Final Difference Image Research Articles

SAR Image Change Detection Based on Multi-scale Feature Extraction

In order to improve the contrast of the difference image and reduce the interference of the speckle noise in the synthetic aperture radar (SAR) image, this paper proposes a SAR image change detection algorithm based on multi-scale feature extraction. In this paper, a kernel matrix with weights is used to extract features of two original images, and then the logarithmic ratio method is used to obtain the difference images of two images, and the change area of the images are extracted. Then, the different sizes of kernel matrix are used to extract the abstract features of different scales of the difference image. This operation can make the difference image have a higher contrast. Finally, the cumulative weighted average is obtained to obtain the final difference image, which can further suppress the speckle noise in the image.

Building change detection from multi-source remote sensing images based on multi-feature fusion and extreme learning machine

ABSTRACT In this paper, in order to improve the accuracy of multi-source remote sensing image building change detection, we propose a method based on multi-feature fusion (MFF) and extreme learning machine (ELM) training. Firstly, we fuse the spectral feature difference image (DI) and textural feature (grey level co-occurrence matrix) DI obtained by change vector analysis (CVA), morphological building index DI, and shape feature DI obtained by subtraction to construct the final DI. Secondly, the coarse change detection map obtained by selecting a threshold for the DI saliency map obtained by the use of the frequency-domain significance (FDS) method is pre-classified by the fuzzy c-means (FCM) clustering algorithm. Finally, the neighborhood features obtained from the original images and the feature images of the changed pixels (buildings) and the unchanged pixels in the coarse change map are extracted and used as reliable samples for the ELM training. By using the trained ELM classifier, undetermined pixels are further separated into changed and unchanged classes. Finally, we combine the ELM classification result and the preclassification result together to form the final building change map. Experiments on two real multi-source datasets show that the proposed method can result in a significant improvement in multi-source remote sensing image building change detection performance.

Change detection in SAR images based on superpixel segmentation and image regression

Change detection (CD) is one of the most important application in remote sensing domain. The difference image (DI) generated by traditional change detection methods are sensitive to several factors, such as atmospheric condition changes, illumination variations, sensor calibration, and speckle noise, greatly affecting the detection performance. To avoid the aforementioned problem, in this paper, a novel approach based on superpixel segmentation and image regression is proposed to detect changes between bitemporal synthetic aperture radar (SAR) images. Specifically, the bitemporal images are firstly divided into a number of superpixel pairs under the guidance of segmentation result of a pre-DI. Next, each pixel in pre-event image is reconstructed utilizing its nearest neighbor to reduce the influence of noise. Then, a set of preselected unchanged sample are selected to learn the local regression model and to estimate the post-event image. After that, the final DI can be obtained by measuring the difference between estimated post-event image and the actual one. Finally, the fuzzy c-means (FCM) clustering algorithm is adopted to generate the binary change map. Adequate experiments on four SAR datasets have been tested, and the experimental results compared with the state-of-the-art methods have proved the superiority of the proposed method.

Synthetic aperture radar image change detection based on Kalman filter and nonlocal means filter in the nonsubsampled shearlet transform domain

An unsupervised change-detection method specifically oriented to improve the change-detection accuracy of synthetic aperture radar (SAR) images is proposed. This method has four main steps. First, a preprocessing method is proposed based on the Kalman filter. We concentrate on reducing the false detection or missing detection rate caused by only using a single filter. Second, the difference image containing explicit information about the changed region is generated based on a standard log-ratio operator. Third, an improved nonsubsampled shearlet transform (NSST) algorithm based on the nonlocal means (NLM) filter is proposed. Simultaneously, we decompose the difference image into low-frequency and high-frequency subbands by the improved NSST algorithm. In particular, in order to effectively preserve the detailed information while denoising, the NLM filter is used to suppress the noise of the high-frequency subbands. Fourth, the final difference image is obtained by the fuzzy C-means algorithm, which is selected because of its high clustering performance and wide range of applications. Experiments conducted on three real datasets of images demonstrate the effectiveness of the proposed method.

