scholarly journals A Comparative Approach of Fuzzy Object Based Image Analysis and Machine Learning Techniques Which Are Applied to Crop Residue Cover Mapping by Using Sentinel-2 Satellite and UAV Imagery

2021 ◽  
Vol 13 (5) ◽  
pp. 937
Author(s):  
Payam Najafi ◽  
Bakhtiar Feizizadeh ◽  
Hossein Navid
Keyword(s):  
Machine Learning ◽  
Image Analysis ◽  
Crop Residue ◽  
Soil Surface ◽  
Machine Learning Algorithms ◽  
Object Based Image Analysis ◽  
Object Based ◽  
Tillage Methods ◽  
Sentinel 2

Conservation tillage methods through leaving the crop residue cover (CRC) on the soil surface protect it from water and wind erosions. Hence, the percentage of the CRC on the soil surface is very critical for the evaluation of tillage intensity. The objective of this study was to develop a new methodology based on the semiautomated fuzzy object based image analysis (fuzzy OBIA) and compare its efficiency with two machine learning algorithms which include: support vector machine (SVM) and artificial neural network (ANN) for the evaluation of the previous CRC and tillage intensity. We also considered the spectral images from two remotely sensed platforms of the unmanned aerial vehicle (UAV) and Sentinel-2 satellite, respectively. The results indicated that fuzzy OBIA for multispectral Sentinel-2 image based on Gaussian membership function with overall accuracy and Cohen’s kappa of 0.920 and 0.874, respectively, surpassed machine learning algorithms and represented the useful results for the classification of tillage intensity. The results also indicated that overall accuracy and Cohen’s kappa for the classification of RGB images from the UAV using fuzzy OBIA method were 0.860 and 0.779, respectively. The semiautomated fuzzy OBIA clearly outperformed machine learning approaches in estimating the CRC and the classification of the tillage methods and also it has the potential to substitute or complement field techniques.

scholarly journals Evaluation of Different Machine Learning Algorithms for Scalable Classification of Tree Types and Tree Species Based on Sentinel-2 Data

2018 ◽  
Vol 10 (9) ◽  
pp. 1419 ◽  
Author(s):  
Mathias Wessel ◽  
Melanie Brandmeier ◽  
Dirk Tiede
Keyword(s):  
Machine Learning ◽  
Tree Species ◽  
Confusion Matrix ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Inventory Data ◽  
Oak Trees ◽  
Object Based ◽  
Sentinel 2

We use freely available Sentinel-2 data and forest inventory data to evaluate the potential of different machine-learning approaches to classify tree species in two forest regions in Bavaria, Germany. Atmospheric correction was applied to the level 1C data, resulting in true surface reflectance or bottom of atmosphere (BOA) output. We developed a semiautomatic workflow for the classification of deciduous (mainly spruce trees), beech and oak trees by evaluating different classification algorithms (object- and pixel-based) in an architecture optimized for distributed processing. A hierarchical approach was used to evaluate different band combinations and algorithms (Support Vector Machines (SVM) and Random Forest (RF)) for the separation of broad-leaved vs. coniferous trees. The Ebersberger forest was the main project region and the Freisinger forest was used in a transferability study. Accuracy assessment and training of the algorithms was based on inventory data, validation was conducted using an independent dataset. A confusion matrix, with User´s and Producer´s Accuracies, as well as Overall Accuracies, was created for all analyses. In total, we tested 16 different classification setups for coniferous vs. broad-leaved trees, achieving the best performance of 97% for an object-based multitemporal SVM approach using only band 8 from three scenes (May, August and September). For the separation of beech and oak trees we evaluated 54 different setups, the best result achieved an accuracy of 91% for an object-based, SVM, multitemporal approach using bands 8, 2 and 3 of the May scene for segmentation and all principal components of the August scene for classification. The transferability of the model was tested for the Freisinger forest and showed similar results. This project points out that Sentinel-2 had only marginally worse results than comparable commercial high-resolution satellite sensors and is well-suited for forest analysis on a tree-stand level.

