Object-based crop classification using multi-temporal SPOT-5 imagery and textural features with a Random Forest classifier

2017 ◽  
Vol 33 (10) ◽  
pp. 1017-1035 ◽  
Author(s):  
Huanxue Zhang ◽  
Qiangzi Li ◽  
Jiangui Liu ◽  
Xin Du ◽  
Taifeng Dong ◽  
...  
Keyword(s):  
Random Forest ◽  
Random Forest Classifier ◽  
Textural Features ◽  
Object Based ◽  
Multi Temporal ◽  
Crop Classification ◽  
Spot 5

scholarly journals On the Use of Neumann Decomposition for Crop Classification Using Multi-Temporal RADARSAT-2 Polarimetric SAR Data

2019 ◽  
Vol 11 (7) ◽  
pp. 776 ◽  
Author(s):  
Qinghua Xie ◽  
Jinfei Wang ◽  
Chunhua Liao ◽  
Jiali Shang ◽  
Juan Lopez-Sanchez ◽  
...  
Keyword(s):  
Time Series ◽  
Random Forest ◽  
Supervised Classification ◽  
Alpha Angle ◽  
Morphological Characteristics ◽  
Random Forest Classifier ◽  
Decomposition Approach ◽  
Multi Temporal ◽  
Crop Classification ◽  
Two Parameters

In previous studies, parameters derived from polarimetric target decompositions have proven as very effective features for crop classification with single/multi-temporal polarimetric synthetic aperture radar (PolSAR) data. In particular, a classical eigenvalue-eigenvector-based decomposition approach named after Cloude–Pottier decomposition (or “H/A/α”) has been frequently used to construct classification approaches. A model-based decomposition approach proposed by Neumann some years ago provides two parameters with very similar physical meanings to polarimetric scattering entropy H and the alpha angle α in Cloude–Pottier decomposition. However, the main aim of the Neumann decomposition is to describe the morphological characteristics of vegetation. Therefore, it is worth investigating the performance of Neumann decomposition on crop classification, since vegetation is the principal type of targets in agricultural scenes. In this paper, a multi-temporal supervised classification method based on Neumann decomposition and Random Forest Classifier (named “ND-RF”) is proposed. The three parameters from Neumann decomposition, computed along the time series of data, are used as classification features. Finally, the Random Forest Classifier is applied for supervised classification. For comparison, an analogue classification scheme is constructed by replacing the Neumann decomposition with the Cloude–Pottier decomposition, hence named CP-RF. For validation, a time series of 11 polarimetric RADARSAT-2 SAR images acquired over an agricultural site in London, Ontario, Canada in 2015 is employed. Totally, 10 multi-temporal combinations of datasets were tested by adding images one by one sequentially along the SAR observation time. The results show that the ND-RF method generally produces better classification performance than the CP-RF method, with the largest improvement of over 12% in overall accuracy. Further tests show that the two parameters similar to entropy and alpha angle produce classification results close to those of CP-RF, whereas the third parameter in the Neumann decomposition is more effective in improving the classification accuracy with respect to the Cloude–Pottier decomposition.

scholarly journals From Land Cover Map to Land Use Map: A Combined Pixel-Based and Object-Based Approach Using Multi-Temporal Landsat Data, a Random Forest Classifier, and Decision Rules

2021 ◽  
Vol 13 (9) ◽  
pp. 1700
Author(s):  
Dang Hung Bui ◽  
László Mucsi
Keyword(s):  
Land Use ◽  
Random Forest ◽  
Land Cover ◽  
Decision Rules ◽  
Random Forest Classifier ◽  
Landsat Images ◽  
Object Based ◽  
Multi Temporal ◽  
Land Cover Map ◽  
Land Cover Maps

