Difference Image Research Articles

Change Detection Based on Fusion Difference Image and Multi-Scale Morphological Reconstruction for SAR Images

Synthetic aperture radar (SAR) image-change detection is widely used in various fields, such as environmental monitoring and ecological monitoring. There is too much noise and insufficient information utilization, which make the results of change detection inaccurate. Thus, we propose an SAR image-change-detection method based on multiplicative fusion difference image (DI), saliency detection (SD), multi-scale morphological reconstruction (MSMR), and fuzzy c-means (FCM) clustering. Firstly, a new fusion DI method is proposed by multiplying the ratio (R) method based on the ratio of the image before and after the change and the mean ratio (MR) method based on the ratio of the image neighborhood mean value. The new DI operator ratio–mean ratio (RMR) enlarges the characteristics of unchanged areas and changed areas. Secondly, saliency detection is used in DI, which is conducive to the subsequent sub-area processing. Thirdly, we propose an improved FCM clustering-change-detection method based on MSMR. The proposed method has high computational efficiency, and the neighborhood information obtained by morphological reconstruction is fully used. Six real SAR data sets are used in different experiments to demonstrate the effectiveness of the proposed saliency ratio–mean ratio with multi-scale morphological reconstruction fuzzy c-means (SRMR-MSMRFCM). Finally, four classical noise-sensitive methods are used to detect our DI method and demonstrate the strong denoising and detail-preserving ability.

Change Detection of Vegetation Cover Using Remote Sensing and GIS – A Case Study of the West Coast Region of South Africa

This article investigates the possible permanent vegetation cover (VC) change over an extended time for five municipal regions in South Africa by applying satellite-acquired remote sensed normalized difference vegetation index (NDVI) values within a geographic information system (GIS), spatial (West Coast District) and time (1981 to 2019 and 2000 to 2020) context. The NDVI index measures surface reflectance and give a quantitative estimation of vegetation growth and biomass. The study found relevance in its application since VC change detection has taken prominence over the past number of years in terms of sustainable development. Methods of analysis include image mapping, temporal image differencing, Moran I statistic, and the Mann-Kendall trend test. In the main areas that recorded significant changes in their NDVI values (plus or minus 0.4 difference on their original NDVI value) over time, in general, have experienced substantial and permanent VC change. These areas are also spatially clustered and concentrated within specific areas within the wider district. However, these areas constitute only a minority of areas (less than 20%), whereas most of the areas within the district did not experience such significant and permanent change in VC. Instead, the changes that did occur in these majority of areas were related to seasonal variation, i.e., temporal changes.

Applications of Image-Based Computer Vision for Remote Surveillance of Slope Instability

Landslides and slope failures represent critical hazards for both the safety of local communities and the potential damage to economically relevant infrastructure such as roads, hydroelectric plants, pipelines, etc. Numerous surveillance methods, including ground-based radar, InSAR, Lidar, seismometers, and more recently computer vision, are available to monitor landslides and slope instability. However, the high cost, complexity, and intrinsic technical limitations of these methods frequently require the design of alternative and complementary techniques. Here, we provide an improved methodology for the application of image-based computer vision in landslide and rockfall monitoring. The newly developed open access Python-based software, Akh-Defo, uses optical flow velocity, image differencing and similarity index map techniques to calculate land deformation including landslides and rockfall. Akh-Defo is applied to two different datasets, notably ground- and satellite-based optical imagery for the Plinth Peak slope in British Columbia, Canada, and satellite optical imagery for the Mud Creek landslide in California, USA. Ground-based optical images were processed to evaluate the capability of Akh-Defo to identify rockfalls and measure land displacement in steep-slope terrains to complement LOS limitations of radar satellite images. Similarly, satellite optical images were processed to evaluate the capability of Akh-Defo to identify ground displacement in active landslide regions a few weeks to months prior to initiation of landslides. The Akh-Defo results were validated from two independent datasets including radar-imagery, processed using state of the art SqueeSAR algorithm for the Plinth Peak case study and very high-resolution temporal Lidar and photogrammetry digital surface elevation datasets for the Mud Creek case study. Our study shows that the Akh-Defo software complements InSAR by mitigating LOS limitations via processing ground-based optical imagery. Additionally, if applied to satellite optical imagery, it can be used as a first stage preliminary warning system (particularly when run on the cloud allowing near real-time processing) prior to processing more expensive but more accurate InSAR products such as SqueeSAR.

