High-resolution Optical Tomography Research Articles

Characteristics and utility of high-resolution optical coherence microscopy images of endocervical canal lesions.

To investigate optical coherence microscopy (OCM) imaging features and the application value of these high-resolution images for identifying endocervical canal lesions (ECLs), which is a clinical dilemma in cervical cancer screening programs. In total, 520 OCM images were obtained by scanning the cervical canal lesions with an ultra-high-resolution OCM system (204 specimens from 73 patients). The OCM morphologic characteristics of ECLs were observed and summarized, and then 3 researchers performed a diagnostic test of OCM images of cervical canal lesions. The accuracy, sensitivity, specificity, positive predictive value, negative predictive value, 95% confidence interval of each parameter, and interinvestigator agreement (κ) were calculated. Normal endocervix, cysts, squamous metaplasia, high-grade squamous intraepithelial lesions involving glands, and invasive carcinoma had distinct OCM characteristics, which correlated well with corresponding H&E histologic sections. The accuracy, sensitivity, and specificity of the 3 researchers were 90.6%, 89.3% (95% CI, 86.5%-91.7%) and 91.6% (95% CI, 89.2%-93.5%), respectively. The positive predictive value was 90.1% (95% CI, 87.3%-92.4%), and the negative predictive value was 90.9% (95% CI, 88.5%-92.9%), with almost perfect agreement (κ = 0.874). The application of the OCM system in cervical canal lesions is feasible and could help improve detection of occult ECLs in cervical cancer screening programs. This study lays the foundation for further research on OCM in cervical canal lesions in vivo, which also has a potential impact on projecting pathologic evaluation beyond what is currently possible, perhaps globally.

Detailed Landslide Traces Database of Hancheng County, China, Based on High-Resolution Satellite Images Available on the Google Earth Platform

Hancheng is located in the eastern part of China’s Shaanxi Province, near the west bank of the Yellow River. It is located at the junction of the active geological structure area. The rock layer is relatively fragmented, and landslide disasters are frequent. The occurrence of landslide disasters often causes a large number of casualties along with economic losses in the local area, seriously restricting local economic development. Although risk assessment and deformation mechanism analysis for single landslides have been performed for landslide disasters in the Hancheng area, this area lacks a landslide traces database. A complete landslide database comprises the basic data required for the study of landslide disasters and is an important requirement for subsequent landslide-related research. Therefore, this study used multi-temporal high-resolution optical images and human-computer interaction visual interpretation methods of the Google Earth platform to construct a landslide traces database in Hancheng County. The results showed that at least 6785 landslides had occurred in the study area. The total area of the landslides was about 95.38 km2, accounting for 5.88% of the study area. The average landslide area was 1406.04 m2, the largest landslide area was 377,841 m2, and the smallest landslide area was 202.96 m2. The results of this study provides an important basis for understanding the spatial distribution of landslides in Hancheng County, the evaluation of landslide susceptibility, and local disaster prevention and mitigation work.

Compressive sensing-based multi-focus line-scanning two-photon microscopy for fast 3D imaging

Fast 3D volume imaging methods have been playing increasingly important roles in biological studies. In this article, we present the design and characterization of a multi-focus line-scanning two-photon microscope. Specifically, a digital micromirror device (DMD) is employed to generate a randomly distributed focus array on a plane (i.e., x-z) via binary holography. Next, a galvanometric mirror scans the focus array in a direction normal to the plane (i.e., y-axis) over the imaging volume. For sparse samples, e.g., neural networks in a brain, 1-3 foci are used together with compressive sensing algorithm to achieve a volume imaging rate of 15.5 volumes/sec over 77 × 120 × 40 µm3. High-resolution optical cross-sectional images on selected planes and regions can be generated by sequentially scanning the laser focus generated on the x-z plane with good imaging speeds (e.g., 107 frames/sec over 80 × 120 × 40 µm3). In the experiments, microbeads, pollens, and mouse brain slices have been imaged to characterize the point spread function and volume image rate and quality at different sampling ratios. The results show that the multi-focus line-scanning microscope presents a fast and versatile 3D imaging platform for deep tissue imaging and dynamic live animal studies.

