Image Fusion-Based Land Cover Change Detection Using Multi-Temporal High-Resolution Satellite Images

SAR data allow regular monitoring of target areas since their acquisition is not affected by weather or light condition problems. This characteristic makes them optimal for civil protection tasks, such as earthquake damage assessment, where quick responses in any weather and light conditions are needed. Change detection (CD) methods address this application by identifying changes over an analyzed area using bi-temporal or multi-temporal SAR images. These methods detect the changes using features that provide useful information about the changes. In the last years, CD methods exploiting deep learning (DL) models have become popular because of their capability to learn features from the input data. Many DL CD methods exploit supervised models to identify changes using labeled bi-temporal or multi-temporal data to train the model. However, the gathering of labeled multi-temporal data is challenging. Transfer learning methods avoid the supervised training and the labeled data gathering by exploiting pre-trained models as feature extractors for CD tasks. They achieve good results when the target images are similar to the images used to pre-train the model. We focus this chapter on the unsupervised DL CD methods that exploit unsupervised DL models to extract feature maps. Unsupervised DL models automatically learn features from unlabeled samples during the training. The feature maps retrieved from these models are used to detect changed areas in multi-temporal images and handle the speckle noise.

Due to the lack of support for a high-resolution image in a short time, land cover change detection is always applied on the multitemporal remote-sensed images with different resolutions. The coarse-resolution image contains a large number of mixed pixels, which can seriously limit the utility of the change detection. Soft classification (SC) can be applied to improve this situation through deriving the abundances and generating the fractional change map, but it cannot provide the spatial distribution of the subpixels. Subpixel mapping (SPM) is a potential solution to resolve this problem, and is designed to use the proportions to obtain a sharpened thematic map at a subpixel scale. Based on this thought, the subpixel scale land cover change mapping result can be realized by integrating these two key techniques. However, in practice, there is a serious limitation to the detail and accuracy of the result, because when the scale factor between the different resolution images is large, the subpixel configuration is complex and the data volumes will be amplified. Moreover, with the high proportion of the changed area in the whole image, the change detection process at the subpixel level gets more difficult. The SPM technique is generally performed based only on the abundances of each and the spatial dependence assumption, so it cannot satisfy the demand. In order to overcome this shortcoming, several new reasonable subpixel scale change detection rules are defined in this paper, which dictate the land cover change map must be constructed according to the existing high-resolution image. The output from SC and prior information of the subpixel feature arrangement is applied into a modified cellular automata (CA) model, which can be regarded as a more reasonable tool to monitor the subpixel changes to resolve the big-data problem. Two experiments are performed and the results prove that the proposed method can effectively improve the accuracy of the change detection maps of the spatial distribution in a subpixel scale.

A post-classification change detection method based on iterative slow feature analysis and Bayesian soft fusion

High/very-high-resolution (HR/VHR) multitemporal images are important in remote sensing to monitor the dynamics of the Earth's surface. Unsupervised object-based image analysis provides an effective solution to analyze such images. Image semantic segmentation assigns pixel labels from meaningful object groups and has been extensively studied in the context of single-image analysis, however not explored for multitemporal one. In this article, we propose to extend supervised semantic segmentation to the unsupervised joint semantic segmentation of multitemporal images. We propose a novel method that processes multitemporal images by separately feeding to a deep network comprising of trainable convolutional layers. The training process does not involve any external label, and segmentation labels are obtained from the argmax classification of the final layer. A novel loss function is used to detect object segments from individual images as well as establish a correspondence between distinct multitemporal segments. Multitemporal semantic labels and weights of the trainable layers are jointly optimized in iterations. We tested the method on three different HR/VHR data sets from Munich, Paris, and Trento, which shows the method to be effective. We further extended the proposed joint segmentation method for change detection (CD) and tested on a VHR multisensor data set from Trento.

ABSTRACTIn this article, we propose a novel difference image (DI) creation method for unsupervised change detection in multi-temporal multi-spectral remote-sensing images based on deep learning theory. First, we apply deep belief network to learn local and high-level features from the local neighbour of a given pixel in an unsupervised manner. Second, a back propagation algorithm is improved to build a DI based on selected training samples, which can highlight the difference on changed regions and suppress the false changes on unchanged regions. Finally, we get the change trajectory map using simple clustering analysis. The proposed scheme is tested on three remote-sensing data sets. Qualitative and quantitative evaluations show its superior performance compared to the traditional pixel-level and texture-level-based approaches.

