scholarly journals Multiscale Region-Level VHR Image Change Detection via Sparse Change Descriptor and Robust Discriminative Dictionary Learning

2015 ◽  
Vol 2015 ◽  
pp. 1-15
Author(s):  
Yuan Xu ◽  
Kun Ding ◽  
Chunlei Huo ◽  
Zisha Zhong ◽  
Haichang Li ◽  
...  
Keyword(s):  
Change Detection ◽  
Dictionary Learning ◽  
Conditional Random Field ◽  
Contextual Information ◽  
Discriminative Ability ◽  
Change Pattern ◽  
Conditional Random Field Model ◽  
Pattern Description ◽  
Image Change Detection ◽  
Discriminative Dictionary Learning

Very high resolution (VHR) image change detection is challenging due to the low discriminative ability of change feature and the difficulty of change decision in utilizing the multilevel contextual information. Most change feature extraction techniques put emphasis on the change degree description (i.e., in what degree the changes have happened), while they ignore the change pattern description (i.e., how the changes changed), which is of equal importance in characterizing the change signatures. Moreover, the simultaneous consideration of the classification robust to the registration noise and the multiscale region-consistent fusion is often neglected in change decision. To overcome such drawbacks, in this paper, a novel VHR image change detection method is proposed based on sparse change descriptor and robust discriminative dictionary learning. Sparse change descriptor combines the change degree component and the change pattern component, which are encoded by the sparse representation error and the morphological profile feature, respectively. Robust change decision is conducted by multiscale region-consistent fusion, which is implemented by the superpixel-level cosparse representation with robust discriminative dictionary and the conditional random field model. Experimental results confirm the effectiveness of the proposed change detection technique.

scholarly journals Ship Detection for PolSAR Images via Task-Driven Discriminative Dictionary Learning

2019 ◽  
Vol 11 (7) ◽  
pp. 769 ◽  
Author(s):  
Huiping Lin ◽  
Hang Chen ◽  
Hongmiao Wang ◽  
Junjun Yin ◽  
Jian Yang
Keyword(s):  
Learning Strategies ◽  
Dictionary Learning ◽  
Binary Classification ◽  
Contextual Information ◽  
Polarimetric Synthetic Aperture Radar ◽  
Robust Detection ◽  
Ship Detection ◽  
Sparsity Constraints ◽  
Active Learning Strategies ◽  
Discriminative Dictionary Learning

Ship detection with polarimetric synthetic aperture radar (PolSAR) has received increasing attention for its wide usage in maritime applications. However, extracting discriminative features to implement ship detection is still a challenging problem. In this paper, we propose a novel ship detection method for PolSAR images via task-driven discriminative dictionary learning (TDDDL). An assumption that ship and clutter information are sparsely coded under two separate dictionaries is made. Contextual information is considered by imposing superpixel-level joint sparsity constraints. In order to amplify the discrimination of the ship and clutter, we impose incoherence constraints between the two sub-dictionaries in the objective of feature coding. The discriminative dictionary is trained jointly with a linear classifier in task-driven dictionary learning (TDDL) framework. Based on the learnt dictionary and classifier, we extract discriminative features by sparse coding, and obtain robust detection results through binary classification. Different from previous methods, our ship detection cue is obtained through active learning strategies rather than artificially designed rules, and thus, is more adaptive, effective and robust. Experiments performed on synthetic images and two RADARSAT-2 images demonstrate that our method outperforms other comparative methods. In addition, the proposed method yields better shape-preserving ability and lower computation cost.

Deformable Dictionary Learning for SAR Image Change Detection

2018 ◽  
Vol 56 (8) ◽  
pp. 4605-4617 ◽  
Author(s):  
Lin Li ◽  
Yongqiang Zhao ◽  
Jinjun Sun ◽  
Rustam Stolkin ◽  
Quan Pan ◽  
...  
Keyword(s):  
Change Detection ◽  
Dictionary Learning ◽  
Sar Image ◽  
Image Change Detection

SAR Image Change Detection Based on Hybrid Conditional Random Field

2015 ◽  
Vol 12 (4) ◽  
pp. 910-914 ◽  
Author(s):  
Hejing Li ◽  
Ming Li ◽  
Peng Zhang ◽  
Wanying Song ◽  
Lin An ◽  
...  
Keyword(s):  
Change Detection ◽  
Random Field ◽  
Conditional Random Field ◽  
Sar Image ◽  
Image Change Detection

scholarly journals Semantic segmentation of satellite images of airports using convolutional neural networks

2020 ◽  
Vol 44 (4) ◽  
pp. 636-645
Author(s):  
V.A. Gorbachev ◽  
I.A. Krivorotov ◽  
A.O. Markelov ◽  
E.V. Kotlyarova
Keyword(s):  
Neural Networks ◽  
Satellite Images ◽  
Field Model ◽  
Conditional Random Field ◽  
Contextual Information ◽  
Semantic Segmentation ◽  
Data Preparation ◽  
Network Training ◽  
Wide Range ◽  
Conditional Random Field Model

The paper is devoted to the development of an effective semantic segmentation algorithm for automation of airport infrastructure labelling in RGB satellite images. This task is addressed using algorithms based on deep convolutional artificial neural networks, as they have proven themselves in a wide range of tasks, including the terrestrial imagery segmentation, where they show consistently high results. A new dataset was labelled for this particular task and a comparative analysis of different architectures and backbones was carried out. A conditional random field model (CRF) was used for postprocessing and accounting of contextual information and neighborhood of objects of different classes in order to eliminate outliers. Features of the solutions applied at all preparatory stages of the algorithm were described, including data preparation, neural network training and post-processing of the training results.

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