Discontinuous Surface Ruptures and Slip Distributions in the Epicentral Region of the 2021 Mw7.4 Maduo Earthquake, China

Discontinuous Surface Ruptures and Slip Distributions in the Epicentral Region of the 2021 Mw7.4 Maduo Earthquake, China

Nd3+-sensitized multilayered rare-earth nanocrystals with enhanced NIR-IIb luminescence for high resolution optical imaging

Luminescence imaging in the second near-infrared (NIR-II) window, particularly within the NIR-IIb (1500–1700 nm) sub-window, has been utilized for both fundamental research and clinical applications due to its exceptional spatiotemporal resolution. Nevertheless, efficient contrast agents designed for NIR-IIb imaging are still scarce. Herein, a multilayered rare-earth nanocrystal (NaYF4:Yb,Er,Ce@NaYF4:Yb,Nd@NaYF4) with strong NIR-IIb luminescence was designed and synthesized. The doping of Ce3+ ions in the core effectively suppresses the upconversion and boosts the downconversion luminescence by populating the Er 4I13/2 level. Additionally, by separating the sensitizer (Nd3+) and the activator (Er3+) in different layers, we are able to maximize the light absorption around 800 nm and reduce the cross relaxations between rare-earth elements, which leads to a strong downconversion emission around 1530 nm under 808 nm excitation. After passivation with NaYF4 shell and phase transfer with highly PEGylated mesoporous silica layer, the nanocomposites with excellent colloidal stability and bright NIR-IIb emission were obtained. With long blood circulation time in vivo, the fabricated nanocomposites enable high resolution and contrast luminescence imaging of the blood vessels and tumors in living mice. Moreover, the rare-earth nanocrystals with superior optical properties also hold great promise for various applications in optical sensing and biomedical imaging.

An MR-based brain template and atlas for optical projection tomography and light sheet fluorescence microscopy in neuroscience.

Optical Projection Tomography (OPT) and light sheet fluorescence microscopy (LSFM) are high resolution optical imaging techniques, ideally suited for ex vivo 3D whole mouse brain imaging. Although they exhibit high specificity for their targets, the anatomical detail provided by tissue autofluorescence remains limited. T1-weighted images were acquired from 19 BABB or DBE cleared brains to create an MR template using serial longitudinal registration. Afterwards, fluorescent OPT and LSFM images were coregistered/normalized to the MR template to create fusion images. Volumetric calculations revealed a significant difference between BABB and DBE cleared brains, leading to develop two optimized templates, with associated tissue priors and brain atlas, for BABB (OCUM) and DBE (iOCUM). By creating fusion images, we identified virus infected brain regions, mapped dopamine transporter and translocator protein expression, and traced innervation from the eye along the optic tract to the thalamus and superior colliculus using cholera toxin B. Fusion images allowed for precise anatomical identification of fluorescent signal in the detailed anatomical context provided by MR. The possibility to anatomically map fluorescent signals on magnetic resonance (MR) images, widely used in clinical and preclinical neuroscience, would greatly benefit applications of optical imaging of mouse brain. These specific MR templates for cleared brains enable a broad range of neuroscientific applications integrating 3D optical brain imaging.

Fly-scan high-throughput coded ptychographic microscopy via active micro-vibration and rolling-shutter distortion correction.

Recent advancements in ptychography have demonstrated the potential of coded ptychography (CP) for high-resolution optical imaging in a lensless configuration. However, CP suffers imaging throughput limitations due to scanning inefficiencies. To address this, we propose what we believe is a novel 'fly-scan' scanning strategy utilizing two eccentric rotating mass (ERM) vibration motors for high-throughput coded ptychographic microscopy. The intrinsic continuity of the 'fly-scan' technique effectively eliminates the scanning overhead typically encountered during data acquisition. Additionally, its randomized scanning trajectory considerably reduces periodic artifacts in image reconstruction. We also developed what we believe to be a novel rolling-shutter distortion correction algorithm to fix the rolling-shutter effects. We built up a low-cost, DIY-made prototype platform and validated our approach with various samples including a resolution target, a quantitative phase target, a thick potato sample and biospecimens. The reported platform may offer a cost-effective and turnkey solution for high-throughput bio-imaging.

Extraction of Coastal Levees Using U-Net Model with Visible and Topographic Images Observed by High-Resolution Satellite Sensors.