Change Detection in Multitemporal Monitoring Images Under Low Illumination

Video surveillance may involve the simultaneous monitoring of a large number of areas. Real-time automatic change detection of a monitoring area (such as involving the movement of people or vehicles) can reduce risks incurred in negligent manual observation. However, the low signal-to-noise ratio (SNR) of dark environments can significantly corrupt camera images, making it difficult for machine learning surveillance systems to detect small changes in monitored images. In addition, in the absence of changes between two multitemporal monitoring images, sensor noise can lead to false alarms. The objective of this paper is to reduce the effect of sensor noise on change detection of monitored images and the run time of change detection algorithms. For these purposes, we proposed a novel multitemporal monitoring image change detection algorithm based on morphological structure filtering and normalized fusion difference image. First, the random noise in two surveillance images was removed using a multidirectional weighted multiscale series of a morphological filter. Next, two difference images were obtained by using the compression log-ratio operator and the mean ratio operator, and a fusion difference image was obtained by equal-weight fusion of the two difference images. Then, the sigmoid function was used to compress the fusion difference map to obtain a normalized fusion difference image, and a median filter was used to obtain a final difference image. Finally, the k-means clustering algorithm was utilized to obtain the change detection results. The experimental results demonstrate that the proposed method can accurately detect changes in a night monitoring scene in real time. Subjective and objective evaluation of the experimental results demonstrate that the proposed method is superior to reference algorithms in terms of change detection accuracy, time and robustness.

Change Detection of Optical Remote Sensing Image Disturbed by Thin Cloud Using Wavelet Coefficient Substitution Algorithm.

The detection of changes in optical remote sensing images under the interference of thin clouds is studied for the first time in this paper. First, the optical remote sensing image is subjected to thin cloud removal processing, and then the processed remote sensing image is subjected to image change detection. Based on the analysis of the characteristics of thin cloud images, a method for removing thin clouds based on wavelet coefficient substitution is proposed in this paper. Based on the change in the wavelet coefficient, the high- and low-frequency parts of the remote sensing image are replaced separately, and the low-frequency clouds are suppressed while maintaining the high-frequency detail of the image, which achieves good results. Then, an unsupervised change detection algorithm based on a combined difference graph and fuzzy c-means clustering algorithm (FCM) clustering is applied. First, the image is transformed into a logarithmic domain, and the image is denoised using Frost filtering. Then, the mean ratio method and the difference method are used to obtain two graph difference maps, and the combined difference graph method is used to obtain the final difference image. The experimental results show that the algorithm can effectively solve the problem of image change detection under thin cloud interference.

Change Detection in SAR Images Based on the ROF Model Semi-Implicit Denoising Method.

The explicit solution of the traditional ROF model in image denoising has the disadvantages of unstable results and requiring many iterations. To solve the problem, a new method, ROF model semi-implicit denoising, is proposed in this paper and applied to change detections of synthetic aperture radar (SAR) images. All remote sensing images used in this article have been calibrated by ENVI software. First, the ROF model semi-implicit denoising method is used to denoise the remote sensing images. Second, for the denoised images, difference images are obtained by the logarithmic ratio and mean ratio methods. The final difference image is obtained by principal component analysis fusion (PCA fusion) of the two difference images. Finally, the final difference image is clustered by fuzzy local information C-means clustering (FLICM) to obtain the change regions. The research results show that the proposed method has high detection accuracy and time operation efficiency.

Change Detection Algorithm Based on Discrete Wavelet Transforms and Neighborhood Fuzzy C-Means

A new algorithm on Discrete Wavelet Transform (DWT) and neighborhood FCM is proposed to detect change area from remote sensing image. First, the subtraction and ratio image are obtained by the subtraction and ratio method from the two registered remote sensing images; Then, the DWT is applied to the subtraction and ratio image, the region intensity-based and energy-based fusion rules is adopted to the low frequency and high frequency wavelet coefficients, and the inverse DWT is used to obtain the final difference image; At last, the neighborhood FCM is carried out to get the change areas, the spatial distance information and gray difference information are considered in the objective function of FCM, which could avoid misclassification and enhance the detection probability. Experimental results show that the proposed algorithm has strong ability to suppress noise and good detection results; the detection probability of unban change area can reach to 98.45%, whereas, the detection probability is up to 87.5% for the discontinuous forest change area.

