The selection of optimum scale in coast area classification of high-resolution remote sensing imagery

2005 ◽  
Author(s):  
Jianyu Chen ◽  
Delu Pan ◽  
Zhihua Mao ◽  
Xiaoyu Zhang
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Remote Sensing Imagery ◽  
Optimum Scale ◽  
Area Classification ◽  
Selection Of

scholarly journals DETECTION OF HARBOURS FROM HIGH RESOLUTION REMOTE SENSING IMAGERY VIA SALIENCY ANALYSIS AND FEATURE LEARNING

2016 ◽  
Vol XLI-B7 ◽  
pp. 573-578
Author(s):  
Yetianjian Wang ◽  
Li Pan ◽  
Dagang Wang ◽  
Yifei Kang
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Feature Learning ◽  
Remote Sensing Imagery ◽  
Manual Selection ◽  
Minimum Bounding Rectangle ◽  
Hsv Colour Space ◽  
Saliency Analysis ◽  
Selection Of ◽  
Better Than

Harbours are very important objects in civil and military fields. To detect them from high resolution remote sensing imagery is important in various fields and also a challenging task. Traditional methods of detecting harbours mainly focus on the segmentation of water and land and the manual selection of knowledge. They do not make enough use of other features of remote sensing imagery and often fail to describe the harbours completely. In order to improve the detection, a new method is proposed. First, the image is transformed to Hue, Saturation, Value (HSV) colour space and saliency analysis is processed via the generation and enhancement of the co-occurrence histogram to help detect and locate the regions of interest (ROIs) that is salient and may be parts of the harbour. Next, SIFT features are extracted and feature learning is processed to help represent the ROIs. Then, by using classified feature of the harbour, a classifier is trained and used to check the ROIs to find whether they belong to the harbour. Finally, if the ROIs belong to the harbour, a minimum bounding rectangle is formed to include all the harbour ROIs and detect and locate the harbour. The experiment on high resolution remote sensing imagery shows that the proposed method performs better than other methods in precision of classifying ROIs and accuracy of completely detecting and locating harbours.

scholarly journals SEMANTIC SEGMENTATION OF CONVOLUTIONAL NEURAL NETWORK FOR SUPERVISED CLASSIFICATION OF MULTISPECTRAL REMOTE SENSING

2018 ◽  
Vol XLII-3 ◽  
pp. 2035-2039
Author(s):  
L. Xue ◽  
C. Liu ◽  
Y. Wu ◽  
H. Li
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
High Resolution ◽  
Network Architecture ◽  
Semantic Segmentation ◽  
Remote Sensing Imagery ◽  
The Neural Network ◽  
Ground Object ◽  
Remote Sensing Image Processing

Semantic segmentation is a fundamental research in remote sensing image processing. Because of the complex maritime environment, the classification of roads, vegetation, buildings and water from remote Sensing Imagery is a challenging task. Although the neural network has achieved excellent performance in semantic segmentation in the last years, there are a few of works using CNN for ground object segmentation and the results could be further improved. This paper used convolution neural network named U-Net, its structure has a contracting path and an expansive path to get high resolution output. In the network , We added BN layers, which is more conducive to the reverse pass. Moreover, after upsampling convolution , we add dropout layers to prevent overfitting. They are promoted to get more precise segmentation results. To verify this network architecture, we used a Kaggle dataset. Experimental results show that U-Net achieved good performance compared with other architectures, especially in high-resolution remote sensing imagery.

Classification of Individual Tree Species in High-Resolution Remote Sensing Imagery Based on Convolution Neural Network

2021 ◽  
Vol 58 (2) ◽  
pp. 0228002
Author(s):  
欧阳光 Ouyang Guang ◽  
荆林海 Jing Linhai ◽  
阎世杰 Yan Shijie ◽  
李慧 Li Hui ◽  
唐韵玮 Tang Yunwei ◽  
...  
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
High Resolution ◽  
Tree Species ◽  
Convolution Neural Network ◽  
Individual Tree ◽  
Remote Sensing Imagery
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