performance indicator and thus useful for benchmarking studies of irrigation command areas. The WUI together with satellite data derived spatial irrigation intensity, crop productivity constitutes important benchmarking indices in irrigation command areas.  相似文献   

4.
This paper reports acreage, yield and production forecasting of wheat crop using remote sensing and agrometeorological data for the 1998–99 rabi season. Wheat crop identification and discrimination using Indian Remote Sensing (IRS) ID LISS III satellite data was carried out by supervised maximum likelihood classification. Three types of wheat crop viz. wheat-1 (high vigour-normal sown), wheat-2 (moderate vigour-late sown) and wheat-3 (low vigour-very late sown) have been identified and discriminated from each other. Before final classification of satellite data spectral separability between classes were evaluated. For yield prediction of wheat crop spectral vegetation indices (RVI and NDVI), agrometeorological parameters (ETmax and TD) and historical crop yield (actual yield) trend analysis based linear and multiple linear regression models were developed. The estimated wheat crop area was 75928.0 ha. for the year 1998–99, which sowed ?2.59% underestimation with land record commissioners estimates. The yield prediction through vegetation index based and vegetation index with agrometeorological indices based models were 1753 kg/ha and 1754 kg/ha, respectively and have shown relative deviation of 0.17% and 0.22%, the production estimates from above models when compared with observed production show relative deviation of ?2.4% and ?2.3% underestimations, respectively.  相似文献   

5.
Light detection and ranging system (LIDAR) can obtain diverse remote sensing datasets which contains different land cover information. The datasets offer vital and significant features for land cover classification. As a new and effective deep learning algorithm, stacked auto-encoders (SAE) consists of multiple auto-encoders in which the code of each auto-encoder is the input of the successive one. The classification precision is closely related to hidden layers, and the number of samples in fine-tuning step also affects classification results. In this paper we study the classifiers based on different number of samples and hidden layers. According to appropriate parameters, we promote SAE with adaptive boosting ensemble strategy to build new classification method. Two tests which are based on LIDAR datasets are implemented. The experiment results prove that the fusion of deep learning and ensemble learning is effective to LIDAR remote sensing images. The proposed method is robust to similar scenes classification. The overall accuracy increases 6% compared with bagging method on test 1.  相似文献   

6.
This paper presents an application of Airborne Laser Scanning (ALS) data in conjunction with an IRS LISS-III image for mapping forest fuel types. For two study areas of 165 km2 and 487 km2 in Sicily (Italy), 16,761 plots of size 30-m × 30-m were distributed using a tessellation-based stratified sampling scheme. ALS metrics and spectral signatures from IRS extracted for each plot were used as predictors to classify forest fuel types observed and identified by photointerpretation and fieldwork. Following use of traditional parametric methods that produced unsatisfactory results, three non-parametric classification approaches were tested: (i) classification and regression tree (CART), (ii) the CART bagging method called Random Forests, and (iii) the CART bagging/boosting stochastic gradient boosting (SGB) approach. This contribution summarizes previous experiences using ALS data for estimating forest variables useful for fire management in general and for fuel type mapping, in particular. It summarizes characteristics of classification and regression trees, presents the pre-processing operation, the classification algorithms, and the achieved results. The results demonstrated superiority of the SGB method with overall accuracy of 84%. The most relevant ALS metric was canopy cover, defined as the percent of non-ground returns. Other relevant metrics included the spectral information from IRS and several other ALS metrics such as percentiles of the height distribution, the mean height of all returns, and the number of returns.  相似文献   

7.
Multi temporal dat acquired at different growth stages increases the dimensionality information content and have advantage over single date data for crop classification. Attempt was made to select suitable single date and combination of multidate data for wheat crop classification in Nalanda district of Bihar state where pulses and other crops are also grown in rabi season. Amongst the single date data February data was found to be better for wheat classification in comparison to November. January, March and April data. Combination of first two principal components each derived from IRS LISS-I four band data acquired in January and February was found to be the best set. Wheat classification accuracy achieved was 94.54 percent.  相似文献   

