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Compared to remote sensing images of medium or low spatial resolution, high-resolution remote sensing images can provide observation data containing more detailed information for georesearch. Accordingly, an important issue for current computer and geoscience experts is to develop useful methods or technology to extract information from these high-resolution satellite images. As part of a series of research into object extraction, this paper focuses mainly on the extraction of bridges over water from high-resolution panchromatic satellite images. Since bridges over water are obviously adjacent to water in remote sensing images, this paper proposes a practical knowledge-based bridge extraction method for remote sensing images of high spatial resolution. The steps involved are: water extraction based on Gauss Markov Random Field (GMRF)-Support Vector Machine (SVM) classification methods which use a SVM to classify the image based on textural features expressed by a GMRF; image thinning and removal of fragmented lines; main trunk detection by width; vectorization; and feature expression. Finally, tests are described for two pieces of panchromatic IKONOS satellite images with a 1 m resolution. The experimental results show that the proposed method is suitable for images with a single-peak histogram (contrast between water and land is sharp) or a multi-peak histogram (greyscale value of water is close to that of land). [ABSTRACT FROM AUTHOR]

Radiometric corrections serve to remove the effects that alter the spectral characteristics of land features, except for actual changes in ground target, becoming mandatory in multi-sensor, multi-date studies. In this paper, we evaluate the effects of two types of radiometric correction methods (absolute and relative) for the determination of land cover changes, using Landsat TM and Landsat ETM+ images. In addition, we present an improvement made to the relative correction method addressed. Absolute correction includes a cross-calibration between TM and ETM+ images, and the application of an atmospheric correction protocol. Relative correction normalizes the images using pseudo-invariant features (PIFs) selected through band-to-band PCA analysis. We present a new algorithm for PIFs selection in order to improve normalization results. A post-correction evaluation index (Quadratic Difference Index (QD)), and post-classification and change detection results were used to evaluate the performance of the methods. Only the absolute correction method and the new relative correction method presented in this paper show good post-correction and post-classification results (QD index ≈ 0; overall accuracy >80%; kappa >0.65) for all the images used. Land cover changes estimations based on uncorrected images present unrealistic change rates (two to three times those obtained with corrected images), which highlights the fact that radiometric corrections are necessary in multi-date multi-sensor land cover change analysis. [ABSTRACT FROM AUTHOR]

By applying the same principles and techniques as described in the first paper, the data transformation equations for SPOT, NOAA, IKONOS, Quick Bird ASTER, and MSS are discussed and presented here. As in TM images, the general radiance level L, the visible-infrared radiation balance B and the band radiance variation vector (direction and speed) V images are all extracted from the above satellite images. All transformed black and white L, B and V images and their colour composites have their universality and individuality. The universality is that the transformed images accord with the three-to-three corresponding regular triangle scheme described in paper I and that the colours on their colour composite are pure, saturated, plentiful, brilliant, information-equilibrated, meaning-definite and close to colours of ground features in nature. The individuality is that the transformed images all have their own regional and seasonal characteristics. The universality is only determined by the coefficients of transformation equations and not dependent upon the original gray level values of image scene in any region and any season. Meanwhile the individuality is only determined by the preservation of regional and seasonal characteristics contained in the original gray level values of concrete image scenes. This paper also deals with the application of the transformed images in image enhancement, ground features interpretation, classification and digital analysis. In addition, some application examples are also offered. Finally the peculiarities and some shortcomings of this method are also summarized. [ABSTRACT FROM AUTHOR]

Frequently cloudy conditions in moist tropical regions often cause difficulty in acquiring good-quality remote-sensing images. This paper explores approaches to detect clouds/hazes and their cast shadows on IKONOS images. Two approaches based on maximum and minimum filters and the second-moment texture measures were tested. The detected clouds/hazes and their cast shadows in one image were then replaced with the pixel values from the same location of another image after image normalization. This research indicates that both the approaches developed in this paper have successfully detected the clouds/hazes and their cast shadows on IKONOS images. In particular, the second-moment texture approach is recommended. [ABSTRACT FROM AUTHOR]

