Algorithm for Automatic Island Segmentation and Identification Based on Narrow Band Deformable Model in UAV Optical Remote Sensing Image

The island survey is important in economic and strategic field, and in recent years the use of remote sensing technology becomes the mainstream in island investigation. As an effective way for improving the efficiency and accuracy of island survey, the automatic segmentation and recognition algorithm has greater significance. For the difficulty in application of deformed model to high-resolution remote sensing images, the segmentation framework of global initial segmentation and local extractive segmentation based on narrow band deformable model is proposed. Based on the sea and land extraction the island initial segmentation is accomplished, and then the narrow band deformable model is used to increase the accuracy of segmentation. Finally the double rings feature of island is used to improve the quality of the segmentation.

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Cloud Detection in Landsat5 Images Based on Template Gradient

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.271-273.205 ◽

2011 ◽

Vol 271-273 ◽

pp. 205-210

Author(s):

Ying Zhao Ma ◽

Wei Li Jiao ◽

Wang Wei

Keyword(s):

Remote Sensing ◽

Data Transmission ◽

Cloud Cover ◽

Remote Sensing Image ◽

Data Rate ◽

Optical Remote Sensing ◽

Cloud Detection ◽

Remote Sensing Images ◽

Optical Remote Sensing Image

Cloud is an important factor affect the quality of optical remote sensing image. How to automatically detect the cloud cover of an image, reduce of useless data transmission, make great significance of higher data rate usefulness. This paper represent a method based on Lansat5 data, which can automatically mark the location of clouds region in each image, and effective calculated for each cloud cover, remove useless remote sensing images.

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An Aircraft Object Detection Algorithm Based on Small Samples in Optical Remote Sensing Image

Applied Sciences ◽

10.3390/app10175778 ◽

2020 ◽

Vol 10 (17) ◽

pp. 5778

Author(s):

Ting Wang ◽

Changqing Cao ◽

Xiaodong Zeng ◽

Zhejun Feng ◽

Jingshi Shen ◽

...

Keyword(s):

Remote Sensing ◽

Object Detection ◽

Detection Algorithm ◽

Small Samples ◽

Optical Remote Sensing ◽

Remote Sensing Images ◽

Sensing Technology ◽

Optical Remote Sensing Image ◽

Aircraft Detection ◽

Small Aircraft

In recent years, remote sensing technology has developed rapidly, and the ground resolution of spaceborne optical remote sensing images has reached the sub-meter range, providing a new technical means for aircraft object detection. Research on aircraft object detection based on optical remote sensing images is of great significance for military object detection and recognition. However, spaceborne optical remote sensing images are difficult to obtain and costly. Therefore, this paper proposes the aircraft detection algorithm, itcan detect aircraft objects with small samples. Firstly, this paper establishes an aircraft object dataset containing weak and small aircraft objects. Secondly, the detection algorithm has been proposed to detect weak and small aircraft objects. Thirdly, the aircraft detection algorithm has been proposed to detect multiple aircraft objects of varying sizes. There are 13,324 aircraft in the test set. According to the method proposed in this paper, the f1 score can achieve 90.44%. Therefore, the aircraft objects can be detected simply and efficiently by using the method proposed. It can effectively detect aircraft objects and improve early warning capabilities.

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Combined Multi-Time Series SAR Imagery and InSAR Technology for Rice Identification in Cloudy Regions

Applied Sciences ◽

10.3390/app11156923 ◽

2021 ◽

Vol 11 (15) ◽

pp. 6923

Author(s):

Rui Zhang ◽

Zhanzhong Tang ◽

Dong Luo ◽

Hongxia Luo ◽

Shucheng You ◽

...

Keyword(s):

Remote Sensing ◽

Time Series ◽

Identification Accuracy ◽

Optical Remote Sensing ◽

Sar Image ◽

Single Time ◽

Growing Period ◽

Sensing Technology ◽

Sar Imagery ◽

Cloudy Region

The use of remote sensing technology to monitor farmland is currently the mainstream method for crop research. However, in cloudy and misty regions, the use of optical remote sensing image is limited. Synthetic aperture radar (SAR) technology has many advantages, including high resolution, multi-mode, and multi-polarization. Moreover, it can penetrate clouds and mists, can be used for all-weather and all-time Earth observation, and is sensitive to the shape of ground objects. Therefore, it is widely used in agricultural monitoring. In this study, the polarization backscattering coefficient on time-series SAR images during the rice-growing period was analyzed. The rice identification results and accuracy of InSAR technology were compared with those of three schemes (single-time-phase SAR, multi-time-phase SAR, and combination of multi-time-phase SAR and InSAR). Results show that VV and VH polarization coherence coefficients can well distinguish artificial buildings. In particular, VV polarization coherence coefficients can well distinguish rice from water and vegetation in August and September, whereas VH polarization coherence coefficients can well distinguish rice from water and vegetation in August and October. The rice identification accuracy of single-time series Sentinel-1 SAR image (78%) is lower than that of multi-time series SAR image combined with InSAR technology (81%). In this study, Guanghan City, a cloudy region, was used as the study site, and a good verification result was obtained.

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Mathematical models for information classification and recognition of multi-target optical remote sensing images

Open Physics ◽

10.1515/phys-2020-0123 ◽

2020 ◽

Vol 18 (1) ◽

pp. 951-960

Author(s):

Haiqing Zhang ◽

Jun Han

Keyword(s):

Remote Sensing ◽

Recognition Rate ◽

Three Dimensional ◽

Remote Sensing Image ◽

Optical Remote Sensing ◽

Specific Class ◽

Three Dimensional Model ◽

Image Information ◽

Information Classification ◽

Optical Remote Sensing Image

Abstract Traditionally, three-dimensional model is used to classify and recognize multi-target optical remote sensing image information, which can only identify a specific class of targets, and has certain limitations. A mathematical model of multi-target optical remote sensing image information classification and recognition is designed, and a local adaptive threshold segmentation algorithm is used to segment multi-target optical remote sensing image to reduce the gray level between images and improve the accuracy of feature extraction. Remote sensing image information is multi-feature, and multi-target optical remote sensing image information is identified by chaotic time series analysis method. The experimental results show that the proposed model can effectively classify and recognize multi-target optical remote sensing image information. The average recognition rate is more than 95%, the maximum robustness is 0.45, the recognition speed is 98%, and the maximum time-consuming average is only 14.30 s. It has high recognition rate, robustness, and recognition efficiency.

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