8.
Accurate classification of heterogeneous land surfaces with homogeneous land cover classes is a challenging task as satellite images are characterized by a large number of features in the spectral and spatial domains. The identifying relevance of a feature or feature set is an important task for designing an effective classification scheme. Here, an ensemble of random forests (RF) classifiers is realized on the basis of relevance of features. Correlation‐based Feature Selection (CFS) was utilized to assess the relevance of a subset of features by studying the individual predictive ability of each feature along with the degree of redundancy between them. Predictability of RF was greatly improved by random selection of the relevant features in each of the splits. An investigation was carried out on different types of images from the Landsat Enhanced Thematic Mapper Plus (Landsat ETM+) and QuickBird sensors. It has been observed that the performance of the RF classifier was significantly improved while using the optimal set of relevant features compared with a few of the most advanced supervised classifiers such as maximum likelihood classifier (MLC), Navie Bayes, multi‐layer perception (MLP), support vector machine (SVM) and bagging.  相似文献   

9.
This study investigates the potential of multi-temporal signature analysis of satellite imagery to map rice area in South 24 Paraganas district of West Bengal. Two optical data (IRS ID LISS III) and three RADARSAT SAR data of different dates were acquired during 2001. Multi-temporal SAR backscatter signatures of different landcovers were incorporated into knowledge based decision rules and kharif landcover map was generated. Based on the spectral variation in signature, the optical data acquired during rabi (January) and summer (March) season were classified using supervised maximum likelihood classifier. A co-incidence matrix was generated using logical approach for a combined “rabi-summer” and “kharif-rabi-summer” landcover mapping. The major landcovers obtained in South 24 Paraganas using remote sensing data are rice, water, aquaculture ponds, homestead, mangrove, and urban area. The classification accuracy of rice area was 98.2% using SAR data. However, while generating combined “kharif-rabi-summer” landcovers, the classification accuracy of rice area was improved from 81.6% (optical data) to 96.6% (combined SAR-Optical). The primary aim of the study is to achieve better accuracy in classifying rice area using the synergy between the two kinds of remotely sensed data.  相似文献   

10.
High spectral resolution spectroscopy enables to have detailed information on chemical and morphological status of crop. An attempt of using space platform for detecting red edge shift during different growth stages of wheat crop is reported. Study was conducted during rabi 1996–97 season using Modular Opto-Electronic Scanner MOS-B Imaging data onboard IRS-P3 satellite. Inverted Gaussian model was fitted for satellite derived reflectances between 650 and 870 nm to derive inflection wavelength and its subsequent change with crop stages i.e. red shift. Red shift of 10 nm observed from crown root initiation stage (703.8 nm) to peak vegetative stage (714.2 nm). A comparative study on temporal behaviour of vegetative indices like NDVI and ARVI with Red edge showed that latter is more atmospherically stable parameter. It is concluded that red edge shift which hitherto has been observed from ground and airborne sensors, can also be detected from space.  相似文献   