These two papers deal with a new method of data transformation. By analysing grey level curves (broken lines) of various ground features in image bands of different satellites, we have found that, inherent in 3- or 4-band satellite images (SPOT, IKONOS, Quick Bird, OrbView, FORMOSAT and MSS) there are three basic remote-sensing characteristics as follows: (1) the general radiance level L; (2) the visible-infrared radiation balance B; and (3) the band radiance variation vector (direction and speed) V. However, inherent in 5- or 7-band satellite images (NOAA, TM), besides the above three, there is an extra basic remote-sensing characteristic, i.e. the thermal radiation intensity I. This is denoted by thermal bands, i.e. the TM band 6 or NOAA (AVHRR) bands 4 and 5, which are relatively independent and can be used directly, and hence are unnecessary for information extraction or data transformation. Therefore, the data transformation only lies in extracting the L, B and V from original satellite images. Furthermore, we have also found that there are three basic ground-cover elements on the Earth's surface: i.e. the bare land (in a broad sense), the vegetation and the water body, which, in different proportions, constitute all ground cover. Moreover, there are three basic (primitive) colours on colour image (including colour composite of satellite images): i.e. red, green and blue, which generate all colours on the colour image. Further research has revealed that the three basic remote-sensing characteristics, the three basic ground-cover elements and the three basic colours on the composite can conceptually constitute a three-to-three corresponding regular triangle scheme. Perhaps a good method of data transformation should make the scheme realistic, i.e. make the three 'threes' all correspond to each other. The research presented here has completed this task by regression calculations and selection of specific variables. First, the methodology and transformation equations for TM images are discussed. The transformed L, B and V images have relatively independent and equally distributed information as well as clear and definite physical, mathematical and geographical significance. They can be used effectively for generating high-quality colour composites, on which the red, green, blue, yellow, pink, cyan and other colours of various kinds are all generated and all pure, saturated, equilibrated, meaning-definite and close to the colours of ground features in nature. As a result, interpretations and discriminations of ground features can be easier and conducted not only by experience, but also by logic. The L, B and V images can also be used effectively for classification and digital analysis of ground features. With regard to the transformation equations for SPOT, NOAA, IKONOS, Quick Bird, OrbView, FORMOSAT and MSS images and the method application will be dealt with in the second paper. [ABSTRACT FROM AUTHOR]

Image‐to‐image registration is a prerequisite step for many important applications in different disciplines. In the field of remote sensing, the launch of new high‐resolution satellites raises the need for a fast and dynamic technique for image‐to‐image registration in order to obtain full benefit from these satellites, especially for applications such as Earth monitoring and map updating. This paper presents the utilization of the Line‐Based Transformation Model (LBTM) for image‐to‐image registration following the successful use of the LBTM for the rectification of high‐resolution satellite images using linear features as control features. The developed model is similar in structure to some other point‐based transformation models. However, line segments on linear features are used first to recover the model transformation coefficients. Based on the recovered coefficients, the whole image is registered using the ordinary point‐based form of the model. Line segments on linear features have been chosen due to their existence in images regardless of the type of the land‐use covered by the images and because line segments can be easily extracted from the images. Moreover, control points may not exist or a complete match between points on images cannot be achieved. Different forms of the developed model are discussed and results using different high‐resolution satellite images from both IKONOS and QuickBird satellites are presented. The experimental results of the new technique show high integrity of the new model and indicate that image‐to‐image registration by LBTM is reliable. [ABSTRACT FROM AUTHOR]

Medium-spatial-resolution satellite images have already proved to be successful in automatic production of global land-cover maps. However, their operational use for land-cover mapping at a national scale has not yet been well established. We find that the reasons for this are not data-source dependent, but are due to the land-cover nomenclatures properties adopted, regional landscape specificities and the methodological approaches used. The aim of this paper is to evaluate the suitability for national applications of land-cover maps derived from automatic classification of medium-spatial-resolution satellite images. To tackle this issue, we produce a land-cover map of Continental Portugal from multitemporal MEdium Resolution Imaging Spectrometer (MERIS) full-resolution satellite images of 2005 and evaluate its accuracy. For the accuracy assessment of the final map, we compute unbiased estimates of overall, user and producer accuracies using an independent testing sample collected through a stratified random sampling design. The overall accuracy of the final map is 80%, with an absolute precision of 2% at the 95% confidence level. High independent accuracy assessment results demonstrate that medium-spatial-resolution satellite images can be used on an operational basis for annual production of land-cover maps suitable for national applications. [ABSTRACT FROM AUTHOR]

