Accuracy Of Change Detection Research Articles

The effect of congruent emotional context on semantic memory during discourse comprehension

ABSTRACT This study examined the effect of emotional context on the semantic memory of subsequent emotional words during discourse comprehension in two eye-tracking experiments. Four-sentence discourses were used as experimental materials. The first three sentences established an emotional or neutral context, while the fourth contained an emotional target word consistent with the preceding emotional context's valence. The discourses were presented twice using the text change paradigm, where the target words were replaced with strongly - or weakly-related words during the second presentation. Thus, four conditions were included in the present study: Emotional-strongly-related, Emotional-weakly-related, Neutral-strongly-related and Neutral-weakly-related. In Experiment 1, negative contexts and negative target words were used, whereas in Experiment 2, positive contexts and positive target words were used. The results revealed a semantic relatedness effect, whereby the strongly-related words have lower change detection accuracy, longer reading times and more fixations in both Experiments 1 and 2. Furthermore, across both experiments, the magnitude of the semantic relatedness effect was greater in the emotionally congruent contexts than in the neutral contexts. These results suggest that emotional context could increase efforts to change the discrimination of subsequent words and demonstrate an important role of emotional context on semantic memory during discourse processing.

Incorporating Phenological Patterns and Multi-source Remote Sensing Imagesfor Cropland Change Detection

Abstract. Accurate information on cropland changes is critical for monitoring arable land minimum, ensuring national food security, and grasping the situation of agricultural production and supply. The change detection using remote sensing images is one of the main methods for quickly extracting cropland changes. However, existing methods were highly susceptible to seasonal differences due to the high heterogeneity of cultivated land. In this study, an integrated framework was proposed to perform change detection by incorporating phenological patterns and multi-source remote sensing images.There were two improvements in this proposed cropland change detection method: 1) the multi-source remote sensing images were utilized to fill the missing data within a time-series image stack by considering phenological patterns of cropland and 2) the Seq2Seq model considering phenological patterns was developed to extract changes in cropland directly. Compared to the traditional change detection methods, the proposed strategy was able to detect change process. Experiments demonstrated that the proposed method can significantly improved change detection accuracies, given a limited number of labeled samples.

Self-supervised image change detection method based on lightweight capsule network

In response to the significant impact of speckle noise on the accuracy of SAR image change detection, the high complexity of existing capsule network based image change detection methods, and the loss of a large amount of original image information in training samples, this paper proposes a self supervised image change detection method based on the Light Capsule Network (SCapsNet). First, the log ratio operator difference graph is generated, and the "pseudo label" of training samples with high confidence is obtained through the maximum inter class variance method and fuzzy C-means clustering method, which lays the foundation for self-supervised learning; Secondly, construct a three channel training sample based on the difference map of two temporal SAR images and logarithmic ratio operators to maximize the preservation of sample information; Then, SCapsNet is designed to extract training sample features through single scale convolution, and a single scale capsule network is used to mine spatial relationships between features; Finally, comparative experiments and ablation experiments were set up and tested on 5 real SAR Datasets. The experimental results show that the The advantage of this method is to improve the operational efficiency of the method while reducing model complexity, obtain stronger robust features, suppress the adverse impact of speckle noise on change detection performance, and improve change detection performance.

Damage Scene Change Detection Based on Infrared Polarization Imaging and Fast-PCANet

Change detection based on optical image processing plays a crucial role in the field of damage assessment. Although existing damage scene change detection methods have achieved some good results, they are faced with challenges, such as low accuracy and slow speed in optical image change detection. To solve these problems, an image change detection approach that combines infrared polarization imaging with a fast principal component analysis network (Fast-PCANet) is proposed. Firstly, the acquired infrared polarization images are analyzed, and pixel image blocks are extracted and filtered to obtain the candidate change points. Then, the Fast-PCANet network framework is established, and the candidate pixel image blocks are sent to the network to detect the change pixel points. Finally, the false-detection points predicted by the Fast-PCANet are further corrected by region filling and filtering to obtain the final binary change map of the damage scene. Comparisons with typical PCANet-based change detection algorithms are made on a dataset of infrared-polarized images. The experimental results show that the proposed Fast-PCANet method improves the PCC and the Kappa coefficient of infrared polarization images over infrared intensity images by 6.77% and 13.67%, respectively. Meanwhile, the inference speed can be more than seven times faster. The results verify that the proposed approach is effective and efficient for the change detection task with infrared polarization imaging. The study can be applied to the damage assessment field and has great potential for object recognition, material classification, and polarization remote sensing.