11.
The invasion by Striga in most cereal crop fields in Africa has posed a significant threat to food security and has caused substantial socioeconomic losses. Hyperspectral remote sensing is an effective means to discriminate plant species, providing possibilities to track such weed invasions and improve precision agriculture. However, essential baseline information using remotely sensed data is missing, specifically for the Striga weed in Africa. In this study, we investigated the spectral uniqueness of Striga compared to other co-occurring maize crops and weeds. We used the in-situ FieldSpec® Handheld 2™ analytical spectral device (ASD), hyperspectral data and their respective narrow-band indices in the visible and near infrared (VNIR) region of the electromagnetic spectrum (EMS) and four machine learning discriminant algorithms (i.e. random forest: RF, linear discriminant analysis: LDA, gradient boosting: GB and support vector machines: SVM) to discriminate among different levels of Striga (Striga hermonthica) infestations in maize fields in western Kenya. We also tested the utility of Sentinel-2 waveband configurations to map and discriminate Striga infestation in heterogenous cereal crop fields. The in-situ hyperspectral reflectance data were resampled to the spectral waveband configurations of Sentinel-2 using published spectral response functions. We sampled and detected seven Striga infestation classes based on three flowering Striga classes (low, moderate and high) against two background endmembers (soil and a mixture of maize and other co-occurring weeds). A guided regularized random forest (GRRF) algorithm was used to select the most relevant hyperspectral wavebands and vegetation indices (VIs) as well as for the resampled Sentinel-2 multispectral wavebands for Striga infestation discrimination. The performance of the four discriminant algorithms was compared using classification accuracy assessment metrics. We were able to positively discriminate Striga from the two background endmembers i.e. soil and co-occurring vegetation (maize and co-occurring weeds) based on the few GRRF selected hyperspectral vegetation indices and the GRRF selected resampled Sentinel-2 multispectral bands. RF outperformed all the other discriminant methods and produced the highest overall accuracy of 91% and 85%, using the hyperspectral and resampled Sentinel-2 multispectral wavebands, respectively, across the four different discriminant models tested in this study. The class with the highest detection accuracy across all the four discriminant algorithms, was the “exclusively maize and other co-occurring weeds” (>70%). The GRRF reduced the dimensionality of the hyperspectral data and selected only 9 most relevant wavebands out of 750 wavebands, 6 VIs out of 15 and 6 out of 10 resampled Sentinel-2 multispectral wavebands for discriminating among the Striga and co-occurring classes. Resampled Sentinel-2 multispectral wavebands 3 (green) and 4 (red) were the most crucial for Striga detection. The use of the most relevant hyperspectral features (i.e. wavebands and VIs) significantly (p ≤ 0.05) increased the overall classification accuracy and Kappa scores (±5% and ±0.2, respectively) in all the machine learning discriminant models. Our results show the potential of hyperspectral, resampled Sentinel-2 multispectral datasets and machine learning discriminant algorithms as a tool to accurately discern Striga in heterogenous maize agro-ecological systems.  相似文献   

12.
The objective of this paper is to demonstrate a new method to map the distributions of C3 and C4 grasses at 30 m resolution and over a 25-year period of time (1988–2013) by combining the Random Forest (RF) classification algorithm and patch stable areas identified using the spatial pattern analysis software FRAGSTATS. Predictor variables for RF classifications consisted of ten spectral variables, four soil edaphic variables and three topographic variables. We provided a confidence score in terms of obtaining pure land cover at each pixel location by retrieving the classification tree votes. Classification accuracy assessments and predictor variable importance evaluations were conducted based on a repeated stratified sampling approach. Results show that patch stable areas obtained from larger patches are more appropriate to be used as sample data pools to train and validate RF classifiers for historical land cover mapping purposes and it is more reasonable to use patch stable areas as sample pools to map land cover in a year closer to the present rather than years further back in time. The percentage of obtained high confidence prediction pixels across the study area ranges from 71.18% in 1988 to 73.48% in 2013. The repeated stratified sampling approach is necessary in terms of reducing the positive bias in the estimated classification accuracy caused by the possible selections of training and validation pixels from the same patch stable areas. The RF classification algorithm was able to identify the important environmental factors affecting the distributions of C3 and C4 grasses in our study area such as elevation, soil pH, soil organic matter and soil texture.  相似文献   

13.
Land suitability analysis is prerequisite for sustainable agriculture and it plays a pivotal role in the niche based agricultural planning in mountain regions. In this paper different parameters viz. climatic (precipitation and temperature), topographic (elevation), soil type and land cover/land use have been used in order to perform land suitability evaluation for cereals food-grain crops in Himachal Pradesh using Geographic Information System (GIS). The suitability analysis was performed by digital processing of geo-referenced data (elevation, climate, soil and landcover) and calculating potential production areas by combining different types of geographical data through decision rules framed for each crop in ArcView spatial analyst. Suitable areas have been delineated for cereal crops in the form of land suitability maps. In comparison to the actual area under cereal crops, the possibility of further expansion under each cereal crop was determined. These discriminated areas appear suitable for growing these crops and can be harnessed efficiently for achieving long term sustainability and food security.  相似文献   