Unsupervised change detection in SAR images based on generalized likelihood ratio test and a two-stage morphological filter

ABSTRACT Generalized likelihood ratio test (GLRT) is an efficient method to generate difference image (DI) for change detection (CD) using synthetic aperture radar (SAR) images. GLRT is usually applied with a fixed-size moving window to the neighbourhood regions in multitemporal SAR images. The fixed window may be however not optimal for all the pixels under test. To solve disadvantages of the GLRT method, an adaptive circular window is proposed in this work. While the adaptive square window and the best fixed square window achieve an average Kappa coefficient of 83.55% and 82.12%, respectively, the adaptive circular window improves the average Kappa coefficient by 83.65% in six datasets. As another difficulty, speckle noise reduces quality of DI in SAR change detection. Three steps are considered to minimize effects of the speckle noise: (1) a two-stage morphological filter is suggested to reduce the speckle noise; (2) to generate DI, the adaptive circular window for generalized likelihood ratio test (ACWGLRT) is proposed that reduces influence of the speckle noise while preserves the edge details of multitemporal images; and (3) spatial fuzzy c-means (SFCM) is used to reduce effects of the residual speckle-noise during DI classification. The experimental results show superior performance of the proposed change detection method with respect to several competitors. The proposed method has the best Kappa coefficient and percentage correct classification (PCC) in four datasets of Ottawa, San Francisco, Farmland C, and Inland water.

MSGATN: A Superpixel-Based Multi-Scale Siamese Graph Attention Network for Change Detection in Remote Sensing Images

With the rapid development of Earth observation technology, how to effectively and efficiently detect changes in multi-temporal images has become an important but challenging problem. Relying on the advantages of high performance and robustness, object-based change detection (CD) has become increasingly popular. By analyzing the similarity of local pixels, object-based CD aggregates similar pixels into one object and takes it as the basic processing unit. However, object-based approaches often have difficulty capturing discriminative features, as irregular objects make processing difficult. To address this problem, in this paper, we propose a novel superpixel-based multi-scale Siamese graph attention network (MSGATN) which can process unstructured data natively and extract valuable features. First, a difference image (DI) is generated by Euclidean distance between bitemporal images. Second, superpixel segmentation is employed based on DI to divide each image into many homogeneous regions. Then, these superpixels are used to model the problem by graph theory to construct a series of nodes with the adjacency between them. Subsequently, the multi-scale neighborhood features of the nodes are extracted through applying a graph convolutional network and concatenated by an attention mechanism. Finally, the binary change map can be obtained by classifying each node by some fully connected layers. The novel features of MSGATN can be summarized as follows: (1) Training in multi-scale constructed graphs improves the recognition over changed land cover of varied sizes and shapes. (2) Spectral and spatial self-attention mechanisms are exploited for a better change detection performance. The experimental results on several real datasets show the effectiveness and superiority of the proposed method. In addition, compared to other recent methods, the proposed can demonstrate very high processing efficiency and greatly reduce the dependence on labeled training samples in a semisupervised training fashion.

Design Assessment and Simulation of PCA Based Image Difference Detection and Segmentation for Satellite Images Using Machine Learning

It is possible to define the quantity of temporal effects by employing multitemporal data sets to discover changes in nature or in the status of any object based on observations taken at various points in time. It's not uncommon to come across a variety of different methods for spotting changes in data. These methods can be categorized under a single umbrella term. There are two primary areas of study: supervised and unsupervised change detection. In this study, the goal is to identify the changes in land cover. Covers a specific area in Kayseri using unsupervised change detection algorithms and Landsat satellite pictures from various years have been gleaned through the use of remote sensing. In the meantime, image differencing is taking place. The method will be applied to the photographs using the image-enhancing process. In the next step, Principal Component Analysis (PCA) is employed. The difference image will be analyzed using Component Analysis. To find out which locations have and which do not. As a first step, a procedure must be in place. We've finished registering images one after the other. Consequently, the photos are being linked together. After then, it's back to black and white. Three non-overlapping portions of the difference image have been created. This can be done using the principal component analysis method. From the eigenvector space, we may get to the fundamental components. As a last point, the major feature vector space fuzzy C-Means Clustering is used to divide the component into two clusters, and then a change detection technique is carried out. As the world's population grew, farmland expansion and unplanned land encroachment intensified, resulting in uncontrolled deforestation around the globe. This project uses unsupervised learning algorithm K-means clustering. In a cost-effective manner that can be employed by officials, companies as well as private groups, to assist in fighting illicit deforestation and analysis of satellite database.