This paper presents a novel method for segmentation and change detection of multispectral images using proximal splitting-based clustering and multiclass support vector machine (MSVM). Initially, the multitemporal satellite images are preprocessed and then textures are extracted using Difference of Offset Gaussian filter. In general, the traditional clustering method uses Euclidean distance as a prime factor for segmentation process. For multitextured images such as remotely sensed images, this metric provides inconsistent output. To achieve better segmented results, proximal splitting algorithm has been proposed. This method has been considered as a solution for iterative minimization problem, which is required to find exact changes between the multitemporal images. The MSVM is chosen to group the segmented clusters into a fixed number of classes, since the clusters obtained from the proximal splitting algorithm are not independent with each other. Then, the classified images are subjected to image differencing method to detect the changes. Experimentation is performed with two real data sets of Landsat7 images, which illustrates that the mean of difference in area obtained by the proposed method is reduced by an average of 35.24% compared to the conventional system. The validity index obtained for data set 1 using proposed algorithm is lower than the existing methods.

A general and extensible framework for assessing change detection techniques

ABSTRACTThis letter presents a novel method for unsupervised change detection (CD) from remote sensing images using level set evolution with local uncertainty constraints (LSELUC). Uncertainty analysis of pixel labels was implemented as prior information to guide the evolution of level curves. Then, local uncertainty and gradient information of level curves were incorporated into the level set energy function to construct local energy constraints. The proposed method can reduce noise, while preserving details in change regions. Furthermore, an advanced regularization strategy of the level set function was adopted to improve the computational efficiency. The performance of the proposed method was validated on two remote sensing data sets. Experimental results show that the proposed method can produce satisfactory CD results.

This article presents a probabilistic model based on a bipartite convolutional architecture for unsupervised change detection. We aim to develop a robust change detection method that can adapt to different types of data and scenarios for multitemporal coregistered remote sensing images of the same spatial resolution. On the premise of coregistration, unsupervised change detection usually suffers from the distinct appearances (different intensities or data structures) of the same object in multitemporal images, such as images obtained in different climatic conditions (season, illumination, and so on), and by different and even heterogeneous sensors. Since change detection in heterogeneous images can also adapt to other scenarios, many methods have been proposed recently focusing on such data, but most of them are limited by the need for labeled data or by specific assumptions. With the excellent and flexible feature learning capability of neural networks, we model the change detection into a Gibbs probabilistic model based on a bipartite neural network. The model is driven by an energy function defined as the squared feature distance, which is the core of change detection. Via optimizing the model, the difference degree of each pixel is automatically obtained for further identification. The probabilistic model learns to capture the distribution in an unsupervised way. Therefore, the proposed method can adapt to various scenarios without being trained by labeled data. Experiments on different types of data and scenarios demonstrate the superiority of the proposed method.

A new method is proposed for change analysis with weight of significance between two multi-temporal multi-spectral images.This method gives us areas which indicate the assigned temporal change, for example, from vegetation to bare soil. Image dataare projected onto a feature space in which the assigned change is emphasized, and temporal changes between two images aredetected with suppression ofirrelevant changes. The validity ofthe method is confirmed by numerical simulation. The methodis successfully applied to actual multi-temporal and multi-spectral images. 1. INTRODUCTION Change analysis is one of the most important processings for monitoring environment. It provides us with information aboutchange, for example, of natural environment to urban area. In change analysis, it is required to analyze the particular changerather than to detect all types of change. There have been a lot of methods reported for change detection[1]. Most of them,however, detect all types of change without specifying the change[2], or analyze the changes categorically using supervisedclassification of two multi-temporal images[3][4]. In the former, the change to be analyzed is detected indirectly using cluster-ing, and in the latter, classification error is serious. A direct method has been required.We propose a method PACE (Particular Change Extractor) for analyzing temporal change with weight of significance. Themethod PACE enables us to select a particular type of change such as from vegetation to bare soil, and extracts areas in whichspectral pattern has the particular change between two multi-temporal and multi-spectral images. The method consists of threeprocedures; data projection, change detection and suppression of irrelevant changes. In the first two, we produce a feature spacederived from an assigned change so that the change is emphasized. Canonical correlation analysis[5] is used for it. We projecta set of image data onto the feature space. The difference between two transformed image data indicates temporal changes. As