Coastal levees play a role in protecting coastal areas from storm surges and high waves, and they provide important input information for inundation damage simulations. However, coastal levee data with uniformity and sufficient accuracy for inundation simulations are not always well developed. Against this background, this study proposed a method to extract coastal levees by inputting high spatial resolution optical satellite image products (RGB images, digital surface models (DSMs), and slope images that can be generated from DSM images), which have high data availability at the locations and times required for simulation, into a deep learning model. The model is based on U-Net, and post-processing for noise removal was introduced to further improve its accuracy. We also proposed a method to calculate levee height using a local maximum filter by giving DSM values to the extracted levee pixels. The validation was conducted in the coastal area of Ibaraki Prefecture in Japan as a test area. The levee mask images for training were manually created by combining these data with satellite images and Google Street View, because the levee GIS data created by the Ibaraki Prefectural Government were incomplete in some parts. First, the deep learning models were compared and evaluated, and it was shown that U-Net was more accurate than Pix2Pix and BBS-Net in identifying levees. Next, three cases of input images were evaluated: (Case 1) RGB image only, (Case 2) RGB and DSM images, and (Case 3) RGB, DSM, and slope images. Case 3 was found to be the most accurate, with an average Matthews correlation coefficient of 0.674. The effectiveness of noise removal post-processing was also demonstrated. In addition, an example of the calculation of levee heights was presented and evaluated for validity. In conclusion, this method was shown to be effective in extracting coastal levees. The evaluation of generalizability and use in actual inundation simulations are future tasks.

Integrating bi-temporal VHR optical and long-term SAR images for built-up area change detection

ABSTRACT With the rapid expansion of urbanization, it is imperative to monitor built-up areas changes to promote the sustainable development of cities, aligning with the goals of Sustainable Development Goal 11(SDG 11). Remote sensing big data is valuable for automatically mapping these changes. Bi-temporal very high-resolution (Bi-VHR) optical images have been widely utilized for fine-grained change detection (CD). However, the significant spatiotemporal inconsistency due to imaging conditions and seasonal variations poses challenges for VHR optical CD. Unlike optical images, synthetic aperture radar (SAR) images are unaffected by atmospheric interference and provide robust spatiotemporal features as a supplement. Previous CD algorithms with SAR overlooked the exploration of long-term features of time series. In this study, we propose a novel CD framework combining long-term SAR with Bi-VHR images. It incorporates a spatial-frequency learning module to enhance SAR temporal features and a multisource feature fusion module to adaptively fuse the heterogeneous features. The experiments are conducted on OS-BCD dataset, which is the first dataset specifically designed for this task. The results demonstrate that our proposal outperforms advanced CD methods with F1 score, IoU and OA of 64.99%, 48.13%, and 95.11%, validating the efficacy of our proposal in accurately detecting changes in built-up areas.

Ground subsidence and polygon development due to thermokarst in the Lena-Aldan interfluve, eastern Siberia, revealed by satellite remote sensing data

Thermokarst development is a topographic change in the landscape that is commonly associated with permafrost degradation in ice-rich permafrost regions. The Lena-Aldan interfluvial area in Central Yakutia in eastern Siberia has undergone extensive thermokarst development in the last three decades, particularly in the vicinity of settlements. Despite the negative effects of thermokarst development on the inhabitants of these settlements, no quantitative observation methods have been developed to investigate the surface displacement due to thermokarst development over entire towns. This study utilized interferometric synthetic aperture radar to reveal ground-surface displacement associated with thermokarst near the settlements of selected towns. The findings showed that significant subsidence was detected in disturbed areas (farming and abundant arable land) near the towns. The magnitude of subsidence in the Tyungyulyu and Mayya areas was less than that in Churapcha and Amga. Polygon density in a defined area in each town was examined using high-resolution optical images. The polygon density in Churapcha was considerably lower than that in Mayya, whereas polygonal texture in some areas in Tyungyulyu and Amga was unclear. Spatial frequency analysis using satellite optical images showed clear differences in averaged spectrum models between well-developed and less-developed polygons, which may reflect trough depths and density of vegetation between polygons. Satellite-based subsidence maps and statistics describing polygon development may be useful for evaluating both initial and subsequent stages of thermokarst development.

Needle of longitudinally polarized light using the circular Airy beam.

An optical needle is created using a radially polarized circular Airy beam with a conical angle, stemmed from the auto-focusing property of light beams. The utilization of the angular spectrum representation serves to illustrate the field distributions of the optical needle, and an explicit formula is provided to describe the angular spectrum of the light beam. The findings suggest that the optical needle exhibits a long depth of focus and well uniformity, and the full width at half maximum of the transverse field distribution is approximately 0.38 λ beyond the diffraction limit. The uniformity of the optical needle can be tailored by adjusting the width of the primary ring, the decay parameter, and the conical angle. Additionally, the depth of focus of the optical needle significantly improves as the radius of the primary ring increases while still maintaining well uniformity. It may find applications in high-resolution optical imaging and optical manipulation.

