A sparsity-driven joint image registration and change detection technique for SAR imagery

The growing rate of breast cancer necessitates immediate global attention. Mammography images are used to determine the stage of malignancy. Breast cancer stages must be identified in order to save a person's life. This article's main goal is to identify different techniques to obtain the difference between two breast cancer mammography images taken of the same individual at different times. This is the first effort to identify breast cancer in mammography images using change detection techniques. The Mammogram Image Change Detection (ICD) technique is also a recent advancement to prevent breast cancer in the early stage and precancerous level in medical images. The main purpose of this work is to observe the changes between breast cancer images in different screening periods using different techniques. Mammogram Breast Cancer Image Change Detection (MBCICD) methods usually start with a Difference Image (DI) and classify the pixels in the DI into changed and unaffected classes using unsupervised fuzzy c means (FCM) clustering methods based on texture features taken from the log and mean ratio difference pictures. Two operators, mean ratio and log ratio, were used to check the changes in the images. The Gabor wavelet is utilized as a feature extraction technique among several standards. Using the Gabor wavelet ratio operators is a useful method for altering the detection of breast cancer in mammography images. Currently, it is challenging to obtain real malignant images of the same person for testing or training. In this study, two images are utilized. To clearly see the changes, one is an image from the MIAS breast cancer mammography images dataset, and the other is a self-generated change image. The research aims to examine the image results and other quantitative analysis results of proposed change detection methods on cancer images. The Mean Ratio Accuracy result is 0.9738, and the Log ratio PCC is 0.9737. The classification results are the Log Ratio + Gabor Filter + FCM is 0.9737, and Mean Ratio +Gabor Filter + FCM is 0.9719. The mean Ratio Accuracy result is 0.9738, Log ratio is 0.9737. Log Ratio + Gabor Filter + FCM is 0.9737, Mean Ratio +Gabor Filter + FCM is 0.9719. Comparing the PCC of proposed change detection methods with the FDA-RMG method on the same dataset, the accuracy is 0.9481 only. The study concludes that variations in mammography breast cancer images could be successfully identified using the ratio operators with Gabor wavelet features.

Many tasks in computer vision require to match image parts. While higher-level methods consider image features such as edges or robust descriptors, low-level approaches compare groups of pixels (patches) and provide dense matching. Patch similarity is a key ingredient to many techniques for image registration, stereo-vision, change detection or denoising. A fundamental difficulty when comparing two patches from “real” data is to decide whether the differences should be ascribed to noise or intrinsic dissimilarity. Gaussian noise assumption leads to the classical definition of patch similarity based on the squared intensity differences. When the noise departs from the Gaussian distribution, several similarity criteria have been proposed in the literature. We review seven of those criteria taken from the fields of image processing, detection theory and machine learning. We discuss their theoretical grounding and provide a numerical comparison of their performance under Gamma and Poisson noises.

With its synoptic view and the repeatability, satellite remote sensing can provide timely, accurate and consistent information about earth’s surface for cost-effective monitoring of environmental changes. In this chapter, recent development in change detection techniques using multitemporal remotely sensed images were reviewed. The chapter covers change detection methods for both optical and SAR images. Various aspects of change detection processes were presented including data preprocessing, change image generation and change detection algorithms such as unsupervised and supervised change detection as well as pixel-based and object-based change detection. The review shows that significant progress has been made in the field of change detection and innovative methods have been developed for change detection using both multitemporal SAR and optical data. Attempts have been made for change detection using multitemporal multisensor/cross-sensor images. The review also identified a number of challenges and opportunities in change detection.