In this paper we present an improved watershed segmentation algorithm for settlement mapping from medium resolution satellite data over plain areas in China. The algorithm can increase the computational efficiency of the fastest reported watershed segmentation algorithm by 30-40%. We apply this method to a selected study area in southern Hebei Province, China. We acquired a Landsat Enhanced Thematic Mapper Plus (ETM + ) image over this area in May 2000, two Landsat Thematic Mapper (TM) images in August 2004 and April 2005, and two Beijing-1 satellite images in May 2006 and May 2007. The three types of images have three similar spectral bands (green, red and near-infrared) with similar spatial resolution (30-32 m). Only the red and near-infrared bands were used in image segmentation for settlement area extraction. The extracted settlement results are compared with manual interpretation results by two people. We assumed the human interpretation results are of higher accuracy than the segmentation results. Our results indicated that our settlement area extraction method is effective. With high quality images, the overall accuracies are nearly 94%, the kappa coefficient can be greater than 0.85. [ABSTRACT FROM AUTHOR]

Google Earth uses aerial and satellite images from various sources as its background. It is designed to enable every person who owns a computer to easily 'fly' to aerial views of any location on the planet. However, just as with ordinary aerial or satellite images, there inevitably exist shadows in Google Earth images, making some ground objects obscured, even unidentifiable. In this paper, we describe a model we have developed for image shadow removal that uses radiative transfer theory in combination with prior knowledge to compensate for the lost information over shadow areas. We arbitrarily selected six images with severe shadows from Beijing, Shanghai, Hong Kong, Tokyo, New York and San Francisco, respectively, for algorithm testing. Our experimental results show that shadows in images can be considerably suppressed. [ABSTRACT FROM AUTHOR]

Future mid-infrared satellite missions exploring the Earth will feature advanced high spatial resolution and directional imaging instruments. Consistent end-to-end simulation of them is an important task, and is sometimes the only way to adapt and optimize a sensor and its observation conditions, to choose and test algorithms for data processing, to estimate errors and to evaluate the capabilities of the whole sensor system. However, contrary to other wavelength ranges, the mid-infrared is highly dependent on atmospheric scattering and emission. Therefore, simulation of atmospheric radiative transfer for remote sensing images will remain a challenging task, because few studies on this topic include a full treatment of atmospheric effects. With a given resolution and directional capabilities of the instrument, and combining with land surface temperature and emissivity data obtained from airborne imagery, TOA (top of atmosphere) radiance images have been simulated pixel by pixel, coupling the atmospheric radiative transfer analytic model extended from MODTRAN4 and the atmospheric adjacency effect model derived from point spread function (for atmospheric directional and adjacency effect). In this way, all major scattering and emission contributions of atmosphere were considered. Based on different atmospheric conditions and geometrical relations between the scene, the Sun and the sensor, simulated TOA radiance images were produced according to simulated workflows, 10-m spatial resolution and a spectral range of 3.5-3.9 μm. Analysis of results indicates that the analytic model and adjacency effect model are more suitable for mid-infrared imaging simulation than other existing models. This paper describes the principle of the two models, the applied methodology, the set-up of the actual image simulations, and then discusses the final results obtained. [ABSTRACT FROM AUTHOR]

Many types of nonlinear classifiers have been proposed to automatically generate land-cover maps from satellite images. Some are based on the estimation of posterior class probabilities, whereas others estimate the decision boundary directly. In this paper, we propose a modular design able to focus the learning process on the decision boundary by using posterior probability estimates. To do so, we use a self-configuring architecture that incorporates specialized modules to deal with conflicting classes, and we apply a learning algorithm that focuses learning on the posterior probability regions that are critical for the performance of the decision problem stated by the user-defined misclassification costs. Moreover, we show that by filtering the posterior probability map, the impulsive noise, which is a common effect in automatic land-cover classification, can be significantly reduced. Experimental results show the effectiveness of the proposed solutions on real multi- and hyperspectral images, versus other typical approaches, that are not based on probability estimates, such as Support Vector Machines. [ABSTRACT FROM AUTHOR]

In this paper, we introduce the idea of documenting operational chains for land degradation assessment using ontologies. We believe that this process will help end users better understand the application domain characteristics and evaluate the results of the assessment process. Since the application domain is wide, various operational chains for land degradation assessment and their associated documentation exist, according to different options. This parameterization process causes the development of different ontologies, which nonetheless are, to a certain extent, linked because of the common software components of the corresponding operational chains. We therefore propose a hierarchical structure of these ontologies; so that several requirements such as understanding of expert knowledge interconnections and of application domain variety, documentation and assimilation of new expert knowledge, and reusability of software components become feasible. [ABSTRACT FROM AUTHOR]

