scholarly journals A deep analysis on high‐resolution dermoscopic image classification

2021 ◽  
Author(s):  
Federico Pollastri ◽  
Mario Parreño ◽  
Juan Maroñas ◽  
Federico Bolelli ◽  
Roberto Paredes ◽  
...  
Keyword(s):  
High Resolution ◽  
Image Classification ◽  
Dermoscopic Image

scholarly journals Dermoscopic Image Classification Using CNN with Handcrafted Features

2021 ◽  
pp. 101550
Author(s):  
K. Sankar Raja Sekhar ◽  
T. Ranga Babu ◽  
G.Prathibha ◽  
Vijay Kotra ◽  
Long Chiau Ming
Keyword(s):  
Image Classification ◽  
Dermoscopic Image

scholarly journals High-Resolution SAR Image Classification Using Multi-Scale Deep Feature Fusion and Covariance Pooling Manifold Network

2021 ◽  
Vol 13 (2) ◽  
pp. 328
Author(s):  
Wenkai Liang ◽  
Yan Wu ◽  
Ming Li ◽  
Yice Cao ◽  
Xin Hu
Keyword(s):  
High Resolution ◽  
Image Classification ◽  
Feature Fusion ◽  
Representation Learning ◽  
Sar Image ◽  
Gabor Filtering ◽  
Feature Maps ◽  
Sar Images ◽  
Multi Scale ◽  
Deep Feature

The classification of high-resolution (HR) synthetic aperture radar (SAR) images is of great importance for SAR scene interpretation and application. However, the presence of intricate spatial structural patterns and complex statistical nature makes SAR image classification a challenging task, especially in the case of limited labeled SAR data. This paper proposes a novel HR SAR image classification method, using a multi-scale deep feature fusion network and covariance pooling manifold network (MFFN-CPMN). MFFN-CPMN combines the advantages of local spatial features and global statistical properties and considers the multi-feature information fusion of SAR images in representation learning. First, we propose a Gabor-filtering-based multi-scale feature fusion network (MFFN) to capture the spatial pattern and get the discriminative features of SAR images. The MFFN belongs to a deep convolutional neural network (CNN). To make full use of a large amount of unlabeled data, the weights of each layer of MFFN are optimized by unsupervised denoising dual-sparse encoder. Moreover, the feature fusion strategy in MFFN can effectively exploit the complementary information between different levels and different scales. Second, we utilize a covariance pooling manifold network to extract further the global second-order statistics of SAR images over the fusional feature maps. Finally, the obtained covariance descriptor is more distinct for various land covers. Experimental results on four HR SAR images demonstrate the effectiveness of the proposed method and achieve promising results over other related algorithms.

High-Resolution SAR Image Classification via Deep Convolutional Autoencoders

2015 ◽  
Vol 12 (11) ◽  
pp. 2351-2355 ◽  
Author(s):  
Jie Geng ◽  
Jianchao Fan ◽  
Hongyu Wang ◽  
Xiaorui Ma ◽  
Baoming Li ◽  
...  
Keyword(s):  
High Resolution ◽  
Image Classification ◽  
Sar Image

Discriminant deep belief network for high-resolution SAR image classification

2017 ◽  
Vol 61 ◽  
pp. 686-701 ◽  
Author(s):  
Zhiqiang Zhao ◽  
Licheng Jiao ◽  
Jiaqi Zhao ◽  
Jing Gu ◽  
Jin Zhao
Keyword(s):  
High Resolution ◽  
Image Classification ◽  
Deep Belief Network ◽  
Sar Image ◽  
Belief Network
Sign in / Sign up

Export Citation Format

Share Document