scholarly journals AUTOMATED UPDATING OF FOREST COVER MAPS FROM CLOUD-FREE SENTINEL-2 MOSAIC IMAGES USING OBJECT-BASED IMAGE ANALYSIS AND MACHINE LEARNING METHODS

2020 ◽  
Vol V-3-2020 ◽  
pp. 803-807
Author(s):  
M. Debella-Gilo ◽  
B. T. Borchsenius ◽  
K. Bjørkelo ◽  
J. Breidenbach
Keyword(s):  
Machine Learning ◽  
Image Analysis ◽  
Classification Accuracy ◽  
Forest Cover ◽  
Land Resource ◽  
Object Based Image Analysis ◽  
Machine Learning Methods ◽  
Object Based ◽  
Mosaic Images ◽  
Sentinel 2

Abstract. Planning sustainable use of land resources and environmental monitoring benefit from accurate and detailed forest information. The basis of accurate forest information is data on the spatial extent of forests. In Norway land resource maps have been carefully created by field visits and aerial image interpretation for over four decades with periodic updating. However, due to prioritization of agricultural and built-up areas, and high requirements with respect to the map accuracy, forest areas and outfields have not been frequently updated. Consequently, in some part of the country, the map has not been updated since its first creation in the 1960s. The Sentinel-2 satellite acquires images with high spatial and temporal resolution which provides opportunities for creating cloud-free mosaic images over areas that are often covered with clouds. Here, we combine object-based image analysis with machine learning methods in an automated framework to map forest area in Sentinel-2 mosaic images. The images are segmented using the eCogntion™ software. Training data are collected automatically from the existing land resource map and filtered using height and greenness information so that the training samples certainly represent their respective classes. Two machine learning algorithms, namely Random Forest (RF) and the Multilayer Perceptron Neural Network (MLP), are then trained and validated before mapping forest area. The effects of including and excluding some features on the classification accuracy is investigated. The results show that the method produces forest cover map at very high accuracy (up to 97%). The MLP performs better than the RF algorithm both in classification accuracy and in robustness against inclusion and exclusion of features.

scholarly journals Delineating Smallholder Maize Farms from Sentinel-1 Coupled with Sentinel-2 Data Using Machine Learning

2021 ◽  
Vol 13 (9) ◽  
pp. 4728
Author(s):  
Zinhle Mashaba-Munghemezulu ◽  
George Johannes Chirima ◽  
Cilence Munghemezulu
Keyword(s):  
Machine Learning ◽  
Food Security ◽  
Rural Communities ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Subsistence Agriculture ◽  
Smallholder Farms ◽  
Main Driver ◽  
Sentinel 2

Rural communities rely on smallholder maize farms for subsistence agriculture, the main driver of local economic activity and food security. However, their planted area estimates are unknown in most developing countries. This study explores the use of Sentinel-1 and Sentinel-2 data to map smallholder maize farms. The random forest (RF), support vector (SVM) machine learning algorithms and model stacking (ST) were applied. Results show that the classification of combined Sentinel-1 and Sentinel-2 data improved the RF, SVM and ST algorithms by 24.2%, 8.7%, and 9.1%, respectively, compared to the classification of Sentinel-1 data individually. Similarities in the estimated areas (7001.35 ± 1.2 ha for RF, 7926.03 ± 0.7 ha for SVM and 7099.59 ± 0.8 ha for ST) show that machine learning can estimate smallholder maize areas with high accuracies. The study concludes that the single-date Sentinel-1 data were insufficient to map smallholder maize farms. However, single-date Sentinel-1 combined with Sentinel-2 data were sufficient in mapping smallholder farms. These results can be used to support the generation and validation of national crop statistics, thus contributing to food security.

scholarly journals GEOBIA 2016: Advances in Object-Based Image Analysis—Linking with Computer Vision and Machine Learning

2019 ◽  
Vol 11 (10) ◽  
pp. 1181 ◽  
Author(s):  
Norman Kerle ◽  
Markus Gerke ◽  
Sébastien Lefèvre
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Image Analysis ◽  
The Netherlands ◽  
Object Based Image Analysis ◽  
Biennial Conference ◽  
Object Based ◽  
The University

