Crop classification accuracy as influenced by training strategy, data transformation and spatial resolution of data

Quality tree species information gathering is the basis for making proper decisions in forest management. By applying new technologies and remote sensing methods, very high resolution (VHR) satellite imagery can give sufficient spatial detail to achieve accurate species-level classification. In this study, the influence of pansharpening of the WorldView-3 (WV-3) satellite imagery on classification results of three main tree species (Quercus robur L., Carpinus betulus L., and Alnus glutinosa (L.) Geartn.) has been evaluated. In order to increase tree species classification accuracy, three different pansharpening algorithms (Bayes, RCS, and LMVM) have been conducted. The LMVM algorithm proved the most effective pansharpening technique. The pixel- and object-based classification were applied to three pansharpened imageries using a random forest (RF) algorithm. The results showed a very high overall accuracy (OA) for LMVM pansharpened imagery: 92% and 96% for tree species classification based on pixel- and object-based approach, respectively. As expected, the object-based exceeded the pixel-based approach (OA increased by 4%). The influence of fusion on classification results was analyzed as well. Overall classification accuracy was improved by the spatial resolution of pansharpened images (OA increased by 7% for pixel-based approach). Also, regardless of pixel- or object-based classification approaches, the influence of the use of pansharpening is highly beneficial to classifying complex, natural, and mixed deciduous forest areas.

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Early Classification Method for US Corn and Soybean by Incorporating MODIS-Estimated Phenological Data and Historical Classification Maps in Random-Forest Regression Algorithm

Photogrammetric Engineering & Remote Sensing ◽

10.14358/pers.21-00003r2 ◽

2021 ◽

Vol 87 (10) ◽

pp. 747-758

Author(s):

Toshihiro Sakamoto

Keyword(s):

Random Forest ◽

Classification Accuracy ◽

Classification Method ◽

Estimation Accuracy ◽

Random Forest Regression ◽

Crop Phenology ◽

Phenological Data ◽

Mixed Pixel ◽

Crop Classification ◽

Emergence Date

An early crop classification method is functionally required in a near-real-time crop-yield prediction system, especially for upland crops. This study proposes methods to estimate the mixed-pixel ratio of corn, soybean, and other classes within a low-resolution MODIS pixel by coupling MODIS-derived crop phenology information and the past Cropland Data Layer in a random-forest regression algorithm. Verification of the classification accuracy was conducted for the Midwestern United States. The following conclusions are drawn: The use of the random-forest algorithm is effective in estimating the mixed-pixel ratio, which leads to stable classification accuracy; the fusion of historical data and MODIS-derived crop phenology information provides much better crop classification accuracy than when these are used individually; and the input of a longer MODIS data period can improve classification accuracy, especially after day of year 279, because of improved estimation accuracy for the soybean emergence date.

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Noisy ECG Signal Data Transformation to Augment Classification Accuracy

Computers Materials & Continua ◽

10.32604/cmc.2022.022711 ◽

2022 ◽

Vol 71 (2) ◽

pp. 2191-2207

Author(s):

Iqra Afzal ◽

Fiaz Majeed ◽

Muhammad Usman Ali ◽

Shahzada Khurram ◽

Akber Abid Gardezi ◽

...

Keyword(s):

Classification Accuracy ◽

Data Transformation ◽

Ecg Signal

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Feature Dimension Reduction Using Stacked Sparse Auto-Encoders for Crop Classification with Multi-Temporal, Quad-Pol SAR Data

Remote Sensing ◽

10.3390/rs12020321 ◽

2020 ◽

Vol 12 (2) ◽

pp. 321

Author(s):

Jiao Guo ◽

Henghui Li ◽

Jifeng Ning ◽

Wenting Han ◽

Weitao Zhang ◽

...

