On-board optical image compression for future high-resolution remote sensing systems

2000 ◽  
Author(s):  
Catherine Lambert-Nebout ◽  
Christophe Latry ◽  
Gilles A. Moury ◽  
Christophe Parisot ◽  
Marc Antonini ◽  
...  
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Image Compression ◽  
Optical Image ◽  
Sensing Systems ◽  
Remote Sensing Systems

scholarly journals ATSS Deep Learning-Based Approach to Detect Apple Fruits

2020 ◽  
Vol 13 (1) ◽  
pp. 54
Author(s):  
Leonardo Josoé Biffi ◽  
Edson Mitishita ◽  
Veraldo Liesenberg ◽  
Anderson Aparecido dos Santos ◽  
Diogo Nunes Gonçalves ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
High Resolution ◽  
Correct Identification ◽  
Sensing Systems ◽  
Bounding Box ◽  
Smallholder Farm ◽  
Remote Sensing Systems ◽  
Rgb Images ◽  
Apple Fruits

In recent years, many agriculture-related problems have been evaluated with the integration of artificial intelligence techniques and remote sensing systems. Specifically, in fruit detection problems, several recent works were developed using Deep Learning (DL) methods applied in images acquired in different acquisition levels. However, the increasing use of anti-hail plastic net cover in commercial orchards highlights the importance of terrestrial remote sensing systems. Apples are one of the most highly-challenging fruits to be detected in images, mainly because of the target occlusion problem occurrence. Additionally, the introduction of high-density apple tree orchards makes the identification of single fruits a real challenge. To support farmers to detect apple fruits efficiently, this paper presents an approach based on the Adaptive Training Sample Selection (ATSS) deep learning method applied to close-range and low-cost terrestrial RGB images. The correct identification supports apple production forecasting and gives local producers a better idea of forthcoming management practices. The main advantage of the ATSS method is that only the center point of the objects is labeled, which is much more practicable and realistic than bounding-box annotations in heavily dense fruit orchards. Additionally, we evaluated other object detection methods such as RetinaNet, Libra Regions with Convolutional Neural Network (R-CNN), Cascade R-CNN, Faster R-CNN, Feature Selective Anchor-Free (FSAF), and High-Resolution Network (HRNet). The study area is a highly-dense apple orchard consisting of Fuji Suprema apple fruits (Malus domestica Borkh) located in a smallholder farm in the state of Santa Catarina (southern Brazil). A total of 398 terrestrial images were taken nearly perpendicularly in front of the trees by a professional camera, assuring both a good vertical coverage of the apple trees in terms of heights and overlapping between picture frames. After, the high-resolution RGB images were divided into several patches for helping the detection of small and/or occluded apples. A total of 3119, 840, and 2010 patches were used for training, validation, and testing, respectively. Moreover, the proposed method’s generalization capability was assessed by applying simulated image corruptions to the test set images with different severity levels, including noise, blurs, weather, and digital processing. Experiments were also conducted by varying the bounding box size (80, 100, 120, 140, 160, and 180 pixels) in the image original for the proposed approach. Our results showed that the ATSS-based method slightly outperformed all other deep learning methods, between 2.4% and 0.3%. Also, we verified that the best result was obtained with a bounding box size of 160 × 160 pixels. The proposed method was robust regarding most of the corruption, except for snow, frost, and fog weather conditions. Finally, a benchmark of the reported dataset is also generated and publicly available.

Comparative analysis of function of the optical spacecraft Earth's remote sensing based on the ratio between the mass of the satellite and the maximum spatial resolution of the target equipment

2017 ◽  
Vol 920 (2) ◽  
pp. 46-56
Author(s):  
O.S. Sizov
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Spatial Resolution ◽  
Regression Function ◽  
Statistical Sampling ◽  
Maximum Resolution ◽  
Sensing Systems ◽  
High Resolution Imagery ◽  
Remote Sensing Systems ◽  
Analysis Of Function

The paper analyzes the development of the technical capabilities of civil optical satellite ERS with a spatial resolution higher than 10 m/pix. On the basis of the ratio between the mass of the spacecraft and the maximum resolution of target equipment is offered hyperbolic regression function that describes the average specification for remote sensing systems with varying degrees of detail. The statistical sampling includes all the civil remote sensing satellites with high and ultra-high resolution, functioning on the orbit at the beginning of May 2016. The analysis on the effectiveness of missions completed their existence, as well as progressive devices and constellations, is conducted by using the obtained function. The data of existing and planned national remote sensing satellites are compared. Author made brief conclusions about the main trends of development of the world›s optical segment of high resolution imagery, as well as about the matching of the Russian group ERS evolution plans to observed trends.

Definitions of Attributes for Limb-Scanning or Limb-Imaging Remote Sensing Systems.

1995 ◽  
Author(s):  
S. D. Lockwood ◽  
D. Hardin ◽  
G. J. Miller ◽  
C. Meesuk ◽  
P. R. Straus
Keyword(s):  
Remote Sensing ◽  
Sensing Systems ◽  
Remote Sensing Systems
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