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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.
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.
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.
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.
Expert Systems with Applications (09574174) 168
Clustering algorithms are affected by the initial seeds, therefore any improvement of the initialization process can improve the final clustering results. There exist several initialization algorithms that most of them are focused on using the distance and density based metrics defined in the feature space. However image space has a great potential to be used as the search space for initial seeds. In this research, developing clustering initialization using spatial information (image space) and spectral information (feature space) with the help of particle swarm optimization has been examined. Standard deviation and homogeneity of pixels in the image space in addition to distance and density of points in the feature space have been utilized in the objective function of the particle swarm optimization. Two different search spaces (feature and image spaces) and 26 objective functions have been applied to a simulated image and two real satellite multi-spectral images. Comparing the results of 26 cases with four prevailing initialization methods, demonstrated that searching for initial seeds in the image space using PSO with a full objective function (using four spectral–spatial criteria) can produce better results than the other tested cases. Using this case for k-means clustering initialization, led to about 20% improvement in overall accuracy relative to the clustering results with commonly used initialization algorithms. © 2020 Elsevier Ltd
Geocarto International (17520762) 35(12)pp. 1311-1326
Generation of precise digital elevation models (DEMs) from stereo satellite images by using rational polynomial coefficients (RPCs) usually needs several ground control points (GCPs). This is mainly due to RPCs biases. However, since GCPs collection is a time consuming and expensive process, global DEMs (GDEMs), as the most inexpensive geospatial information, can be used to improve stereo satellite imagery-based DEMs (IB-DEMs). In this study, a 2.5 D mutual information based DEM matching, between a GDEM and an IB-DEM, was introduced for bias correction of satellite stereo images. Three well-known 30-meter GDEMs, namely, SRTM, ASTER, and AW3D30, were used and compared to assess the efficiency of this approach. The performance of the proposed method was evaluated by processing the stereo images acquired by CARTOSAT-1 satellite from two regions with flat, hilly, and mountainous topography. Evaluation results revealed that the proposed method could significantly improve the geometric accuracy of IB-DEM using all GDEMs. © 2019 Informa UK Limited, trading as Taylor & Francis Group.
International Journal of Remote Sensing (13665901) 40(12)pp. 4526-4543
RapidEye satellite images with high spatial resolution, affordable prices and having Red-Edge band have high potential for time series issues, especially in vegetation studies. Despite these beneficial properties, RapidEye images with 5 m spatial resolution are not sufficiently useful for some applications. According to this problem, enhancing the spatial resolution of RapidEye images can significantly improve the results of the subsequent processes on these images. Fusion of high spatial resolution with high spectral resolution images is known as an effective way to enhance the quality of multispectral remotely sensed images. Unfortunately, the lack of panchromatic band with high spatial resolution has been faced the procedure of improving the spatial resolution of RapidEye images with major problems. In this paper, we have proposed using the free Google Earth (GE) images which have high spatial resolution and high-coverage of land surface to enhance the spatial information of RapidEye images. A simulated panchromatic image has been generated by three band GE image and with three different methods: Mean, principal component analysis (PCA) and weighted average of GE image bands. In the last method, the weights are extracted from the spectral response curve of the satellite which captured the GE image. The simulated panchromatic image has been utilized for pansharpening of RapidEye image in five well-known methods: Brovey, Gram-Schmidt (GS), intensity-hue-saturation (IHS), Pansharp1 and Pansharp2. The most important point is finding the GE image with lowest lag time with RapidEye image. By satisfying this condition, the experiments illuminated that the proposed method can effectively enhance the spatial quality of RapidEye image. Also, this study presented that Pansharp2 method, which used simulated panchromatic image generated by the spectral response curve information, has revealed the best results of RapidEye image pansharpening. © 2019, © 2019 Informa UK Limited, trading as Taylor & Francis Group.
International Journal of Image and Data Fusion (19479832) 10(3)pp. 199-216
In this study, vegetation changes in the Zayandeh-rud river basin in the period 2001 to 2016 have been investigated based on the combining of the 15 different vegetation indices. Two main plans were applied and tested to produce the final vegetation change map. In the first plan, change maps were produced by differencing the original vegetation indices individually. The second plan, which was a fusion perspective, included two algorithms. In the first one, change maps, obtained from vegetation indices, were fused at the decision level using the Majority Voting Method. The second algorithm included a particle swarm optimisation (PSO) based weighted combination of the different vegetation maps. The results show that the high correlation between vegetation indices does not necessarily provide the same results and combination of all them can be done automatically by applying PSO. Although PSO-based combination could not significantly improve the change detection, it solved the problem of finding optimal threshold value for the change detection process. However, from the vegetation change point of view, they all indicate that during the years of study, the vegetation cover has increased in the study area due to irregular water usage of the Zayandeh-rud river in its western part. © 2019, © 2019 Informa UK Limited, trading as Taylor & Francis Group.