Three-Dimensional Optical Imaging of Internal Deformations in Polymeric Microscale Mechanical Metamaterials.

Recent advances in two-photon polymerization fabrication processes are paving the way to creating macroscopic metamaterials with microscale architectures, which exhibit mechanical properties superior to their bulk material counterparts. These metamaterials typically feature lightweight, complex patterns such as lattice or minimal surface structures. Conventional tools for investigating these microscale structures, such as scanning electron microscopy, cannot easily probe the internal features of these structures, which are critical for a comprehensive assessment of their mechanical behavior. In turn, we demonstrate an optical confocal microscopy-based approach that allows for high-resolution optical imaging of internal deformations and fracture processes in microscale metamaterials under mechanical load. We validate this technique by investigating an exemplary metamaterial lattice structure of 80 × 80 × 80 μm3 in size. This technique can be extended to other metamaterial systems and holds significant promise to enhance our understanding of their real-world performance under loading conditions.

Assessment of Mechanically Induced Changes in Helical Fiber Microstructure Using Diffusion Tensor Imaging.

Noninvasive methods to detect microstructural changes in collagen-based fibrous tissues are necessary to differentiate healthy from damaged tissues in vivo but are sparse. Diffusion Tensor Imaging (DTI) is a noninvasive imaging technique used to quantitatively infer tissue microstructure with previous work primarily focused in neuroimaging applications. Yet, it is still unclear how DTI metrics relate to fiber microstructure and function in musculoskeletal tissues such as ligament and tendon, in part because of the high heterogeneity inherent to such tissues. To address this limitation, we assessed the ability of DTI to detect microstructural changes caused by mechanical loading in tissue-mimicking helical fiber constructs of known structure. Using high-resolution optical and micro-computed tomography imaging, we found that static and fatigue loading resulted in decreased sample diameter and a re-alignment of the macro-scale fiber twist angle similar with the direction of loading. However, DTI and micro-computed tomography measurements suggest microstructural differences in the effect of static versus fatigue loading that were not apparent at the bulk level. Specifically, static load resulted in an increase in diffusion anisotropy and a decrease in radial diffusivity suggesting radially uniform fiber compaction. In contrast, fatigue loads resulted in increased diffusivity in all directions and a change in the alignment of the principal diffusion direction away from the constructs' main axis suggesting fiber compaction and microstructural disruptions in fiber architecture. These results provide quantitative evidence of the ability of DTI to detect mechanically induced changes in tissue microstructure that are not apparent at the bulk level, thus confirming its potential as a noninvasive measure of microstructure in helically architected collagen-based tissues, such as ligaments and tendons.

Ice floe segmentation and floe size distribution in airborne and high-resolution optical satellite images: towards an automated labelling deep learning approach

Abstract. Floe size distribution (FSD) has become a parameter of great interest in observations of sea ice because of its importance in affecting climate change, marine ecosystems, and human activities in the polar ocean. A most effective way to monitor FSD in the ice-covered regions is to apply image processing techniques to airborne and satellite remote sensing data, where the segmentation of individual ice floes is a challenge in obtaining FSD from remotely sensed images. In this study, we adopt a deep learning (DL) semantic segmentation network to fast and adaptive implement the task of ice floe instance segmentation. In order to alleviate the costly and time-consuming data annotation problem of model training, classical image processing technique is applied to automatically label ice floes in local-scale marginal ice zone (MIZ). Several state-of-the-art (SoA) semantic segmentation models are then trained on the labelled MIZ dataset and further applied to additional large-scale optical Sentinel-2 images to evaluate their performance in floe instance segmentation and to determine the best model. A post-processing algorithm is also proposed in our work to refine the segmentation. Our approach has been applied to both airborne and high-resolution optical (HRO) satellite images to derive acceptable FSDs at local and global scales.