It is essential to produce land cover maps and land use maps separately for different purposes. This study was conducted to generate such maps in Binh Duong province, Vietnam, using a novel combination of pixel-based and object-based classification techniques and geographic information system (GIS) analysis on multi-temporal Landsat images. Firstly, the connection between land cover and land use was identified; thereafter, the land cover map and land use function regions were extracted with a random forest classifier. Finally, a land use map was generated by combining the land cover map and the land use function regions in a set of decision rules. The results showed that land cover and land use were linked by spectral, spatial, and temporal characteristics, and this helped effectively convert the land cover map into a land use map. The final land cover map attained an overall accuracy (OA) = 93.86%, with producer’s accuracy (PA) and user’s accuracy (UA) of its classes ranging from 73.91% to 100%. Meanwhile, the final land use map achieved OA = 93.45%, and the UA and PA ranged from 84% to 100%. The study demonstrated that it is possible to create high-accuracy maps based entirely on free multi-temporal satellite imagery that promote the reproducibility and proactivity of the research as well as cost-efficiency and time savings.

scholarly journals Mid-season Crop Classification Using Dual-, Compact-, and Full-polarization in Preparation for the Radarsat Constellation Mission (RCM)

2019 ◽  
Vol 11 (13) ◽  
pp. 1582 ◽  
Author(s):  
Mahdianpari ◽  
Mohammadimanesh ◽  
McNairn ◽  
Davidson ◽  
Rezaee ◽  
...  
Keyword(s):  
Variable Importance ◽  
Sar Images ◽  
Object Based ◽  
Multi Temporal ◽  
Importance Analysis ◽  
Crop Mapping ◽  
Crop Classification ◽  
Sar Data ◽  
Highly Correlated ◽  
Marginal Improvement

Despite recent research on the potential of dual- (DP) and full-polarimetry (FP) Synthetic Aperture Radar (SAR) data for crop mapping, the capability of compact polarimetry (CP) SAR data has not yet been thoroughly investigated. This is of particular concern, given the availability of such data from RADARSAT Constellation Mission (RCM) shortly. Previous studies have illustrated potential for accurate crop mapping using DP and FP SAR features, yet what contribution each feature makes to the model accuracy is not well investigated. Accordingly, this study examined the potential of the early- to mid-season (i.e., May to July) RADARSAT-2 SAR images for crop mapping in an agricultural region in Manitoba, Canada. Various classification scenarios were defined based on the extracted features from FP SAR data, as well as simulated DP and CP SAR data at two different noise floors. Both overall and individual class accuracies were compared for multi-temporal, multi-polarization SAR data using the pixel- and object-based random forest (RF) classification schemes. The late July C-band SAR observation was the most useful data for crop mapping, but the accuracy of single-date image classification was insufficient. Polarimetric decomposition features extracted from CP and FP SAR data produced relatively equal or slightly better classification accuracies compared to the SAR backscattering intensity features. The RF variable importance analysis revealed features that were sensitive to depolarization due to the volume scattering are the most important FP and CP SAR data. Synergistic use of all features resulted in a marginal improvement in overall classification accuracies, given that several extracted features were highly correlated. A reduction of highly correlated features based on integrating the Spearman correlation coefficient and the RF variable importance analyses boosted the accuracy of crop classification. In particular, overall accuracies of 88.23%, 82.12%, and 77.35% were achieved using the optimized features of FP, CP, and DP SAR data, respectively, using the object-based RF algorithm.

Winter wheat mapping using a random forest classifier combined with multi-temporal and multi-sensor data

2017 ◽  
Vol 11 (8) ◽  
pp. 783-802 ◽  
Author(s):  
Jiantao Liu ◽  
Quanlong Feng ◽  
Jianhua Gong ◽  
Jieping Zhou ◽  
Jianming Liang ◽  
...  
Keyword(s):  
Random Forest ◽  
Winter Wheat ◽  
Random Forest Classifier ◽  
Sensor Data ◽  
Multi Temporal

Object-based feature selection for crop classification using multi-temporal high-resolution imagery

2018 ◽  
Vol 40 (5-6) ◽  
pp. 2053-2068 ◽  
Author(s):  
Qian Song ◽  
Mingtao Xiang ◽  
Ciara Hovis ◽  
Qingbo Zhou ◽  
Miao Lu ◽  
...  
Keyword(s):  
Feature Selection ◽  
High Resolution ◽  
High Resolution Imagery ◽  
Object Based ◽  
Multi Temporal ◽  
Selection For ◽  
Crop Classification

scholarly journals Crop Classification Method Based on Optimal Feature Selection and Hybrid CNN-RF Networks for Multi-Temporal Remote Sensing Imagery