Fusion of panchromatic (PAN) and multispectral (MS) images is one of the most promising issues in remote sensing. PAN modulation fusion methods are usually based on an assumption that a ratio of two different-resolution versions of an MS band is equal to a ratio of two different-resolution versions of a PAN image. In such fusion methods, image haze is rarely taken into account, and it may produce serious spectral distortion in synthetic images. In this paper, assuming that the previous ratio relationship only holds for haze-free images, two relevant improvement schemes are proposed to better express the ratio relationship of haze-included images. In a test on a spatially degraded IKONOS dataset, the first scheme synthesizes an image with minimum spectral distortion, and the second modifies several current PAN modulation fusion methods and generates high-quality synthetic products. The experiment results confirm that image haze can seriously impact the quality of fused images obtained by using PAN modulation fusion methods, and it should be taken into account in relevant image fusion. [ABSTRACT FROM AUTHOR]

This paper describes single-date and multi-date land-cover classification accuracy results using segment-based, gap-filled Landsat 7 Enhanced Thematic Mapper data compared with Landsat 5 Thematic Mapper data captured one day apart. Maximum likelihood and Decision tree classification algorithms were evaluated. The same training and verification sets of ground data were used for each classification evaluation. For the comparison with the single-date classification, an average decrease of 2.8% in the classification accuracy was obtained with the use of the gap-filled Landsat data. Area estimates for the mid-summer images differed, on average, from 0.6% to 1.9% for a four-class and eight-class classification, respectively. A multi-date land-cover classification was also completed with the addition of a late spring Landsat 5 image, resulting in an average decrease in classification accuracy of 1.8%. [ABSTRACT FROM AUTHOR]

Image shadow segmentation has become a major issue in satellite remote sensing because of the recent commercial availability of high-resolution images. Detecting shadows is important for successfully carrying out applications such as change detection, land monitoring, object recognition, scene reconstruction, colour correction, etc. This paper presents a simple and effective procedure to segment shadow regions on high-resolution colour satellite images. The method applies a region growing process on a specific band (namely, the c 3 component of the c 1 c 2 c 3 colour space). To gain in robustness and precision, the region expansion also imposes a restriction on the saturation and intensity values of the shadow pixels, as well as on their edge gradients. The proposed method has been successfully tested on QuickBird images acquired under different lighting conditions and covering both urban and rural areas. [ABSTRACT FROM AUTHOR]

In image fusion, the spatial resolution ratio can be defined as the ratio between the spatial resolution of the high-resolution panchromatic image and that of the low-resolution multispectral image. This paper attempts to assess the effects of the spatial resolution ratio of the input images on the quality of the fused image. Experimental results indicate that a spatial resolution ratio of 1 : 10 or higher is desired for optimal multisensor image fusion provided the input panchromatic image is not downsampled to a coarser resolution. Due to the synthetic pixels generated from resampling, the quality of the fused image decreases as the spatial resolution ratio decreases (e.g. from 1 : 10 to 1 : 30). However, even with a spatial resolution ratio as small as 1 : 30, the quality of the fused image is still better than the original multispectral image alone for feature interpretation. In cases where the spatial resolution ratio is too small (e.g. 1 : 30), to obtain better spectral integrity of the fused image, one may downsample the input high-resolution panchromatic image to a slightly lower resolution before fusing it with the multispectral image. [ABSTRACT FROM AUTHOR]

Spectral mixture analysis is an efficient approach to spectral decomposition of hyperspectral remotely sensed imagery, using land cover proportions which can be estimated from pixel values through model inversion. In this paper, a kernel least square regression algorithm has been developed for nonlinear approximation of subpixel proportions. This procedure includes two steps. The first step is to select the feature vectors by defining a global criterion to characterize the image data structure in the feature space and the second step is the projection of pixels onto the feature vectors and the application of classical linear regressive algorithm. Experiments using simulated data, synthetic data and Enhanced Thematic Mapper (ETM)+ data have been carried out, and the results demonstrate that the proposed method can improve proportion estimation. By using the simulated and synthetic data, over 85% of the total pixels in the image are found to lie between the 10% difference lines, and the root mean square error (RMSE) is less than 0.09. Using the real data, the proposed method can also perform satisfactorily with an average RMSE of about 0.12. This algorithm was also compared with other widely used kernel based algorithms, i.e. support vector regression and radial basis function neutral network and the results show that the proposed algorithm outperforms other algorithms about 5% in subpixel proportion estimation. [ABSTRACT FROM AUTHOR]