Unsupervised change detection in SAR images based on frequency difference and a modified fuzzy c-means clustering

ABSTRACTChange detection for synthetic aperture radar (SAR) images is a key process in many applications exploiting remote-sensing images. It is a challenging task due to the presence of speckle noise in SAR imaging. This article investigates the problem of change detection in multitemporal SAR images. Our motivation is to avoid using only one detector to measure the change level of different features which is usually considered by classical methods. In this article, we propose an unsupervised change detection approach based on frequency difference in wavelet domain and a modified fuzzy c-means (FCM) clustering algorithm. First, the proposed method extracts high-frequency and low-frequency components using wavelet transform, and then constructs high-frequency and low-frequency difference images using different detectors. Finally, inverse wavelet transform is carried out to obtain the final difference image. In addition, inspired by manifold structure constraint, we incorporate weighted local information into the FCM to reduce the influence of speckle noise. Experimental results performed on simulated and real SAR images show the effectiveness of the proposed method, in terms of detection performance, compared with the state-of-the-art methods.

SAR image change detection based on equal weight image fusion and adaptive threshold in the NSST domain

ABSTRACTIn order to improve the accuracy of change detection and reduce the running time, a change detection method based on equal weight image fusion and adaptive threshold in the NSST domain is proposed. First, the logarithmic transformation is used to transform images and the mean filter is applied to the transformed images. The log-ratio method and the mean ratio method are adopted to generate two kinds of difference images. The final difference image is achieved by equal weight image fusion method. Then, an adaptive threshold denoising method based on non-subsampled shearlet transform (NSST) is used to achieve noise reduction. Finally, the k-means clustering algorithm is utilized to get the change detection results. The experimental results show that the proposed algorithm has better change detection performance than the reference algorithms in visual effect and objective parameters.

Synthetic aperture radar image change detection based on improved bilateral filtering and fuzzy C mean

This paper presents an unsupervised synthetic aperture radar (SAR) image change detection method based on improved bilateral filtering and fuzzy C means (FCM). Many previous approaches to change detection are based on a difference image. Unlike conventional approaches, based on difference images, our method demonstrates superior ability to reduce speckle noise and suppress background information, while still retaining edge information effectively. First, the two images are preprocessed using a Lee filter to remove some of the speckle noise. Second, we use the neighbor-log ratio and the Gauss-log ratio to produce initial change maps. Third, we use the improved bilateral to fuse the two change maps, to obtain an initial difference image. Next, we apply a median filter on the initial difference image, to obtain the final difference image. The above method makes full use of the field information, and it can effectively remove speckle noise while still preserving edge information. Finally, an improved FCM algorithm is used to cluster the denoised difference image. Denoising prior to clustering overcomes the main deficiency of conventional clustering algorithms, which is that they are noise sensitive. Empirical experiments, on three groups of SAR images, suggest that the proposed algorithm outperforms several other methods from the literature, in terms of noise suppression, accuracy, and lower change detection error rates.

LAND COVER CHANGE DETECTION USING SALIENCY ANDWAVELET TRANSFORMATION

How to obtain accurate difference map remains an open challenge in change detection. To tackle this problem, we propose a change detection method based on saliency detection and wavelet transformation. We do frequency-tuned saliency detection in initial difference image (IDI) obtained by logarithm ratio to get a salient difference image (SDI). Then, we calculate local entropy of SDI to obtain an entropic salient difference image (ESDI). The final difference image (FDI) is the wavelet fusion of IDI and ESDI, and Otsu thresholding is used to extract difference map from FDI. Experimental results validate the effectiveness and feasibility.

LAND COVER CHANGE DETECTION USING SALIENCY ANDWAVELET TRANSFORMATION

Abstract. How to obtain accurate difference map remains an open challenge in change detection. To tackle this problem, we propose a change detection method based on saliency detection and wavelet transformation. We do frequency-tuned saliency detection in initial difference image (IDI) obtained by logarithm ratio to get a salient difference image (SDI). Then, we calculate local entropy of SDI to obtain an entropic salient difference image (ESDI). The final difference image (FDI) is the wavelet fusion of IDI and ESDI, and Otsu thresholding is used to extract difference map from FDI. Experimental results validate the effectiveness and feasibility.