Keyword(s):

Dimension Reduction ◽

Classification Accuracy ◽

Short Term Memory ◽

European Space Agency ◽

Agricultural Crop ◽

Temporal Data ◽

Multi Temporal ◽

Crop Classification ◽

Feature Dimension ◽

Feature Dimension Reduction

Crop classification in agriculture is one of important applications for polarimetric synthetic aperture radar (PolSAR) data. For agricultural crop discrimination, compared with single-temporal data, multi-temporal data can dramatically increase crop classification accuracies since the same crop shows different external phenomena as it grows up. In practice, the utilization of multi-temporal data encounters a serious problem known as a “dimension disaster”. Aiming to solve this problem and raise the classification accuracy, this study developed a feature dimension reduction method using stacked sparse auto-encoders (S-SAEs) for crop classification. First, various incoherent scattering decomposition algorithms were employed to extract a variety of detailed and quantitative parameters from multi-temporal PolSAR data. Second, based on analyzing the configuration and main parameters for constructing an S-SAE, a three-hidden-layer S-SAE network was built to reduce the dimensionality and extract effective features to manage the “dimension disaster” caused by excessive scattering parameters, especially for multi-temporal, quad-pol SAR images. Third, a convolutional neural network (CNN) was constructed and employed to further enhance the crop classification performance. Finally, the performances of the proposed strategy were assessed with the simulated multi-temporal Sentinel-1 data for two experimental sites established by the European Space Agency (ESA). The experimental results showed that the overall accuracy with the proposed method was raised by at least 17% compared with the long short-term memory (LSTM) method in the case of a 1% training ratio. Meanwhile, for a CNN classifier, the overall accuracy was almost 4% higher than those of the principle component analysis (PCA) and locally linear embedded (LLE) methods. The comparison studies clearly demonstrated the advantage of the proposed multi-temporal crop classification methodology in terms of classification accuracy, even with small training ratios.

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Training Convolutional Neural Networks with Multi-Size Images and Triplet Loss for Remote Sensing Scene Classification

Sensors ◽

10.3390/s20041188 ◽

2020 ◽

Vol 20 (4) ◽

pp. 1188 ◽

Cited By ~ 10

Author(s):

Jianming Zhang ◽

Chaoquan Lu ◽

Jin Wang ◽

Xiao-Guang Yue ◽

Se-Jung Lim ◽

...

Keyword(s):

Remote Sensing ◽

Classification Accuracy ◽

Model Parameters ◽

Classification Algorithms ◽

Scene Classification ◽

Training Strategy ◽

Network Training ◽

Training Stage ◽

Triplet Loss ◽

And Training

Many remote sensing scene classification algorithms improve their classification accuracy by additional modules, which increases the parameters and computing overhead of the model at the inference stage. In this paper, we explore how to improve the classification accuracy of the model without adding modules at the inference stage. First, we propose a network training strategy of training with multi-size images. Then, we introduce more supervision information by triplet loss and design a branch for the triplet loss. In addition, dropout is introduced between the feature extractor and the classifier to avoid over-fitting. These modules only work at the training stage and will not bring about the increase in model parameters at the inference stage. We use Resnet18 as the baseline and add the three modules to the baseline. We perform experiments on three datasets: AID, NWPU-RESISC45, and OPTIMAL. Experimental results show that our model combined with the three modules is more competitive than many existing classification algorithms. In addition, ablation experiments on OPTIMAL show that dropout, triplet loss, and training with multi-size images improve the overall accuracy of the model on the test set by 0.53%, 0.38%, and 0.7%, respectively. The combination of the three modules improves the overall accuracy of the model by 1.61%. It can be seen that the three modules can improve the classification accuracy of the model without increasing model parameters at the inference stage, and training with multi-size images brings a greater gain in accuracy than the other two modules, but the combination of the three modules will be better.

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The effect of EVI time series density on crop classification accuracy

Optik ◽

10.1016/j.ijleo.2017.11.157 ◽

2018 ◽

Vol 157 ◽

pp. 1065-1072 ◽

Cited By ~ 4

Author(s):

Yulin Zhan ◽

Shakir Muhammad ◽

Pengyu Hao ◽

Zheng Niu

Keyword(s):

Time Series ◽

Classification Accuracy ◽

Crop Classification

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Effects of Class Purity of Training Patch on Classification Performance of Crop Classification with Convolutional Neural Network

Applied Sciences ◽

10.3390/app10113773 ◽

2020 ◽

Vol 10 (11) ◽

pp. 3773

Author(s):