The discontinuities play an important role in both the design and development stages of many geotechnical engineering projects. Therefore, semi-automatic detection of discontinuities based on remote sensing will save a considerable amount of time and cost. Accuracy of the semi-automatically detected discontinuities is also as important as the detection process, since the results directly reflect the uncertainty in the geotechnical problem. The current paper presents an accuracy assessment methodology for semi-automatically detected discontinuities from remotely sensed images. Semi-automatic lineament analysis is performed by using high-resolution satellite imagery for identification of rock discontinuities at the Andesite quarry area in Gölbası, Ankara, Turkey. A high-resolution data IKONOS Precision Plus with 1 m resolution orthorectified image has been used in the present study. The accuracy assessment methodology is developed based on a comparison of manually extracted lineaments using directional filtering and semi-automatically detected lineaments with LINE module of PCI Geomatica 8.2 and manual intervention. The proposed accuracy assessment methodology considers the accuracy based on location and length of the lineaments. In this respect, it has two dimensions. For the algorithm of the developed methodology, a software called LINECOMP, is coded in Java environment. The LINECOMP is composed of three modules. These are: LINE ADDRESS for identification of pixel addresses of lineaments; which is used for locational accuracy assessment; LINE COMPARE for comparing two lineament maps with respect to lineament pixels, which is used for length-based accuracy; and LINE CLASSIFICATION for classifying the assessed accuracy with respect to the amount of location and length matching. The performance of the proposed accuracy assessment method is also verified by the field studies carried out in the study area. [ABSTRACT FROM AUTHOR]

An essential determinant of the value of surrogate digital images is their quality. Image quality measurement has become crucial for most image processing applications. Over the past years, there have been many attempts to develop models or metrics for image quality that incorporate elements of human visual sensitivity. However, there is no current standard and objective definition of spectral image quality. This paper proposes a reliable automatic method for objective image quality measurement by local spatial‐frequency wavelet analysis. The analysis is performed locally by dividing an image into 64×64 pixel blocks, and transforming the data into wavelet domain and sub‐band image domain. A fast lifting wavelet algorithm is developed for computationally efficient spatial‐frequency deconstruction of images to extract the features of edges in sub‐band datasets. Wavelet analysis throughout the spatial‐frequency range with respect to noise, sharpness, brightness, contrast, and modulation transfer function (MTF) reveals more detailed information to relate the quality of an image to the interpretation by using an artificial neural network to optimize weightings and results in a promising quantitative measure of image quality. Experimental results from using this method for image quality measurement exhibit good correlation to subjective visual quality assessments. The proposed approach with fast computation provides a potential metric for reliable image quality quantification, allowing image quality to be automatically evaluated following image capture/compression, but prior to permanent storage in a database. [ABSTRACT FROM AUTHOR]

Principal component analysis (PCA) has been commonly used and has played an important role in remote sensing for information extraction. However, the ordinary PCA based on second‐order covariance or correlation is capable of forming components on the basis of the statistical properties of a majority of pixel values – pixel values around mean values. For many applications, principal components should be constructed on the basis of optimum correlation coefficients so that the components can represent low or high values of minority pixels of interest. A new version of the PCA has been proposed on the basis of an optimum order sample correlation coefficient for enhancing the contribution of the image bands including the low or high minority pixel values that can assist in extracting weak information for image classification and pattern recognition. The ordinary PCA becomes the special case of the new version of the PCA introduced in this paper. The new method was validated with a case study of identification of Au/Cu‐associated alteration zones from a Landsat Thematic Mapper (TM) image in the Mitchell‐Sulphurets district, Canada. [ABSTRACT FROM AUTHOR]

The inversion of radar images to estimate surface soil parameters requires the use of well-calibrated backscattering models. The first objective of this paper is to confirm the empirical calibration proposed by Baghdadi et al. (2004) for the backscattering Integral Equation Model (IEM). That calibration was a partial one, because it covered only a few radar configurations. The second objective is to extend this calibration to include Synthetic Aperture Radar (SAR) data in C-band, for HH and VV polarizations, at any radar incidence between 20° and 50°. Advanced Synthetic Aperture Radar (ASAR) data were used to supplement the database employed by Baghdadi et al. (2004). The results show good agreement between the backscattering coefficients given by the SAR sensors and those estimated from the calibrated IEM. This study also confirms the robustness of this calibration. [ABSTRACT FROM AUTHOR]

