677 research outputs found
An Accurate Vegetation and Non-Vegetation Differentiation Approach Based on Land Cover Classification
Accurate vegetation detection is important for many applications, such as crop yield estimation, landcover land use monitoring, urban growth monitoring, drought monitoring, etc. Popular conventional approaches to vegetation detection incorporate the normalized difference vegetation index (NDVI), which uses the red and near infrared (NIR) bands, and enhanced vegetation index (EVI), which uses red, NIR, and the blue bands. Although NDVI and EVI are efficient, their accuracies still have room for further improvement. In this paper, we propose a new approach to vegetation detection based on land cover classification. That is, we first perform an accurate classification of 15 or more land cover types. The land covers such as grass, shrub, and trees are then grouped into vegetation and other land cover types such as roads, buildings, etc. are grouped into non-vegetation. Similar to NDVI and EVI, only RGB and NIR bands are needed in our proposed approach. If Laser imaging, Detection, and Ranging (LiDAR) data are available, our approach can also incorporate LiDAR in the detection process. Results using a well-known dataset demonstrated that the proposed approach is feasible and achieves more accurate vegetation detection than both NDVI and EVI. In particular, a Support Vector Machine (SVM) approach performed 6% better than NDVI and 50% better than EVI in terms of overall accuracy (OA)
Automatic Model Based Dataset Generation for Fast and Accurate Crop and Weeds Detection
Selective weeding is one of the key challenges in the field of agriculture
robotics. To accomplish this task, a farm robot should be able to accurately
detect plants and to distinguish them between crop and weeds. Most of the
promising state-of-the-art approaches make use of appearance-based models
trained on large annotated datasets. Unfortunately, creating large agricultural
datasets with pixel-level annotations is an extremely time consuming task,
actually penalizing the usage of data-driven techniques. In this paper, we face
this problem by proposing a novel and effective approach that aims to
dramatically minimize the human intervention needed to train the detection and
classification algorithms. The idea is to procedurally generate large synthetic
training datasets randomizing the key features of the target environment (i.e.,
crop and weed species, type of soil, light conditions). More specifically, by
tuning these model parameters, and exploiting a few real-world textures, it is
possible to render a large amount of realistic views of an artificial
agricultural scenario with no effort. The generated data can be directly used
to train the model or to supplement real-world images. We validate the proposed
methodology by using as testbed a modern deep learning based image segmentation
architecture. We compare the classification results obtained using both real
and synthetic images as training data. The reported results confirm the
effectiveness and the potentiality of our approach.Comment: To appear in IEEE/RSJ IROS 201
A Comparative Analysis of Phytovolume Estimation Methods Based on UAV-Photogrammetry and Multispectral Imagery in a Mediterranean Forest
Management and control operations are crucial for preventing forest fires, especially in Mediterranean forest areas with dry climatic periods. One of them is prescribed fires, in which the biomass fuel present in the controlled plot area must be accurately estimated. The most used methods for estimating biomass are time-consuming and demand too much manpower. Unmanned aerial vehicles (UAVs) carrying multispectral sensors can be used to carry out accurate indirect measurements of terrain and vegetation morphology and their radiometric characteristics. Based on the UAV-photogrammetric project products, four estimators of phytovolume were compared in a Mediterranean forest area, all obtained using the difference between a digital surface model (DSM) and a digital terrain model (DTM). The DSM was derived from a UAV-photogrammetric project based on the structure from a motion algorithm. Four different methods for obtaining a DTM were used based on an unclassified dense point cloud produced through a UAV-photogrammetric project (FFU), an unsupervised classified dense point cloud (FFC), a multispectral vegetation index (FMI), and a cloth simulation filter (FCS). Qualitative and quantitative comparisons determined the ability of the phytovolume estimators for vegetation detection and occupied volume. The results show that there are no significant differences in surface vegetation detection between all the pairwise possible comparisons of the four estimators at a 95% confidence level, but FMI presented the best kappa value (0.678) in an error matrix analysis with reference data obtained from photointerpretation and supervised classification. Concerning the accuracy of phytovolume estimation, only FFU and FFC presented differences higher than two standard deviations in a pairwise comparison, and FMI presented the best RMSE (12.3 m) when the estimators were compared to 768 observed data points grouped in four 500 m2 sample plots. The FMI was the best phytovolume estimator of the four compared for low vegetation height in a Mediterranean forest. The use of FMI based on UAV data provides accurate phytovolume estimations that can be applied on several environment management activities, including wildfire prevention. Multitemporal phytovolume estimations based on FMI could help to model the forest resources evolution in a very realistic way
Vegetation Detection and Classification for Power Line Monitoring
Electrical network maintenance inspections must be regularly executed, to provide
a continuous distribution of electricity. In forested countries, the electrical network is
mostly located within the forest. For this reason, during these inspections, it is also
necessary to assure that vegetation growing close to the power line does not potentially
endanger it, provoking forest fires or power outages.
Several remote sensing techniques have been studied in the last years to replace the
labor-intensive and costly traditional approaches, be it field based or airborne surveillance.