Journal of the Indian Society of Remote Sensing (09743006) 47(9)pp. 1455-1469
Change detection is one of the most important applications in remote sensing. Regarding the long period in data gathering by remote sensing, the potential for change detection of the planet through this technology is high. Designing a remote sensing change detection plan is a big challenge due to the existence of several options regarding methodologies and data. Previous reviews in the literature have mostly focused on the change detection methodology, while the other aspects of a change detection plan are often not studied in detail. The major aspects of a remote sensing change detection plan include subject of the change detection, the time elapsed, the study area, analysis unit, accuracy requirement, and outputs constituting focal points here. These aspects are briefed in the form of eight questions fitted in a change detection process together with related discussions. Finally, a table of the main steps involved in the change detection plan and the outputs of each step thereof is compiled. The present review exhibits that more research is necessary to explore additional aspects of a change detection plan beyond the existing change detection methods. © 2019, Indian Society of Remote Sensing.
IEEE Geoscience and Remote Sensing Letters (1545598X) 14(8)pp. 1368-1372
Rational polynomial coefficients (RPCs) biases and over-fitting phenomenon are two major issues in terrainindependent rational function models. These problems degrade the accuracy of extracted spatial information from very high spatial resolution (VHSR) satellite images. This study particularly focused on overcoming the over-fitting problem through an optimal term selection approach. To this end, multiobjective genetic algorithm was used in order to optimize three effective objective functions: the RMSE of ground control points (GCPs), the number, and the distribution of both RPCs and GCPs. Finally, the technique for order of preference by similarity to ideal solution, as an efficient multicriteria decision-making method, was applied to select the best solution, i.e., the optimum terms of RPCs, through the ranking of solutions in the optimum set. The performance of the proposed method was evaluated by using three VHSR images acquired by GeoEye-1, Worldview-3, and Pleiades satellite sensors. Experimental results show that subpixel accuracy can be nearly achieved in all data sets, when over-fitting problem is addressed. The optimal selected terms leaded to a significant improvement compared to the original RPCs. Indeed, our method, which is independent of GCPs distribution, not only requires a small number of GCPs, but also leads to a 30% to 75% improvement when compared to the original RPCs. This improvement in VHSR images, usually makes no more need to remove the RPCs biases. © 2017 IEEE.
Fatemi nasrabadi, S.B. ,
Mirzadeh, S.M.J. ,
Alizadeh naeini, A. ,
Mirzadeh, S.M.J. ,
Alizadeh naeini, A. ,
Fatemi nasrabadi, S.B. CTIT workshop proceedings series (16821750) 42(4W4)pp. 173-177
In this research, the influence of stack number (STKN) on the accuracy of Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global DEM (GDEM) has been investigated. For this purpose, two data sets of ASTER and Reference DEMs from two study areas with various topography (Bomehen and Tazehabad) were used. The Results show that in both study areas, STKN of 19 results in minimum error so that this minimum error has small difference with other STKN. The analysis of slope, STKN, and error values shows that there is no strong correlation between these parameters in both study areas. For example, the value of mean absolute error increase by changing the topography and the increase of slope values and height on cells but, the changes in STKN has no important effect on error values. Furthermore, according to high values of STKN, effect of slope on elevation accuracy has practically decreased. Also, there is no great correlation between the residual and STKN in ASTER GDEM.
Journal of the Indian Society of Remote Sensing (09743006) 44(4)pp. 643-650
One of the disadvantages of the k-means clustering method lies in its dependence on the position of initial centers. Various methods presented so far, have emphasized the use of distance and density of points in the feature space as the most important features of the initial centers of clusters. In the method proposed in this work, the density of points, distance of centers from each other and their distribution in the feature space have been taken into consideration. The method acts on the base of a sub-set of image data and results in defining a standard using the distance of points from the centers of clusters, the density of points and the standard deviation of the distance of the points to the centers of clusters. The initial centers are then selected reiteratively. In this research, the k-means method has been used for clustering the multispectral imagery. The algorithm has been applied on simulated images and real satellite multispectral data. The results were compared with the reference maps. Some prevailing methods for the selection of the initial centers have been implemented for comparative purposes. The results showed that the proposed method, as compared with the existing methods, not only increases the accuracy, but also decreases the number of necessary iterations for clustering, at the same time maintaining the robustness of the method. © 2016, Indian Society of Remote Sensing.
