Concurrent Self-Organizing Maps for Supervised/Unsupervised Change Detection in Remote Sensing Images

Neagoe, Victor-Emil; Stoica, Radu-Mihai; Ciurea, Alexandru-Ioan; Bruzzone, Lorenzo; Bovolo, Francesca

doi:10.1109/jstars.2014.2330808

Cited by 33 publications

(13 citation statements)

References 18 publications

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“…Training of each of the Self-Organizing Map modules SOM (k) (k=1,…, M). One uses the SOM unsupervised algorithm [11], [12], [13] to train each of the SOM modules. Namely, each SOM module has been trained only with the samples having the same label with the neural module label.…”

Section: Support Vector Machine (Svm) Classifier Using An Improved Trmentioning

confidence: 99%

“…The area of Automatic Target Recognition (ATR) for SAR imagery is an ongoing research in many branches of the military and large research institutions [6], [7], [16], [17], [20]. On the other side, there has been an increasing interest in using artificial neural networks (ANN) for image processing and pattern recognition [1], [2], [7], [11], [12], [13], [14], [15], [17]. A typical target recognition system consists of a detection module (filtering and segmentation) and a recognition module (feature selection and classification) [1].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An Advanced Neural Network-Based Approach for Military Ground Vehicle Recognition in Sar Aerial Imagery

Neagoe¹,

Carata²,

Ciotec³

2016

AFASES 2016

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The paper presents a novel neural network approach for automatic target recognition (ATR) in the synthetic aperture radar (SAR) aerial imagery; this is applied to identify military ground vehicles. The proposed ATR algorithm consists of a processing cascade with the following stages: (a) object detection using a pulse-coupled neural network (PCNN) segmentation module; (b) a first feature selection module using Gabor filtering (GF); (c) a second feature selection module using principal component analysis (PCA); (d) a support vector machine (SVM) classifier improved by using virtual training data generation (VTDG) with concurrent self-organization maps (CSOM). The proposed model has been applied for the recognition of three classes of military ground vehicles of the former Soviet

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Section: Support Vector Machine (Svm) Classifier Using An Improved Trmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Advanced Neural Network-Based Approach for Military Ground Vehicle Recognition in Sar Aerial Imagery

Neagoe¹,

Carata²,

Ciotec³

2016

AFASES 2016

Self Cite

View full text Add to dashboard Cite

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“…Numerous machine learning algorithms have been used in CD applications, such as SVM [33][34][35], neural networks [36,37], and decision trees [38][39][40]. Recently, with the development of machine learning techniques, deep learning has attracted increasing attention due to its ability of mining the latent features and representations from the raw data [41].…”

Section: Introductionmentioning

confidence: 99%

Land Cover Change Detection from High-Resolution Remote Sensing Imagery Using Multitemporal Deep Feature Collaborative Learning and a Semi-supervised Chan–Vese Model

Zhang

Shi

Lv³

et al. 2019

Remote Sensing

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This paper presents a novel approach for automatically detecting land cover changes from multitemporal high-resolution remote sensing images in the deep feature space. This is accomplished by using multitemporal deep feature collaborative learning and a semi-supervised Chan–Vese (SCV) model. The multitemporal deep feature collaborative learning model is developed to obtain the multitemporal deep feature representations in the same high-level feature space and to improve the separability between changed and unchanged patterns. The deep difference feature map at the object-level is then extracted through a feature similarity measure. Based on the deep difference feature map, the SCV model is proposed to detect changes in which labeled patterns automatically derived from uncertainty analysis are integrated into the energy functional to efficiently drive the contour towards accurate boundaries of changed objects. The experimental results obtained on the four data sets acquired by different high-resolution sensors corroborate the effectiveness of the proposed approach.

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“…Change detection in remote sensing platform offers a detailed study about environmental issues; land use and land cover changes, impact of urbanization etc. [1]. Change detection of multi temporal images can be done using supervised or by unsupervised classification methods.…”

Section: Introductionmentioning

confidence: 99%

Semi-Supervised Change Detection Method Using Phase Congruency and Local Binary Pattern (PC-LBP)

Dalmiya

Santhi

Divya

2019

International Journal of Simulation: Systems, Science &Amp; Technology

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Several feature extraction methods are available for the application of change detection using remote sensing images. In this paper an efficient feature extraction technique is proposed by combining the phase congruency (PC) and local binary pattern (LBP) to extract the features in the images. The congruency amplitude and orientation is first determined for the two-multi temporal images and the LBP features are extracted. From the feature vectors of two images, neural networks (NN) classify the region into changed and unchanged. The deterministic method developed in this paper exploits improved accuracy in the change detection results. From the performance evaluation, it is noted that proposed method shows high accuracy and better kappa coefficient, which yields good change detection results.

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Concurrent Self-Organizing Maps for Supervised/Unsupervised Change Detection in Remote Sensing Images

Cited by 33 publications

References 18 publications

An Advanced Neural Network-Based Approach for Military Ground Vehicle Recognition in Sar Aerial Imagery

An Advanced Neural Network-Based Approach for Military Ground Vehicle Recognition in Sar Aerial Imagery

Land Cover Change Detection from High-Resolution Remote Sensing Imagery Using Multitemporal Deep Feature Collaborative Learning and a Semi-supervised Chan–Vese Model

Semi-Supervised Change Detection Method Using Phase Congruency and Local Binary Pattern (PC-LBP)

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