The 6th biennial conference on object-based image analysis—GEOBIA 2016—took place in September 2016 at the University of Twente in Enschede, The Netherlands (see www [...]

scholarly journals The Mapping of Land Use Using Object-Based Image Analysis (OBIA) in Klaten Regency

2020 ◽  
Vol 202 ◽  
pp. 06036
Author(s):  
Nurhadi Bashit ◽  
Novia Sari Ristianti ◽  
Yudi Eko Windarto ◽  
Desyta Ulfiana
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Image Analysis ◽  
Satellite Imagery ◽  
Agricultural Land ◽  
Scale Parameter ◽  
Land Use Classification ◽  
Object Based Image Analysis ◽  
Object Based ◽  
Sentinel 2

Klaten Regency is one of the regencies in Central Java Province that has an increasing population every year. This can cause an increase in built-up land for human activities. The built-up land needs to be monitored so that the construction is in accordance with the regional development plan so that it does not cause problems such as the occurrence of critical land. Therefore, it is necessary to monitor land use regularly. One method for monitoring land use is the remote sensing method. The remote sensing method is much more efficient in mapping land use because without having to survey the field. The remote sensing method utilizes satellite imagery data that can be processed for land use classification. This study uses the sentinel 2 satellite image data with the Object-Based Image Analysis (OBIA) algorithm to obtain land use classification. Sentinel 2 satellite imagery is a medium resolution image category with a spatial resolution of 10 meters. The land use classification can be used to see the distribution of built-up land in Klaten Regency without having to conduct a field survey. The results of the study obtained a segmentation scale parameter value of 60 and a merge scale parameter value of 85. The classification results obtained by 5 types of land use with OBIA. Agricultural land use dominates with an area of 50% of the total area.

scholarly journals Fuzzy Object-Based Image Analysis Methods Using Sentinel-2A and Landsat-8 Data to Map and Characterize Soil Surface Residue

2019 ◽  
Vol 11 (21) ◽  
pp. 2583 ◽  
Author(s):  
Payam Najafi ◽  
Hossein Navid ◽  
Bakhtiar Feizizadeh ◽  
Iraj Eskandari ◽  
Thomas Blaschke
Keyword(s):  
Image Analysis ◽  
Agricultural Research ◽  
Soil Surface ◽  
Control Measures ◽  
Landsat 8 ◽  
Surface Residue ◽  
Ground Truth Data ◽  
Object Based Image Analysis ◽  
Object Based ◽  
Sentinel 2A

Soil degradation, defined as the lowering and loss of soil functions, is becoming a serious problem worldwide and threatens agricultural production and terrestrial ecosystems. The surface residue of crops is one of the most effective erosion control measures and it increases the soil moisture content. In some areas of the world, the management of soil surface residue (SSR) is crucial for increasing soil fertility, maintaining high soil carbon levels, and reducing the degradation of soil due to rain and wind erosion. Standard methods of measuring the residue cover are time and labor intensive, but remote sensing can support the monitoring of conservation tillage practices applied to large fields. We investigated the potential of per-pixel and object-based image analysis (OBIA) for detecting and estimating the coverage of SSRs after tillage and planting practices for agricultural research fields in Iran using tillage indices for Landsat-8 and novel indices for Sentinel-2A. For validation, SSR was measured in the field through line transects at the beginning of the agricultural season (prior to autumn crop planting). Per-pixel approaches for Landsat-8 satellite images using normalized difference tillage index (NDTI) and simple tillage index (STI) yielded coefficient of determination (R2) values of 0.727 and 0.722, respectively. We developed comparable novel indices for Sentinel-2A satellite data that yielded R2 values of 0.760 and 0.759 for NDTI and STI, respectively, which means that the Sentinel data better matched the ground truth data. We tested several OBIA methods and achieved very high overall accuracies of up to 0.948 for Sentinel-2A and 0.891 for Landsat-8 with a membership function method. The OBIA methods clearly outperformed per-pixel approaches in estimating SSR and bear the potential to substitute or complement ground-based techniques.

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