Acupoint Selection for Autonomous Massage Based on Infrared Thermography

After breast cancer surgery, the patient has difficulty in selecting the acupoints for autonomous massage, due to the changes in muscles and nerves on the affected side. To overcome the difficulty, this paper proposes an acupoint selection method for autonomous massage after breast cancer surgery, based on infrared thermography. Firstly, the contrast of the infrared thermogram was enhanced by image differencing, and the chest region and upper limbs were segmented through global thresholding. Next, temperature specific detection and edge detection algorithms were combined to automatically locate the main chest organs, and the horizontal and vertical axes of the upper limbs, according to the temperature specificity and temperature variation induced by the physiological structure of chest and upper limbs. Furthermore, the main acupoints for autonomous massage were determined, based on their positions relative to the axes of main organs. Experimental results show that our method achieved a positioning accuracy greater than 90.12%. The excellent positioning effect improves the compliance of patients in active rehabilitation.

Multiscale Geometric Analysis Fusion-Based Unsupervised Change Detection in Remote Sensing Images via FLICM Model.

Remote sensing image change detection is widely used in land use and natural disaster detection. In order to improve the accuracy of change detection, a robust change detection method based on nonsubsampled contourlet transform (NSCT) fusion and fuzzy local information C-means clustering (FLICM) model is introduced in this paper. Firstly, the log-ratio and mean-ratio operators are used to generate the difference image (DI), respectively; then, the NSCT fusion model is utilized to fuse the two difference images, and one new DI is obtained. The fused DI can not only reflect the real change trend but also suppress the background. The FLICM is performed on the new DI to obtain the final change detection map. Four groups of homogeneous remote sensing images are selected for simulation experiments, and the experimental results demonstrate that the proposed homogeneous change detection method has a superior performance than other state-of-the-art algorithms.

Change Detection in Multitemporal SAR Images Based on Slow Feature Analysis Combined With Improving Image Fusion Strategy

Change detection in multitemporal synthetic aperture radar (SAR) images has been an important research content in the field of remote sensing for a long time. In this article, based on the slow feature analysis (SFA) theory and the nonsubsampled contourlet transform (NSCT) algorithm, we propose a novel unsupervised change detection method called NSCT nonlocal means (NSCT-NLM). The powerful extraction to the changed information of SFA and the superior information fusion of NSCT are jointly adopted in this method. The main framework consists of the following parts. First, SFA and the log-ratio operator are used to generate difference images (DIs) independently. Then, the NSCT is used to fuse two DIs into a new higher quality DI. The newly fused DI combines the complementary information of the two kinds of original DI. Therefore, the contrast of the changed regions and unchanged regions is greatly enhanced, as well as the changed details are preserved more completely. Furthermore, an NLM filtering algorithm is employed to suppress the strong speckles in the fused DI. We use the fuzzy C-means algorithm to generate the final binary change map. The experiments are carried out on two public datasets and three real-world SAR datasets from different scenarios. The results demonstrate that the proposed method has higher detection accuracy by comparing with the reference methods.

Change Detection Based on Convolutional Neural Networks Using Stacks of Wavelength-Resolution Synthetic Aperture Radar Images

This article presents two supervised change detection algorithms (CDA) based on convolutional neural networks (CNN) that use stacks of co-registered wavelength-resolution synthetic aperture radar (SAR) images to detect changes in an image under monitoring. The additional information of a scene of interest provided by SAR image stacks can be explored to enhance the performance of change detection algorithms. In particular, stacks of images with similar statistics can be obtained for ultra-wideband (UWB) very high frequency (VHF) SAR systems, as they produce images highly stable in time. The proposed CDAs can be summed up into four stages: difference image formation, semantic segmentation, clustering, and change classification. The CNN-GSP algorithm is based on a ground scene prediction (GSP) image, which is used as a reference to form a difference image (DI). A CNN-based model then analyzes the DI. The CNN-MDI algorithm feeds multiple DIs with identical monitored images to a CNN-based model, which will concurrently analyze their features. Tests with CARABAS-II data show that the proposed CDAs can outperform other state-of-the-art algorithms that also use stacks of WR-SAR images. Beyond that, the proposed algorithms outperformed a CNN-based CDA that does not use image stacks, which shows that CNN-based algorithms can use the additional information provided by stacks of SAR images to reduce false alarm occurrences while increasing the probability of detection of changes.

