Pattern Classification for Remote Sensing Images Using Back Propagation Network

2022 ◽  
pp. 271-280
Author(s):  
Telagarapu Prabhakar ◽  
S. Sri Durga Kameswari ◽  
A. Sudhakar
Keyword(s):  
Remote Sensing ◽  
Pattern Classification ◽  
Back Propagation ◽  
Remote Sensing Images ◽  
Propagation Network

scholarly journals Semantic Segmentation for Buildings of Large Intra-Class Variation in Remote Sensing Images with O-GAN

2021 ◽  
Vol 13 (3) ◽  
pp. 475
Author(s):  
Shuting Sun ◽  
Lin Mu ◽  
Lizhe Wang ◽  
Peng Liu ◽  
Xiaolei Liu ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Economic Status ◽  
Back Propagation ◽  
Tall Buildings ◽  
Semantic Segmentation ◽  
Generative Adversarial Networks ◽  
Building Extraction ◽  
Remote Sensing Images ◽  
Feature Vectors ◽  
Classification Feature

Remote sensing building extraction is of great importance to many applications, such as urban planning and economic status assessment. Deep learning with deep network structures and back-propagation optimization can automatically learn features of targets in high-resolution remote sensing images. However, it is also obvious that the generalizability of deep networks is almost entirely dependent on the quality and quantity of the labels. Therefore, building extraction performances will be greatly affected if there is a large intra-class variation among samples of one class target. To solve the problem, a subdivision method for reducing intra-class differences is proposed to enhance semantic segmentation. We proposed that backgrounds and targets be separately generated by two orthogonal generative adversarial networks (O-GAN). The two O-GANs are connected by adding the new loss function to their discriminators. To better extract building features, drawing on the idea of fine-grained image classification, feature vectors for a target are obtained through an intermediate convolution layer of O-GAN with selective convolutional descriptor aggregation (SCDA). Subsequently, feature vectors are clustered into new, different subdivisions to train semantic segmentation networks. In the prediction stages, the subdivisions will be merged into one class. Experiments were conducted with remote sensing images of the Tibet area, where there are both tall buildings and herdsmen’s tents. The results indicate that, compared with direct semantic segmentation, the proposed subdivision method can make an improvement on accuracy of about 4%. Besides, statistics and visualizing building features validated the rationality of features and subdivisions.

OPTIMAL OPERATION POINT IN 3D DCT- BASED LOSSY COMPRESSION OF COLOR AND MULTICHANNEL REMOTE SENSING IMAGES

2015 ◽  
Vol 74 (20) ◽  
pp. 1803-1821 ◽  
Author(s):  
V. V. Lukin ◽  
S. K. Abramov ◽  
R.A. Kozhemiakin ◽  
Benoit Vozel ◽  
B. Djurovic ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Lossy Compression ◽  
Optimal Operation ◽  
Remote Sensing Images

A Survey of Automatic Road Extraction from Remote Sensing Images

2010 ◽  
Vol 36 (7) ◽  
pp. 912-922 ◽  
Author(s):  
Liang WU ◽  
Yun-An HU
Keyword(s):  
Remote Sensing ◽  
Road Extraction ◽  
Remote Sensing Images
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