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Namdar, Mohammad; Adamowski, Jan; Saadat, Hossein; Sharifi, Forood; Khiri, Afsaneh
doi: 10.1080/01431161.2014.978035pmid: N/A
This study was focused on addressing the need for accurate land-use/land-cover classification (LULC) maps in Iran and in other similarly developing countries. To generate and validate a new LULC map for northeastern Iran’s 2037.5 km2 Hable-roud watershed, a step-by-step process was developed and implemented, consisting of image preprocessing, extraction of training and reference sampling locations, decomposition of multi-spectral thematic mapper bands into features by independent component analysis methods, classification using these features and slope maps, enhancement of land-use classes through image segmentation and zonal statistics, then through consideration of normalized difference vegetation index and climatic zones, followed by ground truthing. This newly developed approach provided maps that distinguished dryland farming, irrigated farmland, forest plantations, and low-, medium-, and high-vegetation density rangelands, while currently available maps for the watershed lef t 39% of lands unclassified or in combined classes. The new maps’ ground-truthing-based overall accuracy and kappa coefficient were 88.3% and 0.83, respectively. In order to develop such an improved LULC map, it was necessary to go beyond the mere analysis of reflectance information, to incorporating climatic and topographic data through this newly proposed step-by-step approach.
Singh, Pankaj Pratap; Garg, R.D.
doi: 10.1080/01431161.2014.978956pmid: N/A
The segmentation and classification of high-resolution satellite images (HRSI) are useful approaches to extract information. In recent times, roads and buildings have been classified for analysis of urban areas in a better manner. Apart from these, healthy trees are also an important factor in HRSI, i.e. adjacent to roads, and vegetation. They reflect the area in an image as land cover. Other important information, shadow, is extracted from satellite images, which indicates the presence of trees and built-up areas such as buildings, flyovers, etc. In this article, a weighted membership-function-based fuzzy c-means with spatial constraints (WMFCSC) approach for automated satellite image classification is proposed. Initially, spatially fuzzy clustering is used to classify the satellite images in healthy trees with vegetation, roads, and shadows, which includes the information of spatial constraints. The road results of the classified image are still having non-road segments. Therefore, the proposed four intermediate stages (IS) are used to extract the road information, followed by the results of road areas of the WMFCSC approach. The framework of IS helps to remove the false road segments which are adjacent to roads and renovates the segmented roads due to the shadow effect. A final step of a hybrid WMFCSC-IS approach is used to extract the road network. The results of classified images confirm the effectiveness of the WMFCSC-IS approach for satellite image classification.
Xu, Yongming; Knudby, Anders; Ho, Hung Chak
doi: 10.1080/01431161.2014.978957pmid: N/A
Air temperature (Ta) is an important climatological variable for forest research and management. Due to the low density and uneven distribution of weather stations, traditional ground-based observations cannot accurately capture the spatial distribution of Ta, especially in mountainous areas with complex terrain and high local variability. In this paper, the daily maximum Ta in British Columbia, Canada was estimated by satellite remote sensing. Aqua MODIS (Moderate Resolution Imaging Spectroradiometer) data and meteorological data for the summer period (June to August) from 2003 to 2012 were collected to estimate Ta. Nine environmental variables (land surface temperature (LST), normalized difference vegetation index (NDVI), modified normalized difference water index (MNDWI), latitude, longitude, distance to ocean, altitude, albedo, and solar radiation) were selected as predictors. Analysis of the relationship between observed Ta and spatially averaged remotely sensed LST indicated that 7 × 7 pixel size was the optimal window size for statistical models estimating Ta from MODIS data. Two statistical methods (linear regression and random forest) were used to estimate maximum Ta, and their performances were validated with station-by-station cross-validation. Results indicated that the random forest model achieved better accuracy (mean absolute error, MAE = 2.02°C, R2 = 0.74) than the linear regression model (MAE = 2.41°C, R2 = 0.64). Based on the random forest model at 7 × 7 pixel size, daily maximum Ta at a resolution of 1 km in British Columbia in the summer of 2003–2012 was derived, and the spatial distribution of summer Ta in this area was discussed. The satisfactory results suggest that this modelling approach is appropriate for estimating air temperature in mountainous regions with complex terrain.