IEEE Geoscience and Remote Sensing Letters (1545598X) 12(7)pp. 1521-1525
Clustering is an important topic in image analysis and has many applications. Owing to the limitations of the feature space in multispectral images and spectral overlap of the clusters, it is required to use some additional information such as the spatial context in image clustering. To increase the accuracy of image clustering, a new Hierarchical Iterative Clustering Algorithm using Spatial and Spectral information (HICLASS) is introduced. This algorithm separates pixels into uncertain and certain categories based on decision distances in the feature space. The algorithm labels the certain pixels using the k-means clustering, and the uncertain ones with the help of information in both spatial and spectral domains of the image. The proposed algorithm is tested using simulated and real data. The benchmark results indicate better performance of HICLASS when compared with the k-means, local embeddings, and some proximity-based algorithms. The overall accuracy of the k-means has increased between 12.5% and 20.4% for different data. The HICLASS method increases the accuracy and generates more homogeneous regions, which are required for object-based applications. © 2004-2012 IEEE.
CTIT workshop proceedings series (16821750) 35
In the context of the analysis of remotely sensed data the question arises of how to analyse large volumes of data. In the specific case of agricultural fields in flat areas these fields can often be modelled in terms of geometric primitives such as triangles and rectangles. In this case the options are classical i.e. bottom-up, starting at the pixel level and resulting in a segmented, labelled image or top-down, starting with a model for image partitioning and resulting in a minimum cost estimation of shape hypotheses with corresponding parameters. Standard bottom-up classification methods usually concern the pixel as a main element and try to label the pixel individually. But various errors are involved in the image analysis with these methods. Mixed pixels, simplicity of the basic assumptions in the classification algorithms, sensor effects, atmospheric effects, and radiometric overlap of land cover objects lead to the wrong detection in image analysis. In this paper we propose a Model-Based Image Analysis (MBIA) approach to analyze the remotely sensed data. In this manner using the available knowledge about the remote sensing system we generate some hypothesis maps and then test them using the radiometric measurements (images). In order to test the method we used the boundaries of the agricultural fields stored in a GIS to model the objects in the scene. The results of the method have been compared with the result of a traditional Maximum-Likelihood classification and a standard Object-Based Classification using the boundaries. Using this approach we could reach to the 94% overall accuracy. © 2004 International Society for Photogrammetry and Remote Sensing. All rights reserved.
CTIT workshop proceedings series (16821750) 35
Traditionally accuracy assessment of the classification results uses some collected reference data (ground truth). Ground truth collection is a time-consuming and money-swallowing activity and usually can not be done completely. Uncertainty is an important subject in remote sensing that can appear and be increased sequentially in a chain of remote sensing from data acquisition, geometric and radiometric processing to the information extraction. Conceptually the relation between uncertainty and accuracy is an inverse relation. This relation can aid us to construct a relation between accuracy measures and uncertainty related measures. In this paper we investigate this relation using the generated synthetic images (for the sake of the reliability of the obtained results) and try to find an uncertainty related measure that has a strong relationship with the accuracy parameters like overall accuracy.We have found that among the uncertainty measures the mean quadratic score has the strong and reliable relationship with the commonly used accuracy measures. This relationship can be a good basis for the future investigations that lead to the classification based accuracy measures and avoiding some problematic data related issued of ground truth data collection. © 2004 International Society for Photogrammetry and Remote Sensing. All rights reserved.
CTIT workshop proceedings series (16821750) 35
Accuracy assessment is an important step in the process of analyzing remote sensing data. It determines the value of the resulting data to a particular user, i.e. the information value. Remote sensing products can serve as the basis for political as well as economical decisions. Users with a variety of applications should be able to evaluate whether the accuracy of the map suits their objectives or not. In the conventional accuracy assessment an error matrix and some accuracy measures derived from it are used. An error matrix is established using some known reference data and corresponding classified data. There are various factors that affect the performance of the accuracy assessment by influencing the error matrix through out the ground truth data collection. In practice, the techniques are of little value if these effective factors are not considered. In this paper the necessity considerations for accuracy assessment including the sampling schemas and the sample size for these sampling methods are studied. Also the factors that affect selecting and applying appropriate sampling schemas and sample size are investigated. For this study numbers of synthetic images and one real image and some reference data are used. Sensitivity of the various sampling schemas has been investigated using the synthetic images and using the real image the obtained results have been confirmed. The results represent that depend on specific conditions such as type and size of the study region and object characteristics, different sampling methods and sample sizes are preferred. © 2004 International Society for Photogrammetry and Remote Sensing. All rights reserved.
