Algorithms and performance comparison of automatic thresholding segmentation for forest regions in remote sensing image

2011 ◽  
Vol 30 (12) ◽  
pp. 3269-3273
Author(s):  
Tian-yi CUI ◽  
Wen-ping LIU ◽  
Ning ZHANG
Keyword(s):  
Remote Sensing ◽  
Remote Sensing Image ◽  
Performance Comparison ◽  
Automatic Thresholding ◽  
Forest Regions ◽  
And Performance

scholarly journals MRA-SNet: Siamese Networks of Multiscale Residual and Attention for Change Detection in High-Resolution Remote Sensing Images

2021 ◽  
Vol 13 (22) ◽  
pp. 4528
Author(s):  
Xin Yang ◽  
Lei Hu ◽  
Yongmei Zhang ◽  
Yunqing Li
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Change Detection ◽  
Remote Sensing Image ◽  
Image Features ◽  
Remote Sensing Images ◽  
The Rich ◽  
The Difference ◽  
Basic Network ◽  
And Performance

Remote sensing image change detection (CD) is an important task in remote sensing image analysis and is essential for an accurate understanding of changes in the Earth’s surface. The technology of deep learning (DL) is becoming increasingly popular in solving CD tasks for remote sensing images. Most existing CD methods based on DL tend to use ordinary convolutional blocks to extract and compare remote sensing image features, which cannot fully extract the rich features of high-resolution (HR) remote sensing images. In addition, most of the existing methods lack robustness to pseudochange information processing. To overcome the above problems, in this article, we propose a new method, namely MRA-SNet, for CD in remote sensing images. Utilizing the UNet network as the basic network, the method uses the Siamese network to extract the features of bitemporal images in the encoder separately and perform the difference connection to better generate difference maps. Meanwhile, we replace the ordinary convolution blocks with Multi-Res blocks to extract spatial and spectral features of different scales in remote sensing images. Residual connections are used to extract additional detailed features. To better highlight the change region features and suppress the irrelevant region features, we introduced the Attention Gates module before the skip connection between the encoder and the decoder. Experimental results on a public dataset of remote sensing image CD show that our proposed method outperforms other state-of-the-art (SOTA) CD methods in terms of evaluation metrics and performance.

Research and Performance Analysis on Parallel Computing of Remote Sensing Image Based on MPICH

2013 ◽  
Vol 680 ◽  
pp. 540-545
Author(s):  
Jun Li ◽  
Wei Feng Ma
Keyword(s):  
Remote Sensing ◽  
Image Processing ◽  
Parallel Computing ◽  
Cluster Computing ◽  
Single Mode ◽  
Remote Sensing Image ◽  
Distributed Parallel Computing ◽  
And Performance ◽  
Occurrence Matrix ◽  
Remote Sensing Image Processing

The traditional centralized single mode becomes a “bottleneck” of remote sensing image processing which cannot meet the needs of future remote sensing image processing development. Fortunately, the distributed parallel computing has provided a turning point to the quick calculation of remote sensing image. This paper presents the cluster computing environment based on the MPI, and advances a project of a parallelized design to the gray level co-occurrence matrix algorithm. Moreover, the experimental data, which is due to the parallelized algorithm running in the cluster, is recorded and analyzed in several respects such as different nodes, time, speedup, efficiency and so on. The analyzed result shows that parallel computing cluster based on MPICH can efficiently improve the speed of remote sensing image processing in the case of more complex algorithms. However, when the number of node increases, the consuming time decreases, and the efficiency will decrease at the same time. So, it is rather important to keep the balance between performance and efficiency. The nodes can not be continuously added into computing, when the consuming time can be accepted.

scholarly journals Deep Learning-Based Semantic Segmentation of Urban Features in Satellite Images: A Review and Meta-Analysis

2021 ◽  
Vol 13 (4) ◽  
pp. 808 ◽  
Author(s):  
Bipul Neupane ◽  
Teerayut Horanont ◽  
Jagannath Aryal
Keyword(s):  
Remote Sensing ◽  
Deep Learning ◽  
Meta Analysis ◽  
Semantic Segmentation ◽  
Performance Comparison ◽  
Current Status ◽  
Remote Sensing Images ◽  
Preparation Methods ◽  
Augmentation Techniques ◽  
And Performance

Availability of very high-resolution remote sensing images and advancement of deep learning methods have shifted the paradigm of image classification from pixel-based and object-based methods to deep learning-based semantic segmentation. This shift demands a structured analysis and revision of the current status on the research domain of deep learning-based semantic segmentation. The focus of this paper is on urban remote sensing images. We review and perform a meta-analysis to juxtapose recent papers in terms of research problems, data source, data preparation methods including pre-processing and augmentation techniques, training details on architectures, backbones, frameworks, optimizers, loss functions and other hyper-parameters and performance comparison. Our detailed review and meta-analysis show that deep learning not only outperforms traditional methods in terms of accuracy, but also addresses several challenges previously faced. Further, we provide future directions of research in this domain.

Deep Learning Based High-Resolution Remote Sensing Image classification

2017 ◽  
Vol 7 (10) ◽  
pp. 22
Author(s):  
Sumit Kaur
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Deep Learning ◽  
Image Classification ◽  
Language Processing ◽  
Object Perception ◽  
Remote Sensing Image ◽  
Research Area ◽  
Remote Sensing Image Classification ◽  
Unsupervised Algorithms

Abstract- Deep learning is an emerging research area in machine learning and pattern recognition field which has been presented with the goal of drawing Machine Learning nearer to one of its unique objectives, Artificial Intelligence. It tries to mimic the human brain, which is capable of processing and learning from the complex input data and solving different kinds of complicated tasks well. Deep learning (DL) basically based on a set of supervised and unsupervised algorithms that attempt to model higher level abstractions in data and make it self-learning for hierarchical representation for classification. In the recent years, it has attracted much attention due to its state-of-the-art performance in diverse areas like object perception, speech recognition, computer vision, collaborative filtering and natural language processing. This paper will present a survey on different deep learning techniques for remote sensing image classification. 

Design and Performance Comparison of Planar and Linear Antenna Array Using ADS

2014 ◽  
Vol 3 (2) ◽  
pp. 22-26
Author(s):  
V. S. Prabhu ◽  
◽  
V. P. Surya Surendran ◽  
V. G. Veena ◽  
◽  
...  
Keyword(s):  
Antenna Array ◽  
Performance Comparison ◽  
Linear Antenna ◽  
Linear Antenna Array ◽  
And Performance

SIMULATION AND PERFORMANCE COMPARISON OF VARIABLE SPEED MULTI-PHASE PMSM MACHINES

2018 ◽  
Vol 9 (1) ◽  
pp. 7
Author(s):  
MOIN SIDDIQUI KHADIM ◽  
FATMA AMREEN ◽  
KHURSHEED SIDDIQUI MOHD ◽  
◽  
◽  
...  
Keyword(s):  
Performance Comparison ◽  
Variable Speed ◽  
And Performance ◽  
Multi Phase
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