Soyeon Park ◽

No-Wook Park

Keyword(s):

Classification Accuracy ◽

Supervised Classification ◽

Patch Size ◽

Classification Performance ◽

Spatial Homogeneity ◽

High Class ◽

Large Size ◽

Training Samples ◽

Compositional Homogeneity ◽

Crop Classification

As the performance of supervised classification using convolutional neural networks (CNNs) are affected significantly by training patches, it is necessary to analyze the effects of the information content of training patches in patch-based classification. The objective of this study is to quantitatively investigate the effects of class purity of a training patch on performance of crop classification. Here, class purity that refers to a degree of compositional homogeneity of classes within a training patch is considered as a primary factor for the quantification of information conveyed by training patches. New quantitative indices for class homogeneity and variations of local class homogeneity over the study area are presented to characterize the spatial homogeneity of the study area. Crop classification using 2D-CNN was conducted in two regions (Anbandegi in Korea and Illinois in United States) with distinctive spatial distributions of crops and class homogeneity over the area to highlight the effect of class purity of a training patch. In the Anbandegi region with high class homogeneity, superior classification accuracy was obtained when using large size training patches with high class purity (7.1%p improvement in overall accuracy over classification with the smallest patch size and the lowest class purity). Training patches with high class purity could yield a better identification of homogenous crop parcels. In contrast, using small size training patches with low class purity yielded the highest classification accuracy in the Illinois region with low class homogeneity (19.8%p improvement in overall accuracy over classification with the largest patch size and the highest class purity). Training patches with low class purity could provide useful information for the identification of diverse crop parcels. The results indicate that training samples in patch-based classification should be selected based on the class purity that reflects the local class homogeneity of the study area.

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EXPLORING MACHINE LEARNING CLASSIFICATION ALGORITHMS FOR CROP CLASSIFICATION USING SENTINEL 2 DATA

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-3-w6-573-2019 ◽

2019 ◽

Vol XLII-3/W6 ◽

pp. 573-578 ◽

Cited By ~ 3

Author(s):

◽

S. S. Ray

Keyword(s):

Machine Learning ◽

Satellite Data ◽

Classification Accuracy ◽

Ground Truth ◽

Kappa Coefficient ◽

Ground Truth Data ◽

Classification Techniques ◽

Machine Learning Classification ◽

Crop Classification ◽

Sentinel 2

Abstract. Crop Classification and recognition is a very important application of Remote Sensing. In the last few years, Machine learning classification techniques have been emerging for crop classification. Google Earth Engine (GEE) is a platform to explore the multiple satellite data with different advanced classification techniques without even downloading the satellite data. The main objective of this study is to explore the ability of different machine learning classification techniques like, Random Forest (RF), Classification And Regression Trees (CART) and Support Vector Machine (SVM) for crop classification. High Resolution optical data, Sentinel-2, MSI (10&thinsp;m) was used for crop classification in the Indian Agricultural Research Institute (IARI) farm for the Rabi season 2016 for major crops. Around 100 crop fields (~400 Hectare) in IARI were analysed. Smart phone-based ground truth data were collected. The best cloud free image of Sentinel 2 MSI data (5 Feb 2016) was used for classification using automatic filtering by percentage cloud cover property using the GEE. Polygons as feature space was used as training data sets based on the ground truth data for crop classification using machine learning techniques. Post classification, accuracy assessment analysis was done through the generation of the confusion matrix (producer and user accuracy), kappa coefficient and F value. In this study it was found that using GEE through cloud platform, satellite data accessing, filtering and pre-processing of satellite data could be done very efficiently. In terms of overall classification accuracy and kappa coefficient, Random Forest (93.3%, 0.9178) and CART (73.4%, 0.6755) classifiers performed better than SVM (74.3%, 0.6867) classifier. For validation, Field Operation Service Unit (FOSU) division of IARI, data was used and encouraging results were obtained.