Change Detection Approach for SAR Imagery Based on Arc-Tangential Difference Image and k-Means++

In this letter, an unsupervised change detection (CD) approach based on arc-tangential difference and <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++ clustering is presented for synthetic aperture radar (SAR) remote-sensing images. The images are first standardized with their variance values using a logarithmic function applied to multitemporal images. The difference image (DI) is then calculated by subtracting the SAR images using the arc-tangential subtraction operator. After that, the DI is subjected to a 2-D Gaussian filter and a median filter, respectively. Filters are essential for determining the best feature space for CD. The 2-D Gaussian filter smooths DIs to retain local area consistency, while the median filter handles edge information. Finally, using <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++, a quick and efficient clustering approach, filtered data is clustered into two classes. Experiments using real-world datasets in Bern, Ottawa, and Yellow River have demonstrated that the given technique is fast, successful, and effective.

Unsupervised Change Detection of Multispectral Images Based on PCA and Low-Rank Prior

In this letter, we propose a new unsupervised change detection method based on low-rank prior for multispectral images. It is assumed that the changed and unchanged pixels are from different subspaces due to different appearance and statistical properties. So, low-rank representation (LRR) is employed to find informative pixels from the superpixels of the difference image (DI). Besides, taking the sparsity of changed pixels in the observed scenes into consideration, the selection rule is designed to distinguish these pixels. Then, principal component analysis (PCA) is used for the training of changed and unchanged dictionaries from these pixels. Finally, the change map is estimated by comparing the reconstruction error of each pixel in DI on changed and unchanged dictionaries. By LRR, more representative pixels are found for subsequent dictionary learning, which can efficiently improve the performance of the proposed method. Experiments on multitemporal images from the Landsat satellite demonstrate the effectiveness of the proposed method.

An Unsupervised Remote Sensing Change Detection Method Based on Multiscale Graph Convolutional Network and Metric Learning

As a fundamental application, change detection (CD) is widespread in the remote sensing (RS) community. With the increase in the spatial resolution of RS images, high-resolution remote sensing (HRRS) image CD tasks receive growing attention. The change information hidden in multitemporal HRRS images could help discover our planet comprehensively. In the current deep learning era, convolutional neural networks (CNNs) have become one of the most powerful tools for a wide range of RS tasks including HRRS image CD, due to their superb feature learning capacity. However, most of them need a large amount of labeled data to accomplish the CD process, which is challenging or even impractical in many RS applications. Also, given the limited valid receptive field, CNNs can only capture short-range context within HRRS images, which is probably not enough to fully explore change information from the images. To overcome these limitations, in this article, we propose an unsupervised CD method, termed GMCD, based on graph convolutional network (GCN) and metric learning. GMCD consists of a Siamese fully convolution network (FCN), a multiscale dynamic GCN (Mlt-GCN), and a pseudolabel generation mechanism based on metric learning. The Siamese FCN contains a Siamese encoder and a pyramid-shaped decoder, aiming to extract multiscale features and integrate them to generate reliable difference images (DIs). Mlt-GCN focuses on capturing the short- and long-range contextual patterns at feature map level to extract changed and unchanged areas completely. The pseudolabel generation mechanism aims to produce reliable pseudolabels (changed, unchanged, and uncertain) to help accomplish the model training in an unsupervised way. Experiments on four HRRS image CD datasets demonstrate that GMCD outperforms the existing state-of-the-art methods.

