scholarly journals Finer Classification of Crops by Fusing UAV Images and Sentinel-2A Data

2019 ◽  
Vol 11 (24) ◽  
pp. 3012 ◽  
Author(s):  
Licheng Zhao ◽  
Yun Shi ◽  
Bin Liu ◽  
Ciara Hovis ◽  
Yulin Duan ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
Satellite Images ◽  
Optimal Choice ◽  
Fragmented Landscape ◽  
Agricultural Insurance ◽  
Cropping Patterns ◽  
Crop Mapping ◽  
Sentinel 2A ◽  
Uav Images ◽  
Uav Image

Accurate crop distribution maps provide important information for crop censuses, yield monitoring and agricultural insurance assessments. Most existing studies apply low spatial resolution satellite images for crop distribution mapping, even in areas with a fragmented landscape. Unmanned aerial vehicle (UAV) imagery provides an alternative imagery source for crop mapping, yet its spectral resolution is usually lower than satellite images. In order to produce more accurate maps without losing any spatial heterogeneity (e.g., the physical boundary of land parcel), this study fuses Sentinel-2A and UAV images to map crop distribution at a finer spatial scale (i.e., land parcel scale) in an experimental site with various cropping patterns in Heilongjiang Province, Northeast China. Using a random forest algorithm, the original, as well as the fused images, are classified into 10 categories: rice, corn, soybean, buckwheat, other vegetations, greenhouses, bare land, water, roads and houses. In addition, we test the effect of UAV image choice by fusing Sentinel-2A with different UAV images at multiples spatial resolutions: 0.03 m, 0.10 m, 0.50 m, 1.00 m and 3.00 m. Overall, the fused images achieved higher classification accuracies, ranging between 10.58% and 16.39%, than the original images. However, the fused image based on the finest UAV image (i.e., 0.03 m) does not result in the highest accuracy. Instead, the 0.10 m spatial resolution UAV image produced the most accurate map. When the spatial resolution is less than 0.10 m, accuracy decreases gradually as spatial resolution decreases. The results of this paper not only indicate the possibility of combining satellite images and UAV images for land parcel level crop mapping for fragmented landscapes, but it also implies a potential scheme to exploit optimal choice of spatial resolution in fusing UAV images and Sentinel-2A, with little to no adverse side-effects.

scholarly journals FSRSS-Net: High-Resolution Mapping of Buildings from Middle-Resolution Satellite Images Using a Super-Resolution Semantic Segmentation Network

2021 ◽  
Vol 13 (12) ◽  
pp. 2290
Author(s):  
Tao Zhang ◽  
Hong Tang ◽  
Yi Ding ◽  
Penglong Li ◽  
Chao Ji ◽  
...  
Keyword(s):  
High Resolution ◽  
Spatial Resolution ◽  
Satellite Images ◽  
Super Resolution ◽  
Semantic Segmentation ◽  
Image Features ◽  
Accuracy Evaluation ◽  
Building Recognition ◽  
Semantic Maps ◽  
Sentinel 2A