14.
Two band simulad WiFS data for five dates correspfonding to rabi sorghun growing season of 1993-94 has been generated for Aurangabad district of Maharashtra. Ground truth data has been used for supervised classificatioa of one date raw image and five date NDVI of simulated WiFS data and the results were compared with those derived from single date IRS LISS I data. Analysis of classification accuracies indicate that single date WIFS data gives slightly lower accuracy of 79 per cent against 81 per cent obtained for single date LISS I data. Overall accuracy for 5-date WiFS data is 96 per cent which shows that classification performance of five date WiFS NDVI data is far superior to the single date data of the IRS-IC WiFS as well as the IRS LISS I. The study thus shows the importance of temporal domain of data acquisition in sorghum crop discrimination, Growth profile for sorghum and other crop classes were generated from multidate WiFS derived NDVI data. Differences in growth profiles of sorghum vigour classes as well as amongst different crop types and forests corroborate the premise of better discrimination of crop types and their vigour on multidate remotely sensed data.  相似文献   

15.
ABSTRACT

Tree species distribution mapping using remotely sensed data has long been an important research area. However, previous studies have rarely established a comprehensive and efficient classification procedure to obtain an accurate result. This study proposes a hierarchical classification procedure with optimized node variables and thresholds to classify tree species based on high spatial resolution satellite imagery. A classification tree structure consisting of parent and leaf nodes was designed based on user experience and visual interpretation. Spectral, textural, and topographic variables were extracted based on pre-segmented images. The random forest algorithm was used to select variables by ranking the impact of all variables. An iterating approach was used to optimize variables and thresholds in each loop by comprehensively considering the test accuracy and selected variables. The threshold range for each selected variable was determined by a statistical method considering the mean and standard deviation for two subnode types at each parent node. Classification of tree species was implemented using the optimized variables and thresholds. The results show that (1) the proposed procedure can accurately map the tree species distribution, with an overall accuracy of over 86% for both training and test stages; (2) critical variables for each class can be identified using this proposed procedure, and optimal variables of most tree plantation nodes are spectra related; (3) the overall forest classification accuracy using the proposed method is more accurate than that using the random forest (RF) and classification and regression tree (CART). The proposed approach provides results with 3.21% and 7.56% higher overall land cover classification accuracy and 4.68% and 10.28% higher overall forest classification accuracy than RF and CART, respectively.  相似文献   

16.
研究山区地表水体信息OLI遥感数据去阴影自动提取方法,设计基于数字高程模型与指数提取的决策树分类方法,提高水体自动识别的精度。该方法选取改进的归一化水体指数、归一化植被指数、比值植被指数、主成分分析前3个分量以及波段之间的组合运算,并结合DEM构建决策树分类规则。综合采用单波段阈值、谱间关系、植被指数和水体指数阈值完成山体水体的去阴影识别研究,与计算机自动识别分类方法比较,其精度明显提高。结果表明,决策树分类方法在精度上明显高于常用的计算机自动分类方法,可以很好地被利用于OLI遥感数据水体信息的海量、大范围提取。  相似文献   

17.
An attempt has been made to understand the potential of temporal Advanced Wide Field Sensor (AWiFS) data aboard IRS-P6 (Resourcesat) to generate the land use land cover information along with the net sown area. The temporal data sets were georeferenced, converted to top of atmosphere reflectance and classified using decision tree classifier, See5. Results indicate that the temporal data set could give a better definition of training sites thereby resulting in good overall kappa (kappa = 0.8651) as well as individual classification accuracies. However, co-registration of temporal datasets accuracies also has got a significant influence on the classification accuracy. Temporal variation in cloud infestation and availability of appropriate data sets within the season (before harvest of the crop) has also affected the classification accuracy.  相似文献   

18.
Accurate spatio-temporal classification of crops is of prime importance for in-season crop monitoring. Synthetic Aperture Radar (SAR) data provides diverse physical information about crop morphology. In the present work, we propose a day-wise and a time-series approach for crop classification using full-polarimetric SAR data. In this context, the 4 × 4 real Kennaugh matrix representation of a full-polarimetric SAR data is utilized, which can provide valuable information about various morphological and dielectric attributes of a scatterer. The elements of the Kennaugh matrix are used as the parameters for the classification of crop types using the random forest and the extreme gradient boosting classifiers.The time-series approach uses data patterns throughout the whole growth period, while the day-wise approach analyzes the PolSAR data from each acquisition into a single data stack for training and validation. The main advantage of this approach is the possibility of generating an intermediate crop map, whenever a SAR acquisition is available for any particular day. Besides, the day-wise approach has the least climatic influence as compared to the time series approach. However, as time-series data retains the crop growth signature in the entire growth cycle, the classification accuracy is usually higher than the day-wise data.Within the Joint Experiment for Crop Assessment and Monitoring (JECAM) initiative, in situ measurements collected over the Canadian and Indian test sites and C-band full-polarimetric RADARSAT-2 data are used for the training and validation of the classifiers. Besides, the sensitivity of the Kennaugh matrix elements to crop morphology is apparent in this study. The overall classification accuracies of 87.75% and 80.41% are achieved for the time-series data over the Indian and Canadian test sites, respectively. However, for the day-wise data, a ∼6% decrease in the overall accuracy is observed for both the classifiers.  相似文献   