Change Type Recognition for SAR Images Using Statistical Bidirectional LSTM Network With Few Labeled Samples

Change detection in synthetic aperture radar (SAR) images has garnered significant research interest. However, to date, only the change area can be obtained, which seriously restricts further development of SAR image applications. To solve this problem, this paper proposes a change type recognition method for SAR images based on a statistical bidirectional long short-term memory (LSTM) network. The proposed method can obtain the changed area and identify the land type change between pairs of SAR images. Considering the statistical characteristics and sample number of SAR images, change detection and type recognition are realized in a unified framework. First, feature extraction is performed through the statistical features of the original SAR image and difference image (DI). Then, a bidirectional LSTM network is used to identify the change types and obtain the change detection results under the small sample deep learning network. On two sets of medium-low resolution SAR image data and one set of GaoFen-3 high-resolution real SAR image data, our method produced high-quality results under small sample conditions.

Unsupervised SAR Image Change Detection for Few Changed Area Based on Histogram Fitting Error Minimization

Change detection in synthetic aperture radar (SAR) images is an essential task of remote sensing image analysis. However, the thresholding procedure is the main difficulty in change detection for few changed areas for traditional change detection methods. In this paper, we propose a novel change detection method for very few changed or even none changed areas. The proposed method contains three procedures: difference image (DI) generation, thresholding, and spatial analysis. In the second procedure, a new thresholding method called Histogram Fitting Error Minimization (HFEM) is proposed for few changed areas. HFEM is derived under the assumption that the unchanged class in the absolute-valued difference image follows the Half-Normal distribution, and the changed class follows the Gaussian distribution. In the spatial analysis procedure, a new conditional random fields (CRF) method based on Half-Normal distribution is proposed to model the mutual influences among image pixels. The proposed CRF method is called Half-Normal CRF (HNCRF). Experiments carried out on both synthetic datasets and four real SAR datasets demonstrate the superiority of our method. Not only few changed datasets but datasets with lots of changes are used in the experiments. The Kappa coefficients of the proposed method can reach up to ten times that of the traditional method under extreme conditions. The results prove that the proposed method outperforms the traditional methods in the case of few changed areas. Meanwhile, the proposed method can get similar results compared to traditional methods under normal conditions.

Novel Multiscale Decision Fusion Approach to Unsupervised Change Detection for High-Resolution Images

High-resolution remote sensing images usually contain multiscale information, which can be used to enhance the change detection (CD) performance. How to make effective use of multiscale information needs intensive study. This letter presents a novel multiscale decision fusion (MDF) method for unsupervised CD based on Dempster–Shafer (DS) theory and modified conditional random field (CRF). The method consists of three main steps: 1) images of three different scales are created automatically by image segmentation, and then three-scale difference images (DIs) are produced by applying change vector analysis to the three-scale images; 2) the membership function of each scale DI is estimated by fuzzy clustering, and the fusion membership, as well as an initial CD map, is obtained by combining the estimated membership using DS theory; and 3) the initial CD map is refined with an improved CRF that incorporates a spatial attraction model. The proposed method can combine the multiscale information in images and the spatial contextual information. The effectiveness of the proposed method was validated by two experiments with high-resolution remote sensing images.

Three-Dimensional Change Detection in Urban Areas Based on Complementary Evidence Fusion

With the acceleration of urbanization, it is essential to carry out change detection (CD) and obtain surface change information in urban areas. In the early stages, the spectral information of remote sensing images was used as a change index to capture the spectral and texture changes of ground objects in a two-dimensional plane. However, due to the dense buildings in urban areas, shadows and occlusions can be easily formed, and spectral information is sensitive to the imaging environment, such as the illumination, atmospheric conditions, and imaging angles, so that the detection results based on remote sensing images can often be incomplete. Most changes include not only 2-D plane changes but also 3-D elevation changes. Compared with spectral information, the elevation is more stable and more resistant to interference. Therefore, the fusion of remote sensing image and digital surface model (DSM) data has the potential to be used to detect the changes in urban areas. In this article, we propose a complementary evidence fusion 3-D CD framework based on the Dempster–Shafer theory (CDST). In this framework, DSM and normalized difference vegetation index (NDVI) data are combined using a complementary evidence combination rule. The DSM data can effectively overcome the impact of shadows, and the NDVI data can capture the relevant changes of height-insensitive ground objects, such as vegetation and water. When mapping the basic probability assignment (BPA) of the difference image (DI), prior knowledge is used to ensure that the BPA is not affected by the data distribution. Since DSM and remote sensing image data are heterogeneous data, there is a high degree of conflict when representing the change information characteristics of specific areas. For example, the change between grassland and road is small in elevation but significant in the spectral details, and the traditional Dempster’s combination rule no longer applies. The proposed CDST framework uses a complementary evidence combination rule, which can effectively alleviate the conflicts between the evidence sources and improve the integrity of the detected changes. The experimental results obtained on real datasets confirm that the proposed method does indeed perform well.

