SAR Image Change Detection Based on Correlation Kernel and Multistage Extreme Learning Machine

Jia, Lu; Li, Ming; Zhang, Peng; Wu, Yan

doi:10.1109/tgrs.2016.2578438

Cited by 34 publications

(13 citation statements)

References 33 publications

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“…Besides, in order to carry out the quantitative evaluation, we compute five criterions [24][25][26][27] by comparing the resulting change detection map with the ground truth: 1) False alarms (FA); 2) Missed alarms (MA); 3) Overall errors (OE) ; 4) Overall accuracy (OA); 5) Kappa statistics (Kappa). Thereinto, FA denotes the number of unchanged pixels detected as changed pixels.…”

Section: Methodsmentioning

confidence: 99%

Change Detection in Bitemporal Remote Sensing Images by using Feature Fusion and Fuzzy C-Means

2018

KSII TIIS

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Change detection of remote sensing images is a profound challenge in the field of remote sensing image analysis. This paper proposes a novel change detection method for bitemporal remote sensing images based on feature fusion and fuzzy c-means (FCM). Different from the state-of-the-art methods that mainly utilize a single image feature for difference image construction, the proposed method investigates the fusion of multiple image features for the task. The subsequent problem is regarded as the difference image classification problem, where a modified fuzzy c-means approach is proposed to analyze the difference image. The proposed method has been validated on real bitemporal remote sensing data sets. Experimental results confirmed the effectiveness of the proposed method.

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Section: Methodsmentioning

confidence: 99%

Change Detection in Bitemporal Remote Sensing Images by using Feature Fusion and Fuzzy C-Means

2018

KSII TIIS

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“…The KELM method integrates kernel learning into ELM and extends the explicit activation function to an implicit mapping function, which avoids the randomly generated parameter issue and demonstrates the superior generalization capability. In diverse HSI learning models, KELM is widely used as the classifier to predict the ground covers for all pixels [13,34].…”

Section: Kernel Based Extreme Learning Machinementioning

confidence: 99%

“…In remote sensing applications, ELM is also well explored for various learning tasks. For synthetic aperture radar (SAR) image change detection, a unified framework is presented by integrating a difference correlation kernel (DCK) and a multistage ELM (MS-ELM), where any changes can be measured by the distance between pair-wise pixels [34]. For ship detection, the proposed model consists of compressed domain, a deep neural network (DNN) and an ELM, in which the ELM is employed to act as efficient feature pooling and decision making [35].…”

Section: Introductionmentioning

confidence: 99%

Spectral-Spatial Hyperspectral Image Classification with Superpixel Pattern and Extreme Learning Machine

et al. 2019

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Spectral-spatial classification of hyperspectral images (HSIs) has recently attracted great attention in the research domain of remote sensing. It is well-known that, in remote sensing applications, spectral features are the fundamental information and spatial patterns provide the complementary information. With both spectral features and spatial patterns, hyperspectral image (HSI) applications can be fully explored and the classification performance can be greatly improved. In reality, spatial patterns can be extracted to represent a line, a clustering of points or image texture, which denote the local or global spatial characteristic of HSIs. In this paper, we propose a spectral-spatial HSI classification model based on superpixel pattern (SP) and kernel based extreme learning machine (KELM), called SP-KELM, to identify the land covers of pixels in HSIs. In the proposed SP-KELM model, superpixel pattern features are extracted by an advanced principal component analysis (PCA), which is based on superpixel segmentation in HSIs and used to denote spatial information. The KELM method is then employed to be a classifier in the proposed spectral-spatial model with both the original spectral features and the extracted spatial pattern features. Experimental results on three publicly available HSI datasets verify the effectiveness of the proposed SP-KELM model, with the performance improvement of 10% over the spectral approaches.

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“…Image registration is an essential and fundamental task for remote sensing interpretation. It is aimed at registering images obtained from different sensors, different perspectives, different times or different imaging conditions [1], and is an essential preliminary task for image fusion [2], 3D modeling [3], and change detection [4]. With the rapid advance of remote sensing systems, more and more data sources can now be acquired.…”

Section: Introductionmentioning

confidence: 99%

Modality-Free Feature Detector and Descriptor for Multimodal Remote Sensing Image Registration

Cui

et al. 2020

Remote Sensing

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The nonlinear radiation distortions (NRD) among multimodal remote sensing images bring enormous challenges to image registration. The traditional feature-based registration methods commonly use the image intensity or gradient information to detect and describe the features that are sensitive to NRD. However, the nonlinear mapping of the corresponding features of the multimodal images often results in failure of the feature matching, as well as the image registration. In this paper, a modality-free multimodal remote sensing image registration method (SRIFT) is proposed for the registration of multimodal remote sensing images, which is invariant to scale, radiation, and rotation. In SRIFT, the nonlinear diffusion scale (NDS) space is first established to construct a multi-scale space. A local orientation and scale phase congruency (LOSPC) algorithm are then used so that the features of the images with NRD are mapped to establish a one-to-one correspondence, to obtain sufficiently stable key points. In the feature description stage, a rotation-invariant coordinate (RIC) system is adopted to build a descriptor, without requiring estimation of the main direction. The experiments undertaken in this study included one set of simulated data experiments and nine groups of experiments with different types of real multimodal remote sensing images with rotation and scale differences (including synthetic aperture radar (SAR)/optical, digital surface model (DSM)/optical, light detection and ranging (LiDAR) intensity/optical, near-infrared (NIR)/optical, short-wave infrared (SWIR)/optical, classification/optical, and map/optical image pairs), to test the proposed algorithm from both quantitative and qualitative aspects. The experimental results showed that the proposed method has strong robustness to NRD, being invariant to scale, radiation, and rotation, and the achieved registration precision was better than that of the state-of-the-art methods.

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SAR Image Change Detection Based on Correlation Kernel and Multistage Extreme Learning Machine

Cited by 34 publications

References 33 publications

Change Detection in Bitemporal Remote Sensing Images by using Feature Fusion and Fuzzy C-Means

Change Detection in Bitemporal Remote Sensing Images by using Feature Fusion and Fuzzy C-Means

Spectral-Spatial Hyperspectral Image Classification with Superpixel Pattern and Extreme Learning Machine

Modality-Free Feature Detector and Descriptor for Multimodal Remote Sensing Image Registration

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