19.
Real time, accurate and reliable estimation of maize yield is valuable to policy makers in decision making. The current study was planned for yield estimation of spring maize using remote sensing and crop modeling. In crop modeling, the CERES-Maize model was calibrated and evaluated with the field experiment data and after calibration and evaluation, this model was used to forecast maize yield. A Field survey of 64 farm was also conducted in Faisalabad to collect data on initial field conditions and crop management data. These data were used to forecast maize yield using crop model at farmers’ field. While in remote sensing, peak season Landsat 8 images were classified for landcover classification using machine learning algorithm. After classification, time series normalized difference vegetation index (NDVI) and land surface temperature (LST) of the surveyed 64 farms were calculated. Principle component analysis were run to correlate the indicators with maize yield. The selected LSTs and NDVIs were used to develop yield forecasting equations using least absolute shrinkage and selection operator (LASSO) regression. Calibrated and evaluated results of CERES-Maize showed the mean absolute % error (MAPE) of 0.35–6.71% for all recorded variables. In remote sensing all machine learning algorithms showed the accuracy greater the 90%, however support vector machine (SVM-radial basis) showed the higher accuracy of 97%, that was used for classification of maize area. The accuracy of area estimated through SVM-radial basis was 91%, when validated with crop reporting service. Yield forecasting results of crop model were precise with RMSE of 255 kg ha?1, while remote sensing showed the RMSE of 397 kg ha?1. Overall strength of relationship between estimated and actual grain yields were good with R2 of 0.94 in both techniques. For regional yield forecasting remote sensing could be used due greater advantages of less input dataset and if focus is to assess specific stress, and interaction of plant genetics to soil and environmental conditions than crop model is very useful tool.  相似文献   

20.
精准农田识别是农作物估产和粮食安全评估的基础。遥感数据作为农田识别的重要数据源,可提供动态、快速的监测结果。高光谱数据在农田识别分类方面具有巨大的应用潜力,但其中的冗余波段影响了分类效率和分类精度。因此,本研究提出了一种适用于高光谱数据农田分类的混合式特征选择算法。首先,基于变量的重要性排序或约束程度,按步长逐步进行降维;其次,寻找分类精度骤减的转折点,并将其对应的变量作为特征子集;最后,利用序列后向选择SBS（Sequential Backward Selection）方法搜索最优分类特征子集。本研究利用GF-5高光谱数据,共研究了3种降维方法（随机森林RF（Random Forest）、互信息MI（Multi-Information）和L1正则化（L1 regularization））和3种分类算法（随机森林、支持向量机SVM（Support Vector Machine）和K近邻KNN（K-Nearest Neighbor））的组合在农田分类中的表现。结果表明,基于L1正则化法得到的特征子集自相关性较低,并且包含的红边和近红外波段有效提高了农田、森林和裸土的区分度。在不同分类模型比较中发现,SVM在高维空间中表现出非常好的抗噪能力,分类精度高于RF和KNN。而RF在低维空间中的泛化能力要高于SVM和KNN。相比于第一步降维得到的特征子集,使用SBS搜索得到的最优特征子集均提高了分类精度。最终,具有23维输入的L1-SVM-SBS分类模型得到了最高的总体分类精度（94.64%）和农田召回率（95.83%）。本研究为高光谱数据特征优选提供了一种新思路,筛选出了更具代表性的特征波段,提高了农田分类精度,对高光谱遥感分类研究具有参考价值。  相似文献   

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