Semisupervised SAR image change detection based on a siamese variational autoencoder

In synthetic aperture radar (SAR) image change detection, the deep learning has attracted increasingly more attention because the difference images (DIs) of traditional unsupervised technology are vulnerable to speckle noise. However, most of the existing deep networks do not constrain the distributional characteristics of the hidden space, which may affect the feature representation performance. This paper proposes a variational autoencoder (VAE) network with the siamese structure to detect changes in SAR images. The VAE encodes the input as a probability distribution in the hidden space to obtain regular hidden layer features with a good representation ability. Furthermore, subnetworks with the same parameters and structure can extract the spatial consistency features of the original image, which is conducive to the subsequent classification. The proposed method includes three main steps. First, the training samples are selected based on the false labels generated by a clustering algorithm. Then, we train the proposed model with the semisupervised learning strategy, including unsupervised feature learning and supervised network fine-tuning. Finally, input the original data instead of the DIs in the trained network to obtain the change detection results. The experimental results on four real SAR datasets show the effectiveness and robustness of the proposed method.

A New Structure for Binary and Multiple Hyperspectral Change Detection Based on Spectral Unmixing and Convolutional Neural Network

The earth is constantly being changed by natural events and human activities that constantly threaten our environment. Therefore, accurate and timely monitoring of changes at the surface of the earth is of great importance for properly facing their consequences. This research presents a new hyperspectral change detection (HCD) framework based on a robust binary mask and convolutional neural network (CNN). The proposed method is implemented in three parts: (1) the first part provides a robust binary change map based on Otsu and dynamic time wrapping (DTW) algorithms; the DTW algorithm plays a predictor role that is a robust predictor for HCD purposes. Also, Otsu’s algorithm gives an estimate about the approximate threshold for detecting change and no-change class areas. These class areas will be used in the next steps. (2) The second part generates pseudo training data based on an image differencing (ID) algorithm and spectral unmixing (SU) manner for multiple change detection. This pseudo training data will be used for training the CNN model in the next step. (3) Finally, the multiple change map is generated by training the CNN network based on pseudo training data. The result of HCD maps is compared to other robust hyperspectral change detection methods by two real bi-temporal hyperspectral image datasets. The result of HCD in multiple change map shows the proposed method can have high performance compared to other HCD methods with an overall accuracy (OA) of more than 92% and Kappa coefficient (KC) of 0.77 and higher.

Probabilistic approximation to change and no change in multispectral remote sensing

ABSTRACT Change assessment is a central and active area of inquiry in remote sensing. Broadly adopted probabilistic methods discriminate between change and no change based on image differencing, normalization and aggregation into a single band metric that is assumed to follow a Chi-square distribution. The adoption of the Chi-square distribution requires the application of band transformation to original data that is computer expensive and that operates under an untested assumption of multivariate distribution of pixel values in input bands. Despite the wide adoption of the Chi-square distribution, its appropriateness for discriminating between change and no change remains an open question. Here, we test the performance of the Chi-square distribution for change assessment compared to the use of the more-generic Gamma distribution. For this purpose, we implement an algorithm that iteratively removes observations labelled as change according to a pre-defined probabilistic distribution and a probability change threshold. We implement the algorithm in three study areas in tropical and subtropical regions representing contrasting ecological conditions and land cover types and changes. We also test whether input multispectral data meets the assumption of multivariate normality required for band transformation and for the use of the Chi-square distribution. We found that the Gamma distribution applied to untransformed data consistently performs more robustly to discriminate between change and no change compared to the application of band transformation and subsequent use of the Chi-square distribution. We also found that, in none of the evaluated cases, input multispectral data meet the assumption of multivariate normality required for band transformation. Our results suggest that assumptions about multivariate normality can affect the robustness of probabilistic change assessment in multispectral remote sensing. We encourage the remote sensing community to adopt the Gamma distribution applied to untransformed data as a probabilistic approach to differentiate between change and no change.