Prasad, M.S. Ganesh; Arora, Manoj K.
doi: 10.1080/01431161.2014.979303pmid: N/A
Representing the quality of thematic maps derived from remote-sensing image classification is important in assessing its fitness for use. Conventional approaches to represent the quality in terms of accuracy need information from the reference data at the same scale. Error-prone or dubious reference data may have an impact on the assessment of quality. Therefore, measures that complement the conventional accuracy measures are required to represent the quality. Uncertainty and confidence are such measures that do not require reference data. Few studies have been attempted to derive pixel-level confidence. However, these measures are not widely adopted by the remote-sensing community due to their limitations. In this article, a simple measure of confidence is derived to represent the quality of fuzzy classification. To derive the confidence value for a pixel, two values, viz. first highest class membership value as evidence and an associated degree of certainty, are required. When the difference between first and second highest membership values is used as degree of certainty in the proposed approach, the confidence measure derived is equal to the complement of existing measure of uncertainty, viz. confusion index in difference form.
Zhang, Chang-Jiang; Chen, Yuan; Duanmu, Chunjiang; Feng, Hua-Jun
doi: 10.1080/01431161.2014.980918pmid: N/A
A novel multi-channel satellite cloud image fusion algorithm constructed in the tetrolet transform domain is proposed. Tetrolet is successfully applied in image denoising, image sparse representation, and image restoration. In this paper, tetrolet transform was introduced into the field of satellite cloud image fusion since its sparse degree is high. Tetrolet can describe the geometric structure feature of the satellite cloud image very well. First, tetrolet transform must be implemented into the multi-channel satellite cloud images to obtain low- and high-frequency coefficients and corresponding covering distribution values. Then, a Laplacian pyramid algorithm must be used to decompose the low-frequency portion in the tetrolet domain by averaging the values of its top layer and taking the maximum absolute values of the other layers. While reconstruction is implemented in this stage, the algorithm takes the maximum standard deviation of the high-frequency parts for each block in the tetrolet domain. Last, an inverse tetrolet transform must be used to obtain the final fused image. This paper compares the proposed image fusion algorithm to three similar image fusion algorithms: the curvelet image fusion algorithm, the non-subsampled contourlet transform (NSCT) image fusion algorithm, and the tetrolet image fusion algorithm. Mutual information, joint entropy, mean structural similarity (MSSIM), standard deviation, and average relative deviation are used as objective criteria to evaluate the quality of the fused images. In order to verify the efficiency of the proposed algorithm, the fusion cloud image is used to determine the centre location of eye and non-eye typhoons. Experimental results show that the proposed algorithm performs well when fusing the information in multi-channel satellite cloud images and improves the precision of locating the typhoon’s centre. The proposed algorithm’s comprehensive performance is superior to similar image fusion algorithms.
Qiu, Zhongfeng; Su, Yuanyuan; Yang, Anan; Wang, Lin; Mao, Zhihua; Zhou, Bin; Chen, Shuguo
doi: 10.1080/01431161.2014.980919pmid: N/A
Distribution of absorption and backscattering coefficients (a(560) and bb(550)) is important for characterizing the marine optical environment. Satellite remote sensing is a useful tool for investigating the absorption and backscattering coefficients in coastal waters. A simple semi-analytical algorithm (SAABS) was developed for estimating a(560) and bb(550) in the Bohai Sea from Medium Resolution Imaging Spectrometer (MERIS) images. Using field measurements, the SAABS model attained root-mean-square (RMS) values of 13.25% and 12.75% for a(560) and bb(550), respectively. The SAABS model was also used to retrieve a(560) and bb(550) from the MERIS image. The match-up analysis results indicate that the RMS values of a(560) and bb(550) retrievals are 18.75% and 17%, respectively. These findings suggested that if the atmospheric correction scheme is available, the SAABS model may be used for the quantitative monitoring of the absorption and backscattering coefficients in the Bohai Sea from the MERIS images.