Besides the previously mentioned disadvantages, these approaches are also prone to
error, since they are dependent of a human operator’s interpretation. In recent years,
Unmanned Aerial Vehicle (UAV) platform applicability for this purpose has been under
debate, due to its flexibility and potential for customisation, as well as the fact it can fly
close to the power lines.
The present study proposes a vegetation management and power line monitoring
method, using a UAV platform. This method starts with the collection of point cloud data
in a forest environment composed of power line structures and vegetation growing close
to it. Following this process, multiple steps are taken, including: detection of objects in
the working environment; classification of said objects into their respective class labels
using a feature-based classifier, either vegetation or power line structures; optimisation
of the classification results using point cloud filtering or segmentation algorithms. The
method is tested using both synthetic and real data of forested areas containing power line
structures. The Overall Accuracy of the classification process is about 87% and 97-99%
for synthetic and real data, respectively. After the optimisation process, these values were
refined to 92% for synthetic data and nearly 100% for real data. A detailed comparison
and discussion of results is presented, providing the most important evaluation metrics
and a visual representations of the attained results.Manutenções regulares da rede elétrica devem ser realizadas de forma a assegurar
uma distribuição contínua de eletricidade. Em países com elevada densidade florestal, a
rede elétrica encontra-se localizada maioritariamente no interior das florestas. Por isso,
durante estas inspeções, é necessário assegurar também que a vegetação próxima da rede
elétrica não a coloca em risco, provocando incêndios ou falhas elétricas.
Diversas técnicas de deteção remota foram estudadas nos últimos anos para substituir
as tradicionais abordagens dispendiosas com mão-de-obra intensiva, sejam elas através de
vigilância terrestre ou aérea. Além das desvantagens mencionadas anteriormente, estas
abordagens estão também sujeitas a erros, pois estão dependentes da interpretação de um
operador humano. Recentemente, a aplicabilidade de plataformas com Unmanned Aerial
Vehicles (UAV) tem sido debatida, devido à sua flexibilidade e potencial personalização,
assim como o facto de conseguirem voar mais próximas das linhas elétricas.
O presente estudo propõe um método para a gestão da vegetação e monitorização da
rede elétrica, utilizando uma plataforma UAV. Este método começa pela recolha de dados
point cloud num ambiente florestal composto por estruturas da rede elétrica e vegetação
em crescimento próximo da mesma. Em seguida,múltiplos passos são seguidos, incluindo:
deteção de objetos no ambiente; classificação destes objetos com as respetivas etiquetas
de classe através de um classificador baseado em features, vegetação ou estruturas da rede
elétrica; otimização dos resultados da classificação utilizando algoritmos de filtragem ou
segmentação de point cloud. Este método é testado usando dados sintéticos e reais de áreas
florestais com estruturas elétricas. A exatidão do processo de classificação é cerca de 87%
e 97-99% para os dados sintéticos e reais, respetivamente. Após o processo de otimização,
estes valores aumentam para 92% para os dados sintéticos e cerca de 100% para os dados
reais. Uma comparação e discussão de resultados é apresentada, fornecendo as métricas
de avaliação mais importantes e uma representação visual dos resultados obtidos
Real-time Semantic Segmentation of Crop and Weed for Precision Agriculture Robots Leveraging Background Knowledge in CNNs
Precision farming robots, which target to reduce the amount of herbicides
that need to be brought out in the fields, must have the ability to identify
crops and weeds in real time to trigger weeding actions. In this paper, we
address the problem of CNN-based semantic segmentation of crop fields
separating sugar beet plants, weeds, and background solely based on RGB data.
We propose a CNN that exploits existing vegetation indexes and provides a
classification in real time. Furthermore, it can be effectively re-trained to
so far unseen fields with a comparably small amount of training data. We
implemented and thoroughly evaluated our system on a real agricultural robot
operating in different fields in Germany and Switzerland. The results show that
our system generalizes well, can operate at around 20Hz, and is suitable for
online operation in the fields.Comment: Accepted for publication at IEEE International Conference on Robotics
and Automation 2018 (ICRA 2018
Vegetation Detection in Images
Tato práce se zabývá detekcí vegetace v obraze. Jsou zde popsány přístupy k detekci vegetace. Pro vytvoření aplikace byla vybrána metoda detekce trávy ve videu v reálném čase. V práci je navrhnut nový způsob vyhodnocení metody, aplikace realizuje původní vyhodnocení po pixelech i nový způsob vyhodnocení po segmentech. Funkce detekce je ověřena na testovací sadě. V závěru práce jsou porovnány výsledky obou přístupů vyhodnocení a popsány výsledky testování. Úspěšnost správně detekované vegetace se pohybuje až k 86,32 %.This project focuses on detection of vegetation in digital image and describes approaches to detect vegetation. Created aplication uses grass detection method in video in real time. There is a new mean of evaluation of the method proposed in this project, using commonly used pixel by pixel detection and also a new detection approach, segment detection. Functionality of the application is checked by set of test images. The thesis is concluded by comparing results of those two approaches. Success of correctly detected vegetation ranges up to 86.32 %.
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