2020 ◽  
Vol 12 (19) ◽  
pp. 3119
Author(s):  
Shuting Yang ◽  
Lingjia Gu ◽  
Xiaofeng Li ◽  
Tao Jiang ◽  
Ruizhi Ren
Keyword(s):  
Remote Sensing ◽  
Feature Selection ◽  
Random Forest ◽  
Remote Sensing Data ◽  
Optical Remote Sensing ◽  
Remote Sensing Images ◽  
Multi Temporal ◽  
Optimal Feature Selection ◽  
Crop Classification ◽  
Optimal Feature

Although efforts and progress have been made in crop classification using optical remote sensing images, it is still necessary to make full use of the high spatial, temporal, and spectral resolutions of remote sensing images. However, with the increasing volume of remote sensing data, a key emerging issue in the field of crop classification is how to find useful information from massive data to balance classification accuracy and processing time. To address this challenge, we developed a novel crop classification method, combining optimal feature selection (OFSM) with hybrid convolutional neural network-random forest (CNN-RF) networks for multi-temporal optical remote sensing images. This research used 234 features including spectral, segmentation, color, and texture features from three scenes of Sentinel-2 images to identify crop types in the Jilin province of northeast China. To effectively extract the effective features of remote sensing data with lower time requirements, the use of OFSM was proposed with the results compared with two traditional feature selection methods (TFSM): random forest feature importance selection (RF-FI) and random forest recursive feature elimination (RF-RFE). Although the time required for OFSM was 26.05 s, which was between RF-FI with 1.97 s and RF-RFE with 132.54 s, OFSM outperformed RF-FI and RF-RFE in terms of the overall accuracy (OA) of crop classification by 4% and 0.3%, respectively. On the basis of obtaining effective feature information, to further improve the accuracy of crop classification we designed two hybrid CNN-RF networks to leverage the advantages of one-dimensional convolution (Conv1D) and Visual Geometry Group (VGG) with random forest (RF), respectively. Based on the selected optimal features using OFSM, four networks were tested for comparison: Conv1D-RF, VGG-RF, Conv1D, and VGG. Conv1D-RF achieved the highest OA at 94.27% as compared with VGG-RF (93.23%), Conv1D (92.59%), and VGG (91.89%), indicating that the Conv1D-RF method with optimal feature input provides an effective and efficient method of time series representation for multi-temporal crop-type classification.

scholarly journals Monitoring Dynamics of Vegetation Cover with the Integration of OBIA and Random Forest Classifier Using Sentinel-2 Multitemporal Satellite Imagery

2022 ◽  
Vol 8 (2) ◽  
pp. 75-84
Author(s):  
Nurwita Mustika Sari ◽  
R. Rokhmatuloh ◽  
Masita Dwi Mandini Manessa
Keyword(s):  
Random Forest ◽  
Satellite Imagery ◽  
Vegetation Cover ◽  
Anthropogenic Activities ◽  
Research Area ◽  
Random Forest Classifier ◽  
Sustainable Environment ◽  
Multi Temporal ◽  
Sentinel 2 ◽  
Dynamics Of Vegetation

The existence of vegetation in an area has an important role to maintain the carrying capacity of the environment and create a comfortable environment as a place to live. In an effort to create a sustainable environment, there are various pressures on vegetation that cause a decrease in vegetation area. Economic activity, population growth and other anthropogenic activities trigger the dynamics of vegetation cover in an area that causes land cover changes from vegetation to non-vegetation. Majalengka Regency as one of the areas with intensive regional physical development in line with the operation of BIJB Kertajati and the Cipali toll road became the study area in this research. This study aims to monitor the dynamics of vegetation cover with the proposed method namely the integration of the OBIA and Random Forest classifier using multi temporal Sentinel-2 satellite imagery. The results show that there is a decrease in the area of vegetation in the research area as much as 4,329.6 hectares to non-vegetation areas in the period 2016-2020. The vegetation area in 2020 is 84,716.07 hectares and non-vegetation area is 35,708 hectares. Thus, there has been a decrease in the percentage of vegetation area from 73.94% in 2016 to 70.35% in 2020, meanwhile for non-vegetation areas there has been an increase from 26.06% in 2016 to 29.65% in 2020.

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