This paper aims to propose a change-detection system in remotely sensed imagery based on the combination of fuzzy sets and neural networks. Multitemporal images are directly classified into change and no-change classes using a fuzzy membership model in order to provide complete information about the change. Presently, two fuzzy models derived from the Mahalanobis distance and a fuzzy neural network (FNN) combination are proposed and compared. In order to evaluate the performance of each model, extensive experiments using different performance indicators are carried out on two SPOT HRV images covering a region of Algeria. Results obtained showed that it has a great potential for land-cover change detection since it allows the nature of change to be extracted automatically. Furthermore, the FNN-based model gives the best performance. This model allows a reduced amount of false alarms with higher change detection accuracy. [ABSTRACT FROM AUTHOR]

Luobupo salt lake is famous for its ring shape which looks like a big ear observed from satellite image. These rings recorded the information about water retreat and advance. In this paper TM data is used to study the brightness of these rings and then deduce the paleoclimate. The result showed that the brightness curve of these rings has a similar climate trend to that deduced from Guliya ice core record. [ABSTRACT FROM AUTHOR]

Artificial targets are generally used in terrestrial laser scanner (TLS) practice for data georeferencing. This is because they are well recognized and modelled from the point cloud and their positions can be contemporarily measured by topographical techniques. The accuracy of target identification directly influences the georeferencing quality. In particular, retro-reflective materials can cause anomalies in range measurement due to the too high amplitude of the returned pulse. If the received pulse intensity exceeds the limits of the sensor dynamic range, the receiver saturates, producing a truncated pulse preventing the correct time-of-flight computation. A series of experiments was performed in order to test the performances of a specific instrument (Optech ILRIS 3D) for the acquisition of artificial targets made of retro-reflective material, resulting in very high reflectance. Dealing with ranges lower than about 300 m, two cases were clearly observed: the wrong distance measurement of points over high reflecting surfaces and the presence of haloes around these surfaces. Neglecting these phenomena has serious implications and can lead to wrong georeferencing. Experiments were executed and data was analysed, providing a qualitative and semi-quantitative phenomenon description. Finally, the design of a target that can be easily recognized and correctly modelled was proposed. [ABSTRACT FROM AUTHOR]

The rational function model (RFM) is widely applied to orthorectification of aerial and satellite imagery. This article proposes a newmethod named Ortho-WTLS to solve the RFM in remote-sensing imagery orthorectification. Based on a weighted total least squares (WTLS) estimator, the proposed method allows one to handle coordinates of ground control points (GCPs) that contain errors and are of unequal accuracies. This situation occurs, e.g. if GCPs are automatically selected. In the proposed model, first, the relationship of two linearization methods for an RFM with errors contained in GCPs is investigated and results in a hybrid linearization. Next, based on WTLS, RFM coefficients are estimated with an iterative computation function. Finally, the performance of the Ortho-WTLS method thus obtained is investigated using simulated images and remotely sensed images by collecting GCPs with varying errors. Experimental results show that the Ortho-WTLS method achieves a more robust estimation of model parameters and a higher orthorectification accuracy when compared with standard LS-based RFM estimation. We conclude that the quality of GCPs has a large impact on the accuracy and that an increasing number of low-precision GCPsmay lead to a decrease in orthorectification quality. [ABSTRACT FROM AUTHOR]

This study proposes a superpixel-based active contour model (SACM) for unsupervised change detection from satellite images. The accuracy of change detection produced by the traditional active contour model suffers from the trade-off parameter. The SACM is designed to address this limitation through the incorporation of the spatial and statistical information of superpixels. The proposed method mainly consists of three steps. First, the difference image is created with change vector analysis method from two temporal satellite images. Second, statistical region merging method is applied on the difference image to produce a superpixel map. Finally, SACM is designed based on the superpixel map to detect changes from the difference image. The SACM incorporates spatial and statistical information and retains the accurate shapes and outlines of superpixels. Experiments were conducted on two data sets, namely Landsat-7 Enhanced Thematic Mapper Plus and SPOT 5, to validate the proposed method. Experimental results show that SACM reduces the effects of the trade-off parameter. The proposed method also increases the robustness of the traditional active contour model for input parameters and improves its effectiveness. In summary, SACM often outperforms some existing methods and provides an effective unsupervised change detection method. [ABSTRACT FROM AUTHOR]