Multiobjective sparse unmixing based hyperspectral change detection

Hyperspectral unmixing can provide the composition of ground objects, while change detection can identify the changes of the same region over time. Therefore, unmixing based hyperspectral change detection can investigate changes in a subpixel-level. It can provide not only whether the change happens or not but also how to change. This paper will gradually establish an unconstrained sparse unmixing based change detection model and design a multiobjective optimization sparse unmixing approach (termed MoSU-CD) to solve it for more subpixel-level change details. Compared with the existing sparse unmixing based change detection approaches, MoSU-CD directly unmixes the difference map of multi-temporal images rather than multi-temporal images themselves respectively. Then it explores the change details of one substance in each pixel based on the unmixing results, i.e., endmembers and abundances. In addition, in order to improve the change detection accuracy, an ensemble decision strategy is designed for the Pareto optimal solutions obtained by multiobjective optimization method. Based on this strategy, the subpixel-level changes can be aggregated to generate an aggressive pixel-level change map. The experimental results on synthetic and real data sets demonstrate that the proposed MoSU-CD not only outperforms the benchmark and state-of-art approaches in terms of pixel-level change detection but also provides additional subpixel-level change information.

MsFNet: Multi-Scale Fusion Network Based on Dynamic Spectral Features for Multi-Temporal Hyperspectral Image Change Detection

With the development of satellite technology, the importance of multi-temporal remote sensing (RS) image change detection (CD) in urban planning, environmental monitoring, and other fields is increasingly prominent. Deep learning techniques enable a profound exploration of the intrinsic features within hyperspectral (HS) data, leading to substantial enhancements in CD accuracy while addressing several challenges posed by traditional methodologies. However, existing convolutional neural network (CNN)-based CD approaches frequently encounter issues during the feature extraction process, such as the loss of detailed information due to downsampling, which hampers a model’s ability to accurately capture complex spectral features. Additionally, these methods often neglect the integration of multi-scale information, resulting in suboptimal local feature extraction and, consequently, diminished model performance. To address these limitations, we propose a multi-scale fusion network (MsFNet) which leverages dynamic spectral features for effective multi-temporal HS-CD. Our approach incorporates a dynamic convolution module with spectral attention, which adaptively modulates the receptive field size according to the spectral characteristics of different bands. This flexibility enhances the model’s capacity to focus on critical bands, thereby improving its ability to identify and differentiate changes across spectral dimensions. Furthermore, we develop a multi-scale feature fusion module which extracts and integrates features from deep feature maps, enriching local information and augmenting the model’s sensitivity to local variations. Experimental evaluations conducted on three real-world HS-CD datasets demonstrate that the proposed MsFNet significantly outperforms contemporary advanced CD methods in terms of both efficacy and performance.

Arterial blood pressure monitoring in stroke cohorts: the impact of reduced sampling rates to optimise remote patient monitoring.

Remote patient monitoring (RPM) beat-to-beat blood pressure (BP) provides an opportunity to measure poststroke BP variability (BPV), which is associated with clinical stroke outcomes. BP sampling interval (SI) influences ambulatory BPV, but RPM BP SI optimisation research is limited. SI and RPM device capabilities require compromises, meaning SI impact requires investigation. Therefore, this study assessed healthy and stroke subtype BPV via optimised BP sampling, aiding sudden BP change identification and potentially assisting cardiovascular event (recurrent stroke) prediction. Leicester Cerebral Haemodynamic Database ischaemic [acute ischaemic stroke (AIS), n = 68] and haemorrhagic stroke (intracerebral haemorrhage, n = 12) patient and healthy control (HC, n = 40) baseline BP data were analysed. Intrasubject and interpatient SD (SDi/SDp) represented individual/population variability with synthetically altered SIs. Matched-filter approaches using cross-correlation function detected sudden BP changes. At SIs between 1 and 180 s, SBP and DBP SDi staticised while SDp increased at SI < 30 s. Mean BP and HR SDi and SDp increased at SI < 60s. AIS BPV, normalised to SI1s, increased at SI30s (26%-131%) and SI120s (1%-274%). BPV increased concomitantly with SI. Cross-correlation analysis showed HC and AIS BP sudden change detection accuracy reductions with increasing SI. Positive BP deviation detection fell 48.48% (SI10s) to 78.79% (SI75s) in HC and 67.5% (SI10s) to 100% (SI75s) in AIS. Negative BP deviation detection fell 50% (SI10s) to 82.35% (SI75s) in HC and 52.27% (SI10s) to 95.45% (SI75s) in AIS. Sudden BP change detection and BPV are relatively robust to SI increases within certain limits, but accuracy reductions generate unacceptable estimates, considerable within RPM device design. This research warrants further SI optimisation.