A rapid high-resolution multi-sensory urban flood mapping framework via DEM upscaling

Urban floods can cause severe loss of economic and social assets, and remote sensing has been an effective tool for flood mapping during disaster response. Due to the complexity of high-density urban structures, high-resolution (HR) optical images can only extract visible floods in open spaces, and floods in shadows and under the canopy are challenging to map. Accurate digital elevation models (DEMs) are essential for inundation estimation towards urban flood mapping, but HR DEMs are often unavailable due to the high acquisition costs. Through DEM upscaling, HR DEMs could be obtained from existing low-resolution (LR) DEMs using deep learning. To this end, a novel multi-sensory HR urban flood mapping framework is proposed in this research. The framework consists of three components: 1) a new DEM upscaling network to infer HR DEMs from existing LR DEMs with a fusion approach, 2) a rapid flood segmentation network to extract visible flood from very-high-resolution (VHR) optical images with limited human labelling, and 3) an accurate Geographical Information System (GIS)-based tool for floodwater extent and depth estimation from the visible flood information along with HR DEMs. The proposed framework was validated on a fluvial flood that occurred in Calgary, Canada, in 2013, where the proposed DEM upscaling network produced an upscaled HR DEM at 2 m resolution from an existing LR DEM at 18 m resolution. In addition, the proposed flood segmentation network has shown accurate visible flood extraction from VHR RGB aerial imagery with over 80% intersection-over-union (IoU) using 10% of human labelling as training samples. Finally, the floodwater extent and floodwater depth estimation using the proposed GIS tool showed significant improvement over conventional flood mapping methods.

A Method for Extracting Photovoltaic Panels from High-Resolution Optical Remote Sensing Images Guided by Prior Knowledge

The extraction of photovoltaic (PV) panels from remote sensing images is of great significance for estimating the power generation of solar photovoltaic systems and informing government decisions. The implementation of existing methods often struggles with complex background interference and confusion between the background and the PV panels. As a result, the completeness and edge clarity of PV panel extraction results are compromised. Moreover, most previous studies have overlooked the unique color characteristics of PV panels. To alleviate these deficiencies and limitations, a method for extracting photovoltaic panels from high-resolution optical remote sensing images guided by prior knowledge (PKGPVN) is proposed. Firstly, aiming to address the problems related to missed extractions and background misjudgments, a Photovoltaic Index (PVI) based on visible images in the three-band is constructed to serve as prior knowledge to differentiate between PV panels and non-PV panels. Secondly, in order to strengthen information interaction between shallow features and deep features and enhance the accuracy and integrity of results, a Residual Convolution Hybrid Attention Module (RCHAM) is introduced into the skip-connection of the encoding–decoding structure. Finally, for the purpose of reducing the phenomenon of blurred edges, a multilevel Feature Loss (FL) function is designed to monitor the prediction results at different scales. Comparative experiments are conducted with seven methods, including U-Net, on publicly available datasets. The experimental results show that our PKGPVN achieves superior performance in terms of evaluation metrics such as IoU (above 82%), Precision (above 91%), Recall (above 89%), and F1-score (above 90%) on the AIR-PV dataset. Additionally, the ablation experiments illustrate the effectiveness of our key parts. The proposed method reduces the phenomena of missed extractions and background misjudgments effectively while producing highly accurate results with clear boundaries.

Screening Image Features of Collapsed Buildings for Operational and Rapid Remote Sensing Identification

The accurate detection of collapsed buildings is of great significance for post-disaster rescue and reconstruction. High-resolution optical images are important data sources for identifying collapsed buildings, and the identification accuracy mainly depends on the features extracted from the images. However, existing research lacks a comprehensive screening and general evaluation of the ability of remote sensing features to detect collapsed buildings, and there is still a considerable gap in the operational process of rapid identification of collapsed buildings in remote sensing. Based on 2630 pairs of building samples distributed in 6 regions worldwide, this study evaluated the ability of 25 remote sensing features (including spectral and spatial features) to detect collapsed buildings and select the most capable ones. Then, we test the application effect of selected features in identifying collapsed buildings on large-scale remote sensing images. Based on the two experiments above, an operational process for rapid identification of collapsed buildings was suggested. The result shows that Homogeneity, Energy, Local Entropy, Local Standard Deviation, and Gradient can effectively and stably distinguish collapsed buildings from non-collapsed buildings (Jeffries-Matusita distances are greater than 1.59 and Transformed Divergences are greater than 1.60) and have high recognition accuracy for collapsed buildings on large-scale remote sensing images (F1-scores are 0.71–0.94). In addition, Contrast, Local Coefficient of Variation, Edge Density, and Global Entropy can also distinguish collapsed buildings from non-collapsed buildings at a normal level (Jeffries-Matusita distances are 1.14–1.28, and Transformed Divergences are 1.24–1.48), while Gradient Orientation Entropy, Fractal Dimension, Local Binary Patterns, Edge, Local Mean, Correlation, Gradient Orientation Standard Deviation, Global Coefficient of Variation, Gabor feature, Local Moran’I, and six spectral features have relatively weak abilities (Jeffries-Matusita distances are less than 0.73, and Transformed Divergences are less than 1.07). The selected remote sensing features can support rapid identification of potential collapsed building areas from post-disaster remote sensing images.