Remote sensing for hazard response requiresa prioriidentification of sensor, transmission, processing, and distribution methods to permit the extraction of relevant information in timescales sufficient to allow managers to make a given time-sensitive decision. This study applies and demonstrates the utility of the Remote Sensing Communication Model (RSCM) to improve a tactical wildfire remote-sensing system to better meet the time-sensitive information requirements of emergency response managers in San Diego County, USA. A thermal infrared airborne remote-sensing system designed and operated by the United States Forest Service Pacific Southwest Research Station for active wildfire monitoring is documented and updated based on the RSCM. Analysis of the thermal infrared remote-sensing system in the context of the RSCM identified three configuration changes that can improve the effectiveness of the information produced when employed by wildfire incident commanders for suppression prioritization: (1) limit spectral sampling collection to a single waveband; (2) complete image processing steps on-board the aircraft; and (3) provide information on wildfire locations to incident commanders in the form of a static map. [ABSTRACT FROM PUBLISHER]

A semi-fragile watermarking scheme for multiple band images is presented. We propose to embed a mark into remote-sensing images applying a tree-structured vector quantization approach to the pixel signatures, instead of processing each band separately. The signature of the multi- or hyperspectral image is used to embed the mark into it in order to detect any significant modification of the original image. The image is segmented into three-dimensional blocks and a tree-structured vector quantizer (TSVQ) is built for each block. These trees are manipulated using an iterative algorithm until the resulting block satisfies a required criterion which establishes the embedded mark. The method is shown to be able to preserve the mark under lossy compression (above a given threshold) but, at the same time, it detects possibly forged blocks and their position in the whole image. [ABSTRACT FROM AUTHOR]

In this study, a two-dimensional continuous wavelet transform is applied to quantify the non-homogeneity from remote-sensing wave images. Our study shows that the non-homogeneity index, which is proposed here, is capable of identifying the degree of non-homogeneity from the wave-field images. However, the influence from the edges of the wave-field image should be considered in analysis. The calculated results from the simulated wave fields and from the natural wave-field images reveal that the non-homogeneity index is influenced by the bathymetry inside the area of the ocean-wave image. It conforms to the character of the natural wave non-homogeneity. After discussing the wave characteristics from the wave-field image with a high non-homogeneity index, an inhomogeneous algorithm is recommended for obtaining accurate and reasonable wave results from the wave image. [ABSTRACT FROM AUTHOR]

Using the panchromatic band of QuickBird high-spatial-resolution satellite images as a data source, we estimated the mean crown diameters of different stands and of stands with different canopy densities by applying the semivariogram methodology used in geostatistics. The results indicated that relatively accurate estimates of the mean crown diameters of stands with a relatively high canopy density could be obtained. The estimated errors increased proportionally with decreasing canopy density. The whole-stand-image (matrix) method we used provided fairly accurate estimates of stand structural parameters, but would not readily permit the evaluation of patterns in stand structure. The transect method exhibited periodicity as a function of distance and, although it permits the detection of directional patterns, it does not depict stand structural parameters as accurately as the matrix method. Our results suggest that this approach could provide a new reference method for solving the problem of automated estimation of mean crown diameter at the stand level. [ABSTRACT FROM AUTHOR]

The Penghu archipelago comprises 64 basaltic volcanic isles lying on the Taiwan Strait between mainland China and Taiwan. The water around and within these isles is shallow and poses considerable difficulty in echo sounding detection for bathymetry. Most existing bathymetry data around such areas are in water depths of greater than 5 m. Therefore, when the water depth is less than 5 m the data tend to be over-extrapolated. In this study, a remote sensing method provides a more effective approach to recording shallow water depths compared to traditional soundings using multitemporal images collected by optical/near-infrared sensors from SPOT satellites. This method employs optical energy reflections to obtain the water depth. In this study, we made several improvements wherein a relative atmosphere correction technique was used to calibrate two images within a similar atmospheric condition. We then compared the satellite images acquired from different dates to obtain the local water attenuation coefficient of sunlight. Finally, we developed a means to estimate the water attenuation coefficient and bottom reflectance which will satisfy the two parameters across the study area. Our results show a high-resolution map of shallow bathymetry for the Penghu archipelago and revealed a maximum depth of about 20 m. This study provides an efficient approach for shallow bathymetry retrieval. Many detailed features revealed by this approach may contribute to further geological research and developments in harbour and coastal engineering. [ABSTRACT FROM AUTHOR]