Image change detection is to recognize the changes between two images that are taken over the same scene but at different times, which has been applied broadly in many fields. Fuzzy clustering is a frequently-used technique for unsupervised change detection. However, traditional fuzzy clustering algorithms are easy to be trapped into a local optimum due to the limits of their optimization processes. To tackle the problem, a novel differential evolution algorithm with an automatically learning selection strategy is proposed in this paper. Different from the selection rules of classical differential evolution algorithm, this method firstly pre-classifies all original individuals and trial individuals according to the scope of the individual fitness at each generation, which will preliminarily determine whether they are selected for the next generation. Secondly, in order to increase the diversity of the population, we choose a few individuals from the non-selected population with a low probability into selected ones. Finally, the samples including partial individuals from the selected and non-selected lists are used to train the neural networks that will learn the selection strategy. This method will learn different selection strategies in every generation, which will significantly accelerate the convergence speed. The proposed change detection method, combining fuzzy clustering with newly designed differential evolution algorithm, show excellent performance. Experiments conducted on Synthetic Aperture Radar images have demonstrated the superiority of the proposed method.

Change Detection (CD) techniques applied over multitemporal multispectral or hyperspectral remote sensing images allow monitoring changes in the land use or catastrophe tracking, among other applications. A multiclass CD technique for multidimensional images that is robust in the presence of noise is presented in this paper. The technique combines fusion at feature level to perform a first change/no change labeling (binary CD) and a later stage with fusion at decision level that performs a supervised multidate classification of the changed pixels (multiclass CD) obtaining the final from-To change map. The acquisition of multidimensional images usually corrupts the original signal by adding noise. This noise can be related with natural random processes or it can be produced during the sensor operation. Additive White Gaussian Noise (AWGN) and speckle noise simulate these effects. In this paper the robustness of the proposed CD technique in noisy scenarios for these two types of noise of varying intensity is evaluated. The experimental results show that the proposed technique is more robust than other alternatives, achieving accuracies close to those obtained in the absence of noise. The proposed technique is designed to be efficiently computed in GPU, thus dealing with the high computational cost of the processing of multidimensional images.

Change detection using deep learning approach with object-based image analysis

We present a novel approach to change detection between two brain MRI scans (reference and target.) The proposed method uses a single modality to find subtle changes; and does not require prior knowledge (learning) of the type of changes to be sought. The method is based on the computation of a local kernel from the reference image, which measures the likeness of a pixel to its surroundings. This kernel is then used as a feature and compared against analogous features from the target image. This comparison is made using cosine similarity. The overall algorithm yields a scalar dissimilarity map (DM), indicating the local statistical likelihood of dissimilarity between the reference and target images. DM values exceeding a threshold then identify meaningful and relevant changes. The proposed method is robust to various challenging conditions including unequal signal strength.

ABSTRACTThe speckle noise of synthetic aperture radar (SAR) images limits its application in change detection. Compared with improved ratio (IR) and log-ratio (LR) operators, the neighborhood-based ratio (NR) technique can restrain the influence of speckle noise and is more suitable for change detection in SAR images. However, we find three drawbacks of NR by analyzing this method carefully. To overcome these defects, we propose an improved neighborhood-based ratio (INR) approach for change detection in SAR images. INR restructures the NR operator to exploit the neighborhood information more reasonably and is expected to reduce the impact of speckle noise better. IR, LR, mean ratio operator, NR, and INR are tested on two data sets to compare their performances in change detection of SAR images. Experimental results show that the proposed method can obtain better difference image than other state-of-art methods and improve the accuracy of change detection in SAR images effectively.

Supervised Change Detection in Satellite Imagery Using Super Pixels and Relevance Feedback

A general and extensible framework for assessing change detection techniques

ABSTRACTThe rapid development of deep learning techniques has revolutionized various remote sensing applications, which is especially true for change detection (CD) areas. Consequently, the past few years have seen a surge of deep learning change detection (DLCD) techniques with unparalleled improvements in precision, efficiency, and automation. Despite their huge success, these methods often follow a data‐driven routine, where massive labeled data is required to guarantee network parameter learning. However, it is costly and labor‐intensive to obtain sufficient labeled data for the CD task, especially pixel‐level annotations. In this context, label‐efficient DLCD (LE‐DLCD) techniques have garnered increasing attention, which are capable of training CD networks with incomplete labels, inexact labels, or even no explicit labels. In this review, we conducted a comprehensive survey of state‐of‐the‐art label‐efficient DLCD methods, which are categorized into six schemes of semi‐supervised CD, weakly supervised CD, self‐supervised CD, active learning CD, few‐shot CD, and unsupervised CD. Subsequently, each scheme is further categorized into finer subcategories for in‐depth summarization and analysis. Next, we make systematic quantitative comparisons of typical LE–DLCD methods to provide valuable guidance for real‐world scenarios. Finally, the challenges and future directions of LE‐DLCD are presented in detail, which aims to shed light and inspiration on this area for the CD community. To facilitate transparency, we have shared the selected LE‐DLCD methods via https://github.com/daifeng2016/Awesome‐Label‐efficient‐Deep‐Learning‐Change‐Detection‐Methods.

