scholarly journals Tree Crown Delineation Algorithm Based on a Convolutional Neural Network

2020 ◽  
Vol 12 (8) ◽  
pp. 1288 ◽  
Author(s):  
José R. G. Braga ◽  
Vinícius Peripato ◽  
Ricardo Dalagnol ◽  
Matheus P. Ferreira ◽  
Yuliya Tarabalka ◽  
...  
Keyword(s):  
Neural Network ◽  
High Resolution ◽  
Convolutional Neural Network ◽  
Tropical Forests ◽  
Large Scale ◽  
Satellite Images ◽  
Tree Level ◽  
Tree Crown ◽  
Individual Tree ◽  
High Resolution Satellite Images

Tropical forests concentrate the largest diversity of species on the planet and play a key role in maintaining environmental processes. Due to the importance of those forests, there is growing interest in mapping their components and getting information at an individual tree level to conduct reliable satellite-based forest inventory for biomass and species distribution qualification. Individual tree crown information could be manually gathered from high resolution satellite images; however, to achieve this task at large-scale, an algorithm to identify and delineate each tree crown individually, with high accuracy, is a prerequisite. In this study, we propose the application of a convolutional neural network—Mask R-CNN algorithm—to perform the tree crown detection and delineation. The algorithm uses very high-resolution satellite images from tropical forests. The results obtained are promising—the R e c a l l , P r e c i s i o n , and F 1 score values obtained were were 0.81 , 0.91 , and 0.86 , respectively. In the study site, the total of tree crowns delineated was 59,062 . These results suggest that this algorithm can be used to assist the planning and conduction of forest inventories. As the algorithm is based on a Deep Learning approach, it can be systematically trained and used for other regions.

scholarly journals Large-Scale Oil Palm Tree Detection from High-Resolution Satellite Images Using Two-Stage Convolutional Neural Networks

2018 ◽  
Vol 11 (1) ◽  
pp. 11 ◽  
Author(s):  
Weijia Li ◽  
Runmin Dong ◽  
Haohuan Fu ◽  
and Le Yu
Keyword(s):  
Neural Network ◽  
High Resolution ◽  
Oil Palm ◽  
Large Scale ◽  
Satellite Images ◽  
Quantitative Detection ◽  
Support Vector ◽  
Palm Tree ◽  
Two Stage ◽  
High Resolution Satellite Images

Being an important economic crop that contributes 35% of the total consumption of vegetable oil, remote sensing-based quantitative detection of oil palm trees has long been a key research direction for both agriculture and environmental purposes. While existing methods already demonstrate satisfactory effectiveness for small regions, performing the detection for a large region with satisfactory accuracy is still challenging. In this study, we proposed a two-stage convolutional neural network (TS-CNN)-based oil palm detection method using high-resolution satellite images (i.e. Quickbird) in a large-scale study area of Malaysia. The TS-CNN consists of one CNN for land cover classification and one CNN for object classification. The two CNNs were trained and optimized independently based on 20,000 samples collected through human interpretation. For the large-scale oil palm detection for an area of 55 km2, we proposed an effective workflow that consists of an overlapping partitioning method for large-scale image division, a multi-scale sliding window method for oil palm coordinate prediction, and a minimum distance filter method for post-processing. Our proposed approach achieves a much higher average F1-score of 94.99% in our study area compared with existing oil palm detection methods (87.95%, 81.80%, 80.61%, and 78.35% for single-stage CNN, Support Vector Machine (SVM), Random Forest (RF), and Artificial Neural Network (ANN), respectively), and much fewer confusions with other vegetation and buildings in the whole image detection results.

scholarly journals Individual tree crown delineation in a highly diverse tropical forest using very high resolution satellite images

2018 ◽  
Vol 145 ◽  
pp. 362-377 ◽  
Author(s):  
Fabien Hubert Wagner ◽  
Matheus Pinheiro Ferreira ◽  
Alber Sanchez ◽  
Mayumi C.M. Hirye ◽  
Maciel Zortea ◽  
...  
Keyword(s):  
High Resolution ◽  
Tropical Forest ◽  
Satellite Images ◽  
Tree Crown ◽  
Individual Tree ◽  
High Resolution Satellite Images ◽  
Crown Delineation ◽  
Tree Crown Delineation ◽  
Very High

scholarly journals High Resolution Satellite Images for Instantaneous Shoreline Extraction Using New Enhancement Algorithms

Geosciences ◽  
2019 ◽  
Vol 9 (3) ◽  
pp. 123 ◽  
Author(s):  
Donatella Dominici ◽  
Sara Zollini ◽  
Maria Alicandro ◽  
Francesca Della Torre ◽  
Paolo Buscema ◽  
...  
Keyword(s):  
High Resolution ◽  
Large Scale ◽  
Satellite Images ◽  
Human Life ◽  
Essential Element ◽  
Action Planning ◽  
High Resolution Satellite Images ◽  
High Resolution Images ◽  
Territorial Government ◽  
Very High

Knowledge of a territory is an essential element in any future planning action and in appropriate territorial and environmental requalification action planning. The current large-scale availability of satellite data, thanks to very high resolution images, provides professional users in the environmental, urban planning, engineering, and territorial government sectors, in general, with large amounts of useful data with which to monitor the territory and cultural heritage. Italy is experiencing environmental emergencies, and coastal erosion is one of the greatest threats, not only to the Italian heritage and economy, but also to human life. The aim of this paper is to find a rapid way of identifying the instantaneous shoreline. This possibility could help government institutions such as regions, civil protection, etc., to analyze large areas of land quickly. The focus is on instantaneous shoreline extraction in Ortona (CH, Italy), without considering tides, using WorldView-2 satellite images (50-cm resolution in panchromatic and 2 m in multispectral). In particular, the main purpose of this paper is to compare commercial software and ACM filters to test their effectiveness.

scholarly journals Context-Enabled Extraction of Large-Scale Urban Functional Zones from Very-High-Resolution Images: A Multiscale Segmentation Approach

2019 ◽  
Vol 11 (16) ◽  
pp. 1902 ◽  
Author(s):  
Shouji Du ◽  
Shihong Du ◽  
Bo Liu ◽  
Xiuyuan Zhang
Keyword(s):  
High Resolution ◽  
Large Scale ◽  
Satellite Images ◽  
Nearest Neighbor ◽  
Context Information ◽  
Functional Zone ◽  
High Resolution Satellite Images ◽  
Segmentation Approach ◽  
Functional Zones ◽  
Very High

Urban functional-zone (UFZ) analysis has been widely used in many applications, including urban environment evaluation, and urban planning and management. How to extract UFZs’ spatial units which delineates UFZs’ boundaries is fundamental to urban applications, but it is still unresolved. In this study, an automatic, context-enabled multiscale image segmentation method is proposed for extracting spatial units of UFZs from very-high-resolution satellite images. First, a window independent context feature is calculated to measure context information in the form of geographic nearest-neighbor distance from a pixel to different image classes. Second, a scale-adaptive approach is proposed to determine appropriate scales for each UFZ in terms of its context information and generate the initial UFZs. Finally, the graph cuts algorithm is improved to optimize the initial UFZs. Two datasets including WorldView-2 image in Beijing and GaoFen-2 image in Nanchang are used to evaluate the proposed method. The results indicate that the proposed method can generate better results from very-high-resolution satellite images than widely used approaches like image tiles and road blocks in representing UFZs. In addition, the proposed method outperforms existing methods in both segmentation quality and running time. Therefore, the proposed method appears to be promising and practical for segmenting large-scale UFZs.

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