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THE ANALYSIS OF BURROWS RECOGNITION ACCURACY IN XINJIANG'S PASTURE AREA BASED ON UAV VISIBLE IMAGES WITH DIFFERENT SPATIAL RESOLUTION

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-3-1575-2018 ◽

2018 ◽

Vol XLII-3 ◽

pp. 1575-1579 ◽

Cited By ~ 1

Author(s):

D. Sun ◽

J. H. Zheng ◽

T. Ma ◽

J. J. Chen ◽

X. Li

Keyword(s):

Spatial Resolution ◽

Classification Accuracy ◽

Animal Husbandry ◽

Identification Accuracy ◽

Future Research ◽

Ground Vegetation ◽

Visible Images ◽

Low Altitude ◽

Pasture Area ◽

The Impact

The rodent disaster is one of the main biological disasters in grassland in northern Xinjiang. The eating and digging behaviors will cause the destruction of ground vegetation, which seriously affected the development of animal husbandry and grassland ecological security. UAV low altitude remote sensing, as an emerging technique with high spatial resolution, can effectively recognize the burrows. However, how to select the appropriate spatial resolution to monitor the calamity of the rodent disaster is the first problem we need to pay attention to. The purpose of this study is to explore the optimal spatial scale on identification of the burrows by evaluating the impact of different spatial resolution for the burrows identification accuracy. In this study, we shoot burrows from different flight heights to obtain visible images of different spatial resolution. Then an object-oriented method is used to identify the caves, and we also evaluate the accuracy of the classification. We found that the highest classification accuracy of holes, the average has reached more than 80&thinsp;%. At the altitude of 24&thinsp;m and the spatial resolution of 1cm, the accuracy of the classification is the highest We have created a unique and effective way to identify burrows by using UAVs visible images. We draw the following conclusion: the best spatial resolution of burrows recognition is 1&thinsp;cm using DJI PHANTOM-3 UAV, and the improvement of spatial resolution does not necessarily lead to the improvement of classification accuracy. This study lays the foundation for future research and can be extended to similar studies elsewhere.

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AN APPLICATION OF ROLL-INVARIANT POLARIMETRIC FEATURES FOR CROP CLASSIFICATION FROM MULTI-TEMPORAL RADARSAT-2 SAR DATA

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-1-451-2018 ◽

2018 ◽

Vol XLII-1 ◽

pp. 451-456 ◽

Cited By ~ 1

Author(s):

M. Ustuner ◽

F. B. Sanli ◽

S. Abdikan ◽

M. T. Esetlili ◽

G. Bilgin

Keyword(s):

Classification Accuracy ◽

Alpha Angle ◽

Test Site ◽

Classification Performance ◽

Support Vector ◽

Observation Data ◽

Data Set ◽

Crop Classification ◽

Sar Data ◽

Spatio Temporal

Abstract. Crops are dynamically changing and time-critical in the growing season and therefore multitemporal earth observation data are needed for spatio-temporal monitoring of the crops. This study evaluates the impacts of classical roll-invariant polarimetric features such as entropy (H), anisotropy (A), mean alpha angle (&alpha;) and total scattering power (SPAN) for the crop classification from multitemporal polarimetric SAR data. For this purpose, five different data set were generated as following: (1) H&alpha;, (2) H&alpha;Span, (3) H&alpha;A, (4) H&alpha;ASpan and (5) coherency [T] matrix. A time-series of four PolSAR data (Radarsat-2) were acquired as 13 June, 01 July, 31 July and 24 August in 2016 for the test site located in Konya, Turkey. The test site is covered with crops (maize, potato, summer wheat, sunflower, and alfalfa). For the classification of the data set, three different models were used as following: Support Vector Machines (SVMs), Random Forests (RFs) and Naive Bayes (NB). The experimental results highlight that H&alpha;ASpan (91.43% for SVM, 92.25% for RF and 90.55% for NB) outperformed all other data sets in terms of classification performance, which explicitly proves the significant contribution of SPAN for the discrimination of crops. Highest classification accuracy was obtained as 92.25% by RF and H&alpha;ASpan while lowest classification accuracy was obtained as 66.99% by NB and H&alpha;. This experimental study suggests that roll-invariant polarimetric features can be considered as the powerful polarimetric components for the crop classification. In addition, the findings prove the added benefits of PolSAR data investigation by means of crop classification.

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