An image Analysis Algorithm for Detecting Modified Content on the Internet Against Cybercrime. A Technique for Estimating the Probability of Modification

Aim: The aim of this study is to present a cutting-edge image analysis algorithm designed to estimate the probability of modifications in digital images, a critical component in cybersecurity for detecting altered content on the Internet. This technique enhances the accuracy of change detection by distinguishing real inconsistencies from accidental changes. As the problem of sophisticated image processing software and fake images continues to rise on the Internet, the issue of image authenticity becomes increasingly crucial in many areas of science and society. Traditional visual inspection methods are inadequate, as research shows human perception is limited in recognizing the authenticity of images. This study aims to improve the accuracy of detecting modifications by distinguishing authentic inconsistencies from random anomalies Project and methods: This project involves developing an algorithm that leverages noise analysis and a statistical validation step to detect image modifications. The algorithm employs the False Positive Rate Index (FPRI) to manage false positives, providing a reliable confidence level for each detection. The method includes visual exploration to interpret detections and compares the algorithm’s performance with other state-of-the-art techniques in scenarios such as retouching, coloring, and merging. The algorithm analyzes noise in images, considering changes in its statistical properties due to image processing like noise reduction, demosaicing, chromatic aberration correction, color matching, gamma correction, and image compression. It compares global noise curves with local ones to identify potential changes, with results visualized using heat maps. Results: The algorithm demonstrates promising performance across various test scenarios, successfully identifying true modifications among random events. However, it shows limitations in detecting certain types of forgeries, such as internal copy transfer and merging in high-noise areas. Tests conducted on various data sets, including scenarios of retouching, coloring, and merging, confirm the algorithm’s effectiveness in detecting modifications. The results are compared with other noise analysis methods such as Splicebuster, Noiseprint, and Mahdian, showing superior performance in most cases. Conclusions: The proposed method represents a significant advance in image forgery detection within the field of cybersecurity, offering rigorous statistical validation and a measurable level of detection confidence. Despite some challenges in detecting specific types of manipulation, the algorithm is a valuable tool in digital image analysis and forensic research, enhancing the reliability of image alteration detection. As the use of digital technologies increases, so does the importance of image interpretation in areas like fire investigations and other incidents involving records from both classical and thermographic imaging systems. This method is particularly valuable in forensic investigations, where objective evidence of image authenticity is crucial. Future challenges include the use of neural networks to replicate the characteristic noise of original images to hide alterations, underscoring the need for continued development of advanced forgery detection techniques. Keywords: wykrywanie modyfikacji obrazu, cyberbezpieczeństwo, analiza obrazu, wykrywanie fałszywych obrazów

Evaluating the Accuracy of Change detection Using Spectral Indices and spectral classification, A Case Study of Fayoum Governorate, Egypt

Evaluating the Accuracy of Change detection Using Spectral Indices and spectral classification, A Case Study of Fayoum Governorate, Egypt

LULC change detection analysis of Chamarajanagar district, Karnataka state, India using CNN-based deep learning method

The change detection analysis of land use land cover (LULC) is an important task in several fields and applications such as environmental monitoring, urban planning, disaster management, and climate change studies. This study focuses on the use of remote sensing (RS) and geographic information systems (GIS) to identify the changes in Chamarajanagar district, which is located in Karnataka state, South India. This paper mainly focuses on the classification and change detection analysis of LULC in 2011 and 2021 using linear imaging self-scanning sensor-III (LISS-III) satellite images. Traditional methods for LULC classification involve manual interpretation of satellite images, which provides lower accuracy. Therefore, this paper proposed the Convolutional Neural Network (CNN)-based deep learning classification method for LULC classification. The main objective of the research work is to perform an accurate change detection of the Chamarajanagar district using the classified maps of the years 2011 and 2021. The proposed classification method is outperformed, with a classification accuracy of 95.27 % and 94.57 % for LISS-III satellite imagery of the years 2011 and 2021 respectively. Further, change detection analysis has been carried out using classified maps and results show a decline of 3.23 sq. km, 22.7 sq. km, and 3.83 sq. km in the areas covered by vegetation, agricultural land, and forest area, respectively. In other classes, such as built-up, water bodies, and barren land, an increase in land cover was observed by 5.59 sq. km, 1.99 sq. km, and 20.92 sq. km, respectively.