Satellite mapping of buildings and built-up areas used to be delineated from high spatial resolution (e.g., meters or sub-meters) and middle spatial resolution (e.g., tens of meters or hundreds of meters) satellite images, respectively. To the best of our knowledge, it is important to explore a deep-learning approach to delineate high-resolution semantic maps of buildings from middle-resolution satellite images. The approach is termed as super-resolution semantic segmentation in this paper. Specifically, we design a neural network with integrated low-level image features of super-resolution and high-level semantic features of super-resolution, which is trained with Sentinel-2A images (i.e., 10 m) and higher-resolution semantic maps (i.e., 2.5 m). The network, based on super-resolution semantic segmentation features is called FSRSS-Net. In China, the 35 cities are partitioned into three groups, i.e., 19 cities for model training, four cities for quantitative testing and the other 12 cities for qualitative generalization ability analysis of the learned networks. A large-scale sample dataset is created and utilized to train and validate the performance of the FSRSS-Net, which includes 8597 training samples and 766 quantitative accuracy evaluation samples. Quantitative evaluation results show that: (1) based on the 10 m Sentinel-2A image, the FSRSS-Net can achieve super-resolution semantic segmentation and produce 2.5 m building recognition results, and there is little difference between the accuracy of 2.5 m results by FSRSS-Net and 10 m results by U-Net. More importantly, the 2.5 m building recognition results by FSRSS-Net have higher accuracy than the 2.5 m results by U-Net 10 m building recognition results interpolation up-sampling; (2) from the spatial visualization of the results, the building recognition results of 2.5 m are more precise than those of 10 m, and the outline of the building is better depicted. Qualitative analysis shows that: (1) the learned FSRSS-Net can be also well generalized to other cities that are far from training regions; (2) the FSRSS-Net can still achieve comparable results to the U-Net 2 m building recognition results, even when the U-Net is directly trained using both 2-meter resolution GF2 satellite images and corresponding semantic labels.

scholarly journals Seeding Crop Detection Framework Using Prototypical Network Method in UAV Images

Agriculture ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 26
Author(s):  
Di Zhang ◽  
Feng Pan ◽  
Qi Diao ◽  
Xiaoxue Feng ◽  
Weixing Li ◽  
...  
Keyword(s):  
Aerial Images ◽  
Large Field ◽  
Small Scale ◽  
Agricultural Insurance ◽  
Aerial Vehicle ◽  
Simple Linear Iterative Clustering ◽  
Evaluation Dataset ◽  
Uav Images ◽  
Uav Image ◽  
Candidate Regions

With the development of unmanned aerial vehicle (UAV), obtaining high-resolution aerial images has become easier. Identifying and locating specific crops from aerial images is a valuable task. The location and quantity of crops are important for agricultural insurance businesses. In this paper, the problem of locating chili seedling crops in large-field UAV images is processed. Two problems are encountered in the location process: a small number of samples and objects in UAV images are similar on a small scale, which increases the location difficulty. A detection framework based on a prototypical network to detect crops in UAV aerial images is proposed. In particular, a method of subcategory slicing is applied to solve the problem, in which objects in aerial images have similarities at a smaller scale. The detection framework is divided into two parts: training and detection. In the training process, crop images are sliced into subcategories, and then these subcategory patch images and background category images are used to train the prototype network. In the detection process, a simple linear iterative clustering superpixel segmentation method is used to generate candidate regions in the UAV image. The location method uses a prototypical network to recognize nine patch images extracted simultaneously. To train and evaluate the proposed method, we construct an evaluation dataset by collecting the images of chilies in a seedling stage by an UAV. We achieve a location accuracy of 96.46%. This study proposes a seedling crop detection framework based on few-shot learning that does not require the use of labeled boxes. It reduces the workload of manual annotation and meets the location needs of seedling crops.

Mapping and Monitoring of the Land Use/Cover Changes in the Wider Area of ltanos, Crete, Using Very High Resolution EO Imagery With Specific Interest in Archaeological Sites

2019 ◽  
Author(s):  
Sawyer Reid stippa ◽  
George Petropoulos ◽  
Leonidas Toulios ◽  
Prashant K. Srivastava
Keyword(s):  
High Resolution ◽  
Spatial Resolution ◽  
Satellite Images ◽  
Archaeological Site ◽  
Digital Photography ◽  
Archaeological Sites ◽  
Large Area ◽  
High Resolution Imagery ◽  
Site Mapping ◽  
Rule Sets