Change detection method based on fractal model and wavelet transform for multitemporal SAR images

This paper proposed a novel method for unsupervised change detection of remote sensing images using principal component analysis and differential evolution (PDECD). PDECD consists of two main steps. Firstly, an eigenvector space is generated by principal component analysis (PCA) of image blocks. Difference image is projected onto the eigenvector space to extract image local features, which is essentially composed of local smoothing feature and edge fidelity features. Then PDECD regards change detection as an optimal clustering problem and utilizes the differential evolution algorithm (DE) to search for the optimal change detection results without any priori knowledge. Compared with the existing methods, PDECD is not only robust to image noise, but also sensitive to small changed details. In addition, PDECD can avoid tracking to the local optima in change detection process and improve the detection performance due to the powerful global optimization capability of DE. Considering the image data belonging to two clusters cannot separated by sharp boundaries, so the Jm index of standard fuzzy clustering method is used as the objective function of DE. In order to improve the robustness and automatic detection capability of PDECD, control parameters of DE have been adjusted adaptively. Experiments conducted on real SAR and optical remote sensing images demonstrate the effectiveness of the proposed method.

In this paper a change detection study was conducted using multi-temporal images from two commonly used sensors, MODIS and TM, between June and October, 2003 over Southern Ontario, Canada to evaluate the sensitivity of MODIS images for seasonal land cover changes. Post-classification change detection was used to determine the type of change that had occurred and allow for from-to types of changes to be evaluated. NDVI image differencing was also performed on the MODIS and TM images to compare the vegetation index changes at different spatial resolutions. It was found that MODIS classifications approximated those produced with TM data only when incorporating the thermal band in the classification procedure which takes advantage of the urban heat island effect. Results demonstrate that MODIS post-classification change detection can approximate the levels of change/no-change compared to TM post-classification however the type of change was not accurate due to the spectral mixing that occurs at the coarser 250 meter spatial resolution of MODIS data. The more change at TM level for a MODIS pixel, the higher the likelihood of this corresponding to change at the MODIS level. This study demonstrates that MODIS data would be best suited for detecting changes in large agricultural areas with large field size of homogeneous crop type and growth stage or large areas of forest stands with similar characteristics.

It is quite an important and challenging problem for change detection (CD) from heterogeneous remote sensing images. The images obtained from different sensors (i.e., synthetic aperture radar (SAR) & optical camera) characterize the distinct properties of objects. Thus, it is impossible to detect changes by direct comparison of heterogeneous images. In this article, a new unsupervised change detection (USCD) method is proposed based on image translation. The cycle-consistent adversarial networks (CycleGANs) are employed to learn the subimage to subimage mapping relation using the given pair (i.e., before and after the event) of heterogeneous images from which the changes will be detected. Then, we can translate one image (e.g., SAR) from its original feature space (e.g., SAR) to another space (e.g., optical). By doing this, the pair of images can be represented in a common feature space (e.g., optical). The pixels with close pattern values in the before-event image may have quite different values in the after-event image if the change happens on some ones. Thus, we can generate the difference map between the translated before-event image and the original after-event image. Then, the difference map is divided into changed and unchanged parts. However, these detection results are not very reliable. We will select some significantly changed and unchanged pixel pairs from the two parts with the clustering technique (i.e., <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula> -means). These selected pixel pairs are used to learn a binary classifier, and the other pixel pairs will be classified by this classifier to obtain the final CD results. Experimental results on different real datasets demonstrate the effectiveness of the proposed USCD method compared with several other related methods.

In this paper, a novel change detection method learned from Recurrent Neural Network with transferable ability is proposed. The proposed method, which is based on an improved Long Short Term Memory (LSTM) model, aims at: 1) learning a novel change detection rule to distinguish changed regions with high accuracy; 2) analyzing a new target data with transferable ability from learned change rule; 3) learning the differencing information and detecting the changes independently without any classifiers. In the process of learning the change rule, a core memory cell is utilized to detect and record the differencing information in multi-temporal images; meanwhile, the memory cell can update the storage by iteration for optimization. Finally, experiments are performed on two multi-temporal datasets, and the results show superior performance on detecting changes with transferable ability.