Lu, Dengsheng; Li, Guiying; Moran, Emilio; Dutra, Luciano; Batistella, Mateus
doi: 10.1080/01431161.2014.980920pmid: N/A
Texture has long been recognized as valuable in improving land-cover classification, but how data from different sensors with varying spatial resolutions affect the selection of textural images is poorly understood. This research examines textural images from the Landsat Thematic Mapper (TM), ALOS (Advanced Land Observing Satellite) PALSAR (Phased Array type L-band Synthetic Aperture Radar), the SPOT (Satellite Pour l’Observation de la Terre) high-resolution geometric (HRG) instrument, and the QuickBird satellite, which have pixel sizes of 30, 12.5, 10/5, and 0.6 m, respectively, for land-cover classification in the Brazilian Amazon. GLCM (grey-level co-occurrence matrix)-based texture measures with various sizes of moving windows are used to extract textural images from the aforementioned sensor data. An index based on standard deviations and correlation coefficients is used to identify the best texture combination following separability analysis of land-cover types based on training sample plots. A maximum likelihood classifier is used to conduct the land-cover classification, and the results are evaluated using field survey data. This research shows the importance of textural images in improving land-cover classification, and the importance becomes more significant as the pixel size improved. It is also shown that texture is especially important in the case of the ALOS PALSAR and QuickBird data. Overall, textural images have less capability in distinguishing land-cover types than spectral signatures, especially for Landsat TM imagery, but incorporation of textures into radiometric data is valuable for improving land-cover classification. The classification accuracy can be improved by 5.2–13.4% as the pixel size changes from 30 to 0.6 m.
doi: 10.1080/01431161.2014.980921pmid: N/A
Earth’s telluric field variations, known as seismic electric signals (SESs), when combined with available remote-sensing technique (GPS) deformation field measurements, can enhance the likelihood of elucidating the underlying physics of the dynamic processes prevailing in pre-seismic regions before fatal failure. The update of a power law relation between the lead time of the precursory SESs and the stress drop in the destructive Mw 6.1 earthquake that occurred in 2014 on Cephalonia Island, Greece, led to an exponent value which agrees remarkably well with those previously obtained and falls within the range of critical exponents for fracture. The stability of this exponent confirms the credibility of the above power law and possibly implies that upon SES emission, non-linear dynamic processes with features of criticality govern the pre-focal area. It is suggested for a future study that a proper combination of remote-sensing techniques with ground-based electric field measures be coordinated in order to verify that a region has entered the critical stage.
Shanmugam, S.; SrinivasaPerumal, P.
doi: 10.1080/01431161.2014.980922pmid: N/A
Many spectral matching algorithms, ranging from the traditional clustering techniques to the recent automated matching models, have evolved. This paper provides a review and up-to-date information on the past and current role of the spectral matching approaches adopted in hyperspectral satellite image processing. The need for spectral matching has been deliberated and a list of spectral matching algorithms has been compared and described. A review of the conventional spectral angle measures and the advanced automated spectral matching tools indicates that, for better performance of target detection, there is a need for combining two or more spectral matching techniques. From the studies of several authors, it is inferred that continuous improvement in the matching techniques over the past few years is due to the need to handle and analyse hyperspectral image data for various applications. The need to develop a well-built and specialized spectral library to accommodate the resources from enormous spectral data is suggested. This may improve accuracy in mineral and soil mapping, vegetation species identification and health monitoring, and target detection. The future role of cloud computing in accessing globally distributed spectral libraries and performing spectral matching is highlighted. Rather than inferring that a particular matching algorithm is the best, this paper points out the requirements of an ideal algorithm. With increasing usage of hyperspectral data for resources mapping, the review presented in this paper will certainly benefit the large and emerging community of hyperspectral image users.
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