TMLNet: Triad Multitask Learning Network for multiobjective based change detection

Change detection is an essential computer vision task in remote sensing applications. It faces challenges of image registration errors, variation in image capturing conditions, clouds, etc. It is observed that an accurate change detection task requires effective feature learning, which needs a noiseless representation. Moreover, change detection improves with error-free image reconstruction as an auxiliary task, which needs joint feature representation by a feature extractor. In this work, we proposed a novel triad (which is a combination of input images and its difference) learning based multiresolution architecture TMLNet for effective change detection. We also proposed a multi-context local self-attention module to efficiently calculate long-range pixel relations with multiple contexts. An enhanced backbone module with top-down connections and multi-scale channel and spatial attention is utilized for change map generation. It provides less noisy features extracted through the backbone. Laplacian pyramid loss is used to preserve small details in feature reconstruction. A set of comprehensive experimentations reveals that the proposed scheme achieved the state-of-the-art result for the F1 Score, intersection over union, and overall accuracy values in seven benchmark datasets. Our model code is available at https://github.com/chouhan-avinash/TMLNet.

RFL-CDNet: Towards accurate change detection via richer feature learning

Change Detection is a crucial but extremely challenging task of remote sensing image analysis, and much progress has been made with the rapid development of deep learning. However, most existing deep learning-based change detection methods mainly focus on intricate feature extraction and multi-scale feature fusion, while ignoring the insufficient utilization of features in the intermediate stages, thus resulting in sub-optimal results. To this end, we propose a novel framework, named RFL-CDNet, that utilizes richer feature learning to boost change detection performance. Specifically, we first introduce deep multiple supervision to enhance intermediate representations, thus unleashing the potential of backbone feature extractor at each stage. Furthermore, we design the Coarse-To-Fine Guiding (C2FG) module and the Learnable Fusion (LF) module to further improve feature learning and obtain more discriminative feature representations. The C2FG module aims to seamlessly integrate the side prediction from previous coarse-scale into the current fine-scale prediction in a coarse-to-fine manner, while LF module assumes that the contribution of each stage and each spatial location is independent, thus designing a learnable module to fuse multiple predictions. Experiments on several benchmark datasets show that our proposed RFL-CDNet achieves state-of-the-art performance on WHU cultivated land dataset and CDD dataset, and the second best performance on WHU building dataset. The source code and models are publicly available at https://github.com/Hhaizee/RFL-CDNet.

MDANet: A High-Resolution City Change Detection Network Based on Difference and Attention Mechanisms under Multi-Scale Feature Fusion

In the domains of geographic information systems and remote sensing image analysis, change detection is vital for examining surface variations in high-resolution remote sensing pictures. However, the intricate texture characteristics and rich details found in high-resolution remote sensing photos are difficult for conventional change detection systems to deal with. Target misdetection, missed detections, and edge blurring are further problems with current deep learning-based methods. This research proposes a high-resolution city change detection network based on difference and attention mechanisms under multi-scale feature fusion (MDANet) to address these issues and improve the accuracy of change detection. First, to extract features from dual-temporal remote sensing pictures, we use the Siamese architecture as the encoder network. The Difference Feature Module (DFM) is employed to learn the difference information between the dual-temporal remote sensing images. Second, the extracted difference features are optimized with the Attention Refinement Module (ARM). The Cross-Scale Fusion Module (CSFM) combines and enhances the optimized attention features, effectively capturing subtle differences in remote sensing images and learning the finer details of change targets. Finally, thorough tests on the BTCDD dataset, LEVIR-CD dataset, and CDD dataset show that the MDANet algorithm performs at a cutting-edge level.

T-UNet: triplet UNet for change detection in high-resolution remote sensing images

ABSTRACT Remote sensing image change detection aims to identify differences between images acquired at different times in the same area, crucial for land management, environmental monitoring, and disaster assessment. Current change detection methods mainly use Siamese or early fusion structures. Siamese networks focus on object features at different times but lack attention to change information, leading to false alarms and missed detections. Early fusion structures focus on fused features but neglect temporal object features, hindering accurate change detection. To address these issues, we propose a novel network, Triplet UNet (T-UNet), employing a triplet encoder to simultaneously extract object features and change features between the pre- and post-time-phase images. To effectively interact features extracted from the three branches of triplet encoder, we propose a multi-branch spatial-spectral cross-attention module. In the decoder, we employ channel attention and spatial attention mechanisms to fully mine and integrate detailed texture and semantic localization information. The proposed T-UNet surpasses seven other state-of-the-art methods on three publicly available datasets. Extensive experiments verify the effectiveness of the proposed structure and modules as well as the superiority of the proposed T-UNet. The source code for the proposed T-UNet is accessible at https://github.com/Pl-2000/T-UNet.