Archaeological site mapping is important for both understanding the history as well as protecting them from excavation during the developmental activities. As archaeological sites generally spread over a large area, use of high spatial resolution remote sensing imagery is becoming increasingly applicable in the world. The main objective of this study was to map the land cover of the Itanos area of Crete and of its changes, with specific focus on the detection of the landscape’s archaeological features. Six satellite images were acquired from the Pleiades and WorldView-2 satellites over a period of 3 years. In addition, digital photography of two known archaeological sites was used for validation. An Object Based Image Analysis (OBIA) classification was subsequently developed using the five acquired satellite images. Two rule-sets were created, one using the standard four bands which both satellites have and another for the two WorldView-2 images their four extra bands included. Validation of the thematic maps produced from the classification scenarios confirmed a difference in accuracy amongst the five images. Comparing the results of a 4-band rule-set versus the 8-band showed a slight increase in classification accuracy using extra bands. The resultant classifications showed a good level of accuracy exceeding 70%. Yet, separating the archaeological sites from the open spaces with little or no vegetation proved challenging. This was mainly due to the high spectral similarity between rocks and the archaeological ruins. The satellite data spatial resolution allowed for the accuracy in defining larger archaeological sites, but still was a difficulty in distinguishing smaller areas of interest. The digital photography data provided a very good 3D representation for the archaeological sites, assisting as well in validating the satellite-derived classification maps. All in all, our study provided further evidence that use of high resolution imagery may allow for archaeological sites to be located, but only where they are of a suitable size archaeological features.

scholarly journals Comparing of Landsat 8 and Sentinel 2A using Water Extraction Indexes over Volta River

2017 ◽  
Vol 10 (1) ◽  
pp. 1 ◽  
Author(s):  
Clement Kwang ◽  
Edward Matthew Osei Jnr ◽  
Adwoa Sarpong Amoah
Keyword(s):  
Remote Sensing ◽  
Satellite Images ◽  
Remote Sensing Data ◽  
Unsupervised Classification ◽  
Water Bodies ◽  
Water Extraction ◽  
Landsat 8 ◽  
Sensing Data ◽  
Optimal Classifier ◽  
Sentinel 2A

Remote sensing data are most often used in water bodies’ extraction studies and the type of remote sensing data used also play a crucial role on the accuracy of the extracted water features. The performance of the proposed water indexes among the various satellite images is not well documented in literature. The proposed water indexes were initially developed with a particular type of data and with advancement and introduction of new satellite images especially Landsat 8 and Sentinel, therefore the need to test the level of performance of these water indexes as new image datasets emerged. Landsat 8 and Sentinel 2A image of part Volta River was used. The water indexes were performed and then ISODATA unsupervised classification was done. The overall accuracy and kappa coefficient values range from 98.0% to 99.8% and 0.94 to 0.98 respectively. Most of water bodies enhancement indexes work better on Sentinel 2A than on Landsat 8. Among the Landsat based water bodies enhancement ISODATA unsupervised classification, the modified normalized water difference index (MNDWI) and normalized water difference index (NDWI) were the best classifier while for Sentinel 2A, the MNDWI and the automatic water extraction index (AWEI_nsh) were the optimal classifier. The least performed classifier for both Landsat 8 and Sentinel 2A was the automatic water extraction index (AWEI_sh). The modified normalized water difference index (MNDWI) has proved to be the universal water bodies enhancement index because of its performance on both the Landsat 8 and Sentinel 2A image.

scholarly journals A Deep Learning Approach on Building Detection from Unmanned Aerial Vehicle-Based Images in Riverbank Monitoring

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3921 ◽  
Author(s):  
Wuttichai Boonpook ◽  
Yumin Tan ◽  
Yinghua Ye ◽  
Peerapong Torteeka ◽  
Kritanai Torsri ◽  
...  
Keyword(s):  
Deep Learning ◽  
Network Architecture ◽  
Semantic Segmentation ◽  
Water Levels ◽  
Building Detection ◽  
Network Training ◽  
Building Information ◽  
Open Standard ◽  
Uav Images ◽  
Uav Image