Comparative Analysis of Pixel-Level Fusion Algorithms and a New High-Resolution Dataset for SAR and Optical Image Fusion

Synthetic aperture radar (SAR) and optical images often present different geometric structures and texture features for the same ground object. Through the fusion of SAR and optical images, it can effectively integrate their complementary information, thus better meeting the requirements of remote sensing applications, such as target recognition, classification, and change detection, so as to realize the collaborative utilization of multi-modal images. In order to select appropriate methods to achieve high-quality fusion of SAR and optical images, this paper conducts a systematic review of current pixel-level fusion algorithms for SAR and optical image fusion. Subsequently, eleven representative fusion methods, including component substitution methods (CS), multiscale decomposition methods (MSD), and model-based methods, are chosen for a comparative analysis. In the experiment, we produce a high-resolution SAR and optical image fusion dataset (named YYX-OPT-SAR) covering three different types of scenes, including urban, suburban, and mountain. This dataset and a publicly available medium-resolution dataset are used to evaluate these fusion methods based on three different kinds of evaluation criteria: visual evaluation, objective image quality metrics, and classification accuracy. In terms of the evaluation using image quality metrics, the experimental results show that MSD methods can effectively avoid the negative effects of SAR image shadows on the corresponding area of the fusion result compared with CS methods, while model-based methods exhibit relatively poor performance. Among all of the fusion methods involved in the comparison, the non-subsampled contourlet transform method (NSCT) presents the best fusion results. In the evaluation using image classification, most experimental results show that the overall classification accuracy after fusion is better than that before fusion. This indicates that optical-SAR fusion can improve land classification, with the gradient transfer fusion method (GTF) yielding the best classification results among all of these fusion methods.

Glacial Lake Outburst Flood Monitoring and Modeling through Integrating Multiple Remote Sensing Methods and HEC-RAS

The Shishapangma region, situated in the middle of the Himalayas, is rich in glacial lakes and glaciers. Hence, glacial lake outburst floods (GLOFs) have become a top priority because of the severe threat posed by GLOFs to the downstream settlements. This study presents a comprehensive analysis of GLOF hazards using multi-source remote sensing datasets and designs a flood model considering the different breaching depths and release volumes for the Galong Co region. Based on high-resolution optical images, we derived the expanding lake area and volume of glacial lakes. We monitored deformation velocity and long-term deformation time series around the lake dam with Small BAseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR). The glacier thinning trend was obtained from the difference in the Digital Elevation Model (DEM). We identified potential avalanche sources by combining topographic slope and measurable deformation. We then carried out flood modeling under three different scenarios using the hydrodynamic model HEC-RAS for Galong Co, which is formed upstream of Nyalam. The results show that the Nyalam region is exposed to high-intensity GLOFs in all scenarios. The larger breaching depth and release volumes caused a greater flow depth and peak discharge. Overall, the multiple remote sensing approaches can be applied to other glacial lakes, and the modeling can be used as a basis for GLOF mitigation.

Effect of Urbanization Through Land Coverage Classification

AbstractLand cover changes from rural or natural habitats to impervious surfaces that support various human activities are called urbanization. The evaluation of policy alternatives for future expansion and the promotion of sustainable urban development therefore place great emphasis on the mapping, analysis, and monitoring of land cover changes in metropolitan environments. By assessing changes in relevant environmental indicators at a range of scales, from local to regional, this study seeks to determine the rate of urbanization and analyze its impact on the environment in and around two key cities in Tamilnadu. Chennai and Madurai are the urban areas under investigation. The results are based on classifications of medium to high spatial resolution optical satellite images from 1995 to 2020. Various classification approaches with combinations of spectral, shape, and texture input information were combined to achieve high classification accuracy. Based on the categories, environmental indicators were created and used to calculate the impact of urbanization on the environment. The findings show that each research area experienced varying degrees of urban expansion and environmental impact. Urban areas grew tenfold between 1995 and 2015, according to a study comparing Chennai and Madurai. To evaluate the spatiotemporal dynamics of urbanization and its environmental impact in various metropolitan regions, this research shows the value of combining urban and environmental indicators with remote sensing data.