HSAA-CD: A Hierarchical Semantic Aggregation Mechanism and Attention Module for Non-Agricultural Change Detection in Cultivated Land

Cultivated land plays a fundamental role in the sustainable development of the world. Monitoring the non-agricultural changes is important for the development of land-use policies. A bitemporal image transformer (BIT) can achieve high accuracy for change detection (CD) tasks and also become a key scientific tool to support decision-making. Because of the diversity of high-resolution RSIs in series, the complexity of agricultural types, and the irregularity of hierarchical semantics in different types of changes, the accuracy of non-agricultural CD is far below the need for the management of the land and for resource planning. In this paper, we proposed a novel non-agricultural CD method to improve the accuracy of machine processing. First, multi-resource surveying data are collected to produce a well-tagged dataset with cultivated land and non-agricultural changes. Secondly, a hierarchical semantic aggregation mechanism and attention module (HSAA) bitemporal image transformer method named HSAA-CD is performed for non-agricultural CD in cultivated land. The proposed HSAA-CD added a hierarchical semantic aggregation mechanism for clustering the input data for U-Net as the backbone network and an attention module to improve the feature edge. Experiments were performed on the open-source LEVIR-CD and WHU Building-CD datasets as well as on the self-built RSI dataset. The F1-score, intersection over union (IoU), and overall accuracy (OA) of these three datasets were 88.56%, 84.29%, and 68.50%; 79.84%, 73.41%, and 59.29%; and 98.83%, 98.39%, and 93.56%, respectively. The results indicated that the proposed HSAA-CD method outperformed the BIT and some other state-of-the-art methods and proved to be suitable accuracy for non-agricultural CD in cultivated land.

Unsupervised Change Detection in HR Remote Sensing Imagery Based on Local Histogram Similarity and Progressive Otsu

Unsupervised change detection of land cover in multispectral satellite remote sensing images with a spatial resolution of 2–5 m has always been a challenging task. This paper presents a method of detecting land cover changes in high-spatial-resolution remote sensing imagery. This method has three characteristics: (1) Extended center-symmetric local binary pattern (XCS-LBP) is used to extract image features to emphasize spatial context information in initial change detection. Then, spectral information is combined to improve the accuracy of change detection. (2) The local histogram distance of XCS-LBP features is used as the change vector to improve the expression of change information. (3) A progressive Otsu method is developed for threshold segmentation of the change vector to reduce the false detection rate. Four datasets with different landscape complexities and seven state-of-the-art unsupervised change detection methods were used to test the performance of the proposed method. Quantitative results showed that the proposed method reduced the false detection rate and improved the accuracy of the detection of land cover changes. The F1 score achieved by the proposed method reached 0.8688, 0.8867, 0.7725, and 0.6634, respectively, which are higher than the highest corresponding F1 score achieved by the benchmark methods (0.8533, 0.8549, 0.6545, and 0.5895, respectively).

Tracking the dynamics of tidal wetlands with time-series satellite images in the Yangtze River Estuary, China

ABSTRACT Tidal wetlands provide a variety of ecosystem services to coastal communities but suffer severe losses due to anthropogenic activities in the Yangtze River Estuary (YRE). However, the detailed dynamics of tidal wetlands have not been well studied with sufficient spatiotemporal resolution. Here, we proposed a rapid classification method that integrates the COntinuous monitoring of Land Disturbance (COLD) algorithm and Median Composite (MC) based on the dense Landsat time series to track the dynamic processes of tidal wetlands in the YRE from 1990 to 2020. The results showed that the COLD-MC demonstrated remarkable effectiveness in detecting the change of tidal wetlands and excellent overall accuracy and kappa coefficient ranging from 90% to 96% and 0.89–0.95, respectively. The overall accuracy of change detection was 97% with an absolute error of 0.4 years. We found that the total area of tidal wetlands experienced a net loss of 59.75 km2 in the YRE, but the gain and loss of the study period were 1556.07 and 1615.82 km2, respectively. Land reclamation, sediment reduction, and Spartina alterniflora invasion pose significant threats to tidal wetlands. Sustainable management could be implemented through the establishment of nature reserves and ecological sediment enhancement engineering projects.