Buildings along riverbanks are likely to be affected by rising water levels, therefore the acquisition of accurate building information has great importance not only for riverbank environmental protection but also for dealing with emergency cases like flooding. UAV-based photographs are flexible and cloud-free compared to satellite images and can provide very high-resolution images up to centimeter level, while there exist great challenges in quickly and accurately detecting and extracting building from UAV images because there are usually too many details and distortions on UAV images. In this paper, a deep learning (DL)-based approach is proposed for more accurately extracting building information, in which the network architecture, SegNet, is used in the semantic segmentation after the network training on a completely labeled UAV image dataset covering multi-dimension urban settlement appearances along a riverbank area in Chongqing. The experiment results show that an excellent performance has been obtained in the detection of buildings from untrained locations with an average overall accuracy more than 90%. To verify the generality and advantage of the proposed method, the procedure is further evaluated by training and testing with another two open standard datasets which have a variety of building patterns and styles, and the final overall accuracies of building extraction are more than 93% and 95%, respectively.

scholarly journals A MINIMUM SPANNING TREE BASED METHOD FOR UAV IMAGE SEGMENTATION

2016 ◽  
Vol III-7 ◽  
pp. 111-117
Author(s):  
Ping Wang ◽  
Zheng Wei ◽  
Weihong Cui ◽  
Zhiyong Lin
Keyword(s):  
Image Segmentation ◽  
Coastal Area ◽  
Minimum Spanning Tree ◽  
Edge Weight ◽  
Segmentation Method ◽  
Optimal Criterion ◽  
The Rich ◽  
Uav Images ◽  
Uav Image ◽  
Scale Control

This paper proposes a Minimum Span Tree (MST) based image segmentation method for UAV images in coastal area. An edge weight based optimal criterion (merging predicate) is defined, which based on statistical learning theory (SLT). And we used a scale control parameter to control the segmentation scale. Experiments based on the high resolution UAV images in coastal area show that the proposed merging predicate can keep the integrity of the objects and prevent results from over segmentation. The segmentation results proves its efficiency in segmenting the rich texture images with good boundary of objects.

A Study on The Use of UAV Images to Improve the Separation Accuracy of Agricultural Land Areas

2021 ◽  
Author(s):  
Fahime Arabi Aliabad ◽  
Hamid Reza Ghafarian Malamiri ◽  
Saeed Shojaei
Keyword(s):  
Spatial Resolution ◽  
Agricultural Land ◽  
Principal Component ◽  
Landsat Image ◽  
Fuzzy Artmap ◽  
Landsat 8 ◽  
Pass Filter ◽  
Tree Methods ◽  
Uav Images ◽  
High Pass

Abstract Classifying satellite images with medium spatial resolution such as Landsat, it is usually difficult to distinguish between plant species, and it is impossible to determine the area covered with weeds. In this study, a Landsat 8 image along with UAV images was used to separate pistachio cultivars and separate weed from trees. In order to use the high spatial resolution of UAV images, image fusion was carried out through high-pass filter, wavelet, principal component transformation, BROVEY, IHS and Gram Schmidt methods, and ERGAS, RMSE and correlation criteria were applied to assess their accuracy. The results represented that the wavelet method with R2, RMSE and ERGAS 0.91, 12.22 cm and 2.05 respectively had the highest accuracy in combining these images. Then, images obtained by this method were chosen with a spatial resolution of 20 cm for classification. Different classification methods including unsupervised method, maximum likelihood, minimum distance, fuzzy artmap, perceptron and tree methods were evaluated. Moreover, six soil classes, Ahmad Aghaei, Akbari, Kalleh Ghoochi, Fandoghi and a mixing class of Kalleh Ghoochi and Fandoghi were applied and also three classes of soil, pistachio tree and weeds were extracted from the trees. The results demonstrated that the fuzzy artmap method had the highest accuracy in separating weeds from trees, differentiating various pistachio cultivars with Landsat image and also classification with combined image and had 0.87, 0.79 and 0.87 kappa coefficients respectively. The comparison between pistachio cultivars through Landsat image and combined image showed that the validation accuracy obtained from harvest has raised by 17% because of combination of images. The results of this study indicated that the combination of UAV and Landsat 8 images affects well to separate pistachio cultivars and determine the area covered with weeds.

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