Using spectrotemporal indices to improve the fruit-tree crop classification accuracy

2017 ◽  
Vol 128 ◽  
pp. 158-169 ◽  
Author(s):  
M.A. Peña ◽  
R. Liao ◽  
A. Brenning
Keyword(s):  
Classification Accuracy ◽  
Fruit Tree ◽  
Tree Crop ◽  
Crop Classification

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

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.

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

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.

The effect of EVI time series density on crop classification accuracy

Optik ◽  
2018 ◽  
Vol 157 ◽  
pp. 1065-1072 ◽  
Author(s):  
Yulin Zhan ◽  
Shakir Muhammad ◽  
Pengyu Hao ◽  
Zheng Niu
Keyword(s):  
Time Series ◽  
Classification Accuracy ◽  
Crop Classification

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

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.

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

2019 ◽  
Vol XLII-3/W6 ◽  
pp. 573-578 ◽  
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

<p><strong>Abstract.</strong> 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&amp;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.</p>

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

2018 ◽  
Vol XLII-1 ◽  
pp. 451-456 ◽  
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

<p><strong>Abstract.</strong> 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 (<span style="text-decoration: overline">&amp;alpha;</span>) 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<span style="text-decoration: overline">&amp;alpha;</span>, (2) H<span style="text-decoration: overline">&amp;alpha;</span>Span, (3) H<span style="text-decoration: overline">&amp;alpha;</span>A, (4) H<span style="text-decoration: overline">&amp;alpha;</span>ASpan and (5) coherency [<i>T</i>] 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&amp;alpha;ASpan (91.43<span class="thinspace"></span>% for SVM, 92.25<span class="thinspace"></span>% for RF and 90.55<span class="thinspace"></span>% 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<span class="thinspace"></span>% by RF and H&amp;alpha;ASpan while lowest classification accuracy was obtained as 66.99<span class="thinspace"></span>% by NB and H&amp;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.</p>

scholarly journals Origin and diversity of an underutilized fruit tree crop, cempedak (Artocarpus integer, Moraceae)

2018 ◽  
Vol 105 (5) ◽  
pp. 898-914 ◽  
Author(s):  
Maria M. H. Wang ◽  
Elliot M. Gardner ◽  
Richard C. K. Chung ◽  
Ming Yee Chew ◽  
Abd Rahman Milan ◽  
...  
Keyword(s):  
Fruit Tree ◽  
Tree Crop

scholarly journals Polarimetric Target Decompositions and Light Gradient Boosting Machine for Crop Classification: A Comparative Evaluation

2019 ◽  
Vol 8 (2) ◽  
pp. 97 ◽  
Author(s):  
Mustafa Ustuner ◽  
Fusun Balik Sanli
Keyword(s):  
Classification Accuracy ◽  
Learning Algorithm ◽  
Test Site ◽  
Gradient Boosting ◽  
Model Based ◽  
Light Gradient ◽  
Gradient Boosting Machine ◽  
Multi Temporal ◽  
Decomposition Models ◽  
Crop Classification

In terms of providing various scattering mechanisms, polarimetric target decompositions provide certain benefits for the interpretation of PolSAR images. This paper tested the capabilities of different polarimetric target decompositions in crop classification, while using a recently launched ensemble learning algorithm—namely Light Gradient Boosting Machine (LightGBM). For the classification of different crops (maize, potato, wheat, sunflower, and alfalfa) in the test site, multi-temporal polarimetric C-band RADARSAT-2 images were acquired over an agricultural area near Konya, Turkey. Four different decomposition models (Cloude–Pottier, Freeman–Durden, Van Zyl, and Yamaguchi) were employed to evaluate polarimetric target decomposition for crop classification. Besides the polarimetric target decomposed parameters, the original polarimetric features (linear backscatter coefficients, coherency, and covariance matrices) were also incorporated for crop classification. The experimental results demonstrated that polarimetric target decompositions, with the exception of Cloude–Pottier, were found to be superior to the original features in terms of overall classification accuracy. The highest classification accuracy (92.07%) was achieved by Yamaguchi, whereas the lowest (75.99%) was achieved by the covariance matrix. Model-based decompositions achieved higher performance with respect to eigenvector-based decompositions in terms of class-based accuracies. Furthermore, the results emphasize the added benefits of model-based decompositions for crop classification using PolSAR data.

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