Several problems in image understanding can be solved by applying the mathematical concept of moments. In this presentation a review of the moment concept will be given, followed by a discussion of the applications of moments to image pattern recognition, change detection, quality evaluation and data compression.

The relationship between visual evoked potentials resulting from substitution of one image of a human "face diagram" for another and assessment of perceived differences between the emotional expressions of these faces were studied. Emotions were altered by changing the curvature of the mouth and/or the slope of the brows. Unlike the traditional approach, in which visual evoked potentials are recorded in response to presentation of a single stimulus bearing a face image, visual evoked potentials in the present study were recorded as the response to instantaneous substitution of a reference stimulus with a test stimulus, and thus represented the direct response to the difference between the stimuli. A characteristic of this approach was the use of a series of functionally associated test stimuli, in which there was a monotonic increase in the difference between the test and references images in terms of the variable characteristics of the stimuli. Analysis revealed differences in the amplitudes of the P120, N180, and P230 peaks in leads O1, O2, P3, P4, T5, and T6, which demonstrated high levels of correlation both with a points-scale assessment of perceived differences between the emotional expressions of faces and with the physical (configurative) differences between images of the same faces as defined by the differences in the orientation angles of lines determining mouth curvature and brow angle. Responses were seen to differences between stimuli for both types of change in pairs of images, both going from reference image to test image and from test image to reference image. Changes in the interpeak amplitude of the P120-N180 potential in the temporal areas of both hemispheres provided the earliest electrophysiological measure of perceived differences between the emotional expressions of human faces. This suggests application of the spherical model for the perception of emotions to specify emotional facial expressions in terms of the activity of line orientation detectors.

Generative adversarial networks (GANs) possess powerful image translation capabilities. They can transform images acquired from different sensors into a unified domain, effectively mitigating the incomparability problem caused by imaging discrepancies in multimodal remote sensing change detection (CD). However, existing approaches predominantly emphasize domain unification while neglecting the loss of fine-grained features inherent in the translation process, consequently compromising both image translation quality and CD accuracy. To overcome these limitations, we propose a novel texture and structure interaction guided GAN (TSIG-GAN). This network establishes interactive guidance between image texture and structural features through a carefully designed dual-stream cross encoder-decoder architecture, enabling in-depth mining of fine-grained features and significantly improving the fidelity of translated images. Furthermore, to address the spatial scale diversity and complexity of remote sensing images, we develop a multi-scale adaptive feature pyramid (MAFP) module and a contextual semantic interaction guidance (CSIG) mechanism, aiming to further strengthen the model's robust representation of fine-grained features across multiple scales and complex scenes. Specifically, the MAFP module effectively captures spatial details of targets at different resolutions by dynamically integrating multi-scale features, thereby preventing detail loss in small objects due to scale discrepancies. The CSIG mechanism achieves deep interaction between texture and structural features at the contextual semantic level, further promoting their mutual cooperation, thereby enhancing the consistency of fine-grained features representation and semantic integrity in complex scenes. Finally, the translated fine-grained images are fed into a custom CD network to extract changes. To evaluate the effectiveness of the proposed method, we conducted systematic experiments on five representative real-world datasets and performed comparative analysis with sixteen state-of-the-art multimodal CD methods. The experimental results demonstrate that TSIG-GAN achieves significant improvements in both image translation and CD performance, exhibiting superior fine-grained restoration capability and change identification capability.

Integration of Sentinel-1 and -2 imagery through advanced cloud computing improves hillside vineyard soil moisture analysis