BiLSTM Classifier Model for Land Cover Change Detection and Classification

Objective: This research proposes a change detection in the satellite images and land cover analysis using a Bidirectional Long Short-Term Memory (BiLSTM) model and vegetation index-based feature maps for future map generation. Change detection complements land cover analysis by identifying and quantifying alterations in land cover over time. Theoretical framework: Land cover analysis and land cover change detection plays crucial role in understanding and monitoring the Earth's surface.Despite its importance, land cover analysis and change detection face several challenges. One major challenge is obtaining accurate and up-to-date information on land cover and change detection across large areas can be complex and costly. Inconsistent data sources and limited access to historical records can hinder the accuracy and reliability of change detection analyses.These challenges requires a combination of technological advancements, improved data availability, refined algorithms, robust validation approaches, and interdisciplinary collaborations. Method: BiLSTM method is used for implementation which is powerful for land cover classification, as it allowing the integration of spatial and temporal information and capturing complex patterns in satellite imagery data. BiLSTM, tend to be more complex but they often offer higher accuracy and the ability to learn intricate patterns and representations. Results and conclusion: The six vegetation index-based feature maps are considered.Therefore, the resulting accuracy is also determined using the Flamingo-Hyena optimization (FHO), and the experimental outcomes disclosed that the proposed model is superior to the existing model in terms of accuracy with 0.95%, MSE with 0.05%, precision with 0.94%, Recall with 0.94%, and F1 measure with 0.94% respectively. vegetation index-based feature maps are essential for land cover analysis and change detection as they provide valuable information on vegetation dynamics, ecological processes, land management, and climate change impacts. Research implications: This process land cover analysis and change detection helps to detect deforestation, urban expansion, agricultural expansion, and other land cover changes. By harnessing these techniques, policy makers can address emerging issues such as deforestation, loss of biodiversity, and encroachment on natural habitats.

A fast hyperspectral change detection algorithm for agricultural crops based on low-rank matrix and morphological feature extraction

Crop change detection study is the foundation of agricultural sustainability. The inherent high spectral resolution of hyperspectral images, combined with multi-temporal datasets, facilitates the detection of subtle changes. To enhance the accuracy and applicability of hyperspectral change detection in agricultural scenes, this paper introduces a fast hyperspectral change detection approach for agricultural crops based on low-rank matrix and morphological feature extraction (FLRaMF). The goal is to improve detection precision and computational efficiency of the change detection process. The method initially employs rapid low-rank matrix extraction to separate changing and non-changing pixels in the spectral domain. Subsequently, spatial information is introduced using attribute profiles, restricting spectral anomalies through hyperspectral morphology, which ultimately improves the detection results. This study utilized four hyperspectral change detection datasets in agricultural crop scenarios, optimizing and analyzing parameters. Experimental results and analysis indicate that the FLRaMF method can achieve higher detection accuracy with reduced computation cost in unsupervised, default parameter scenarios when performing agricultural crop change detection tasks.

Relationship between the variations in glacier features classified on a large scale with climate variables: a case study of Gangotri Glacier.

Changes in glacier area, glacial lakes, debris cover, and geomorphological features such as debris fans have a significant impact on glacial dynamics. Therefore, precise and timely observation and tracking of glacier surface changes is a necessity. The availability of high spatial resolution remote sensing images has made it viable to analyse the glacier surface changes at a local level. However, with an increase in spatial resolution, the spectral variability increases, giving rise to additional challenges (such as false changes and misregistration) in the change detection process. These challenges can preferably be dealt with using an object-based change detection (OBCD) approach rather than the conventional pixel-based change detection approach. Therefore, this study has proposed an OBCD methodology using high-spatial-resolution remote sensing images to detect changes in glacier features. Variability in glacier features has been further analysed by associating it with important climate variables, that is, air temperature and precipitation. As a case study, the changes in Gangotri Glacier (Uttarakhand Himalayas in India) features have been studied using high-spatial-resolution WorldView-2 and Linear Imaging Self-Scanning System (LISS)-4 images for a 3-year period 2011-2014. The spectral correspondences between glacier surface and non-glacier surface have been handled by considering brightness temperature and slope as ancillary data to improvise their distinction. A change detection accuracy of ~ 84% has been obtained using the OBCD approach. Results further show that the variations in glacier features are in congruence with the climatic observations.