• Model/Object/ Knowledge Based Image Analysis
• Classification Accuracy Assessment Methods
• Image Segmentation/Classification
• Thematic Mapping Using Remotely Sensed Data
• Multi-Source Remote Sensing Data Reasoning
• Remotely Sensed Data Integration
• DEM Fusion
• New Disciplines in Applied RS
Remote Sensing Image Analysis & processing
Spatial Data Processing and Analysis in GIS
Research Output
Articles
Publication Date: 2025
Journal of the Indian Society of Remote Sensing (09743006)
Choosing an image with a suitable spatial scale is essential for achieving accurate and cost-effective vegetation maps. While previous studies have mainly focused on traditional vegetation indices (VIs), this study aims to evaluate the sensitivity of various VIs to the spatial resolution of satellite images. Six different satellite images with spatial resolutions ranging from 0.5 to 30 m were utilized in three distinct study regions. Out of the available vegetation indices, 14 VIs were selected and computed, resulting in a total of 252 vegetation maps. The resulting vegetation maps from each VI were compared with a ground reference map, and multiple quality measures were computed. Subsequently, the TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) method was employed to rank the VIs for each satellite image based on these measures. The results from the TOPSIS method and correlation analysis on the generated vegetation maps demonstrated that higher resolution imagery leads to improved overall accuracy across all VIs, except that fused data do not have any considerable effect on the accuracy of the vegetation maps. In conclusion, it is reasonable to assert that in some scenarios, medium-resolution images can be utilized instead of high-resolution images to achieve satisfactory accuracy. © Indian Society of Remote Sensing 2025.
Publication Date: 2025
Remote Sensing Applications: Society and Environment (23529385)37
The study explores the feasibility of combining full-waveform LiDAR data with optical imagery to accurately count trees by simultaneously analyzing the structural characteristics of both data types. A new approach was developed to demonstrate the potential of full-waveform LiDAR data in tree counting. The method involved generating a binary vegetation map from an aerial color image and segmenting it. Conventional features for the LiDAR point cloud, and 11 structural features for the LiDAR waveforms were generated. Using generated features, a multi-step filter-out process was applied to remove extra points and identify the number of trees in each segment. The method achieved a tree-counting accuracy of about 94%, indicating reliable results. Further analysis revealed a 17% commission error and an 11% omission error resulted from overestimating and underestimating trees, respectively. To compare the strength of data integration, tree counting was performed independently on waveform LiDAR and optical image independently. However, the accuracy of the results obtained from each data set separately was lower than that obtained from integrating both data sets. Errors were more prevalent in large segments with over five trees or dense tree cover of different shapes and sizes. The integration of full-waveform LiDAR data enhanced accuracy in tree counting, overcoming the limitations of individual data sources. The study highlights the significance of integrating these data types for improved remote sensing applications. © 2025 Elsevier B.V.
Publication Date: 2024
Remote Sensing Applications: Society and Environment (23529385)35
Although Light Detection and Ranging (LiDAR) technology is currently one of the most efficient methods for acquiring high-density point cloud, there are still challenges in terms of data reliability. In particular, the accuracy assessment of LiDAR data, especially in the height component, is one of the main issues in this context. This study introduces a rapid and cost-effective platform to improve the accuracy and precision of LiDAR data by integrating high-density GNSS-Ranging measurements with LiDAR data. The platform offers the capability to rapidly collect a significant number of network real time kinematic (NRTK) points with centimetric precision. A continuous correction surface is proposed to integrate the platform and LiDAR data, resulting in improved accuracy for all ground-class LiDAR data. Evaluation using GNSS benchmarks and NRTK checkpoints showed a significant reduction in LiDAR height errors after applying the correction surface. The root mean squares error (RMSE) decreased from 18.5 cm to 8.2 cm when compared to GNSS benchmarks and from 17.4 cm to 5.3 cm for approximately 1000 NRTK control points. The results indicate that collecting a large number of high-density GNSS ground targets and applying a correction surface to LiDAR height data significantly enhance the accuracy and precision of the LiDAR extracted products. © 2024 Elsevier B.V.
Publication Date: 2023
Geocarto International (17520762)38(1)
Several methods have been developed to detect differences between temporal satellite images for change detection. Image differencing, which is easy to compute and implement, does not require ground-based data. In this study, the performance of 11 other spectral distances was explored in addition to simple differencing for change detection. Moreover, the fusion of these distances was evaluated using various methods, including linear combination, classification, and majority voting. Comparing the results in different study areas showed that Pearson-Correlation and Spearman-Correlation were the most accurate distances. Additionally, the evaluation of the results indicated that the unsupervised fusion of different distances could increase the final accuracy by an average of 10%. Furthermore, the classification of distance images, which had slightly lower accuracy than the post-classification comparison of original images, was more accurate than the fusion of distances using these methods or thresholding the individual distances. © 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.