This study compared a non-parametric and a parametric model for discriminating among uplands (non-wetlands), woody wetlands, emergent wetlands and open water. Satellite images obtained on 6 March 2005 and 16 October 2005 from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and geographic information system (GIS) data layers formed the input for analysis using classification and regression tree (CART®) and multinomial logistic regression analysis. The overall accuracy of the CART model was 73.3%. The overall accuracy of the logit model was 76.7%. The accuracies were not statistically different from each other (McNemar χ 2 = 1.65, p = 0.19). The CART producer's accuracy of the emergent wetlands was higher than the accuracy from the multinomial logit (57.1% vs. 40.7%), whereas woody wetlands identified by the multinomial logit model presented a producer's accuracy higher than that from the CART model (68.7% vs. 52.6%). A McNemar test between the two models and National Wetland Inventory (NWI) maps showed that their accuracies were not statistically different. Overall, these two models provided promising results, although they are not sufficiently accurate to replace current methods of wetland mapping based on feature extraction in high-resolution orthoimagery. [ABSTRACT FROM AUTHOR]

Satellite imagery is being used increasingly in association with national forest inventories (NFIs) to produce maps and enhance estimates of forest attributes. We simulated several image spatial resolutions within sparsely and heavily forested study areas to assess resolution effects on estimates of forest land area, independent of other sensor characteristics. We spatially aggregated 30 m datasets to coarser spatial resolutions (90, 150, 210, 270, 510 and 990 m) and produced estimates of forest proportion for each spatial resolution using both model- and design-based approaches. Average-based aggregation had no effect on per-image estimates of forest proportion; image variability decreased with increasing spatial resolution and local variability peaked between 210 and 270 m. Majority-based aggregation resulted in overestimation of forest land in a heavily forested landscape and underestimation of forest land in a sparsely forested landscape, with both trends following a natural log distribution. Of the spatial resolutions tested, 30 m was superior for obtaining estimates using model-based approaches. However, standard errors of design-based inventory estimates of forest proportion were smallest when accompanying stratification maps which were aggregated to between 90 and 150 m spatial resolutions and strata thresholds were optimized by study area. These results suggest that spatially aggregating existing 30 m land cover datasets can provide NFIs with gains in precision of their estimates of forest land area, while reducing image storage size and processing times; land cover datasets derived from coarser spatial resolution sensors may provide similar benefits. [ABSTRACT FROM AUTHOR]

Leaves are the primary interface where energy, water and carbon exchanges occur between the forest ecosystems and the atmosphere. Leaf area index (LAI) is a measure of the amount of leaf area in a stand, and the tree crown size characterizes how leaves are clumped in the canopy. Both LAI and tree crown size are of essential ecological and management value. There is a lot of interest in extracting both canopy structural parameters from remote sensing. The LAI is generally estimated with spectral information from remotely sensed images at relatively coarse spatial resolution. There has been much less success in estimating tree crown size with remote sensing. The recent availability of abundant high spatial resolution imagery from space offers new potential for extracting LAI and tree crown size, particularly in the spatial domain. This study found that the spatial information in Ikonos imagery is highly valuable in estimating both tree crown size and LAI. When the conifer- and hardwood-dominated stands are pooled, tree crown sizes of conifer stands relate best to the ratio of image variance at 2×2 m spatial resolution to that at 3×3 m spatial resolution, while LAI relates best to image variance at 4×4 m spatial resolution. When the conifer- and hardwood-dominated stands are separated, image spatial information estimates tree crown size much better for conifer-dominated stands than for the hardwood-dominated stands, while the relationship between image spatial information and LAI is strengthened after the two types of stands are combined. Tree crown size is more sensitive to image spatial resolution than LAI. Image variance is more useful in estimating LAI than normalized difference vegetation index (NDVI) and simple ratio vegetation index (SRVI). Combining both spatial and spectral information provides some improvement in estimating LAI compared with using spatial information alone. Therefore, future efforts to estimate canopy structure with high resolution imagery should also use image spatial information. [ABSTRACT FROM AUTHOR]

Ranging techniques such as lidar (LIght Detection And Ranging) and digital stereo-photogrammetry show great promise for mapping forest canopy height. In this study, we combine these techniques to create hybrid photo-lidar canopy height models (CHMs). First, photogrammetric digital surface models (DSMs) created using automated stereo-matching were registered to corresponding lidar digital terrain models (DTMs). Photo-lidar CHMs were then produced by subtracting the lidar DTM from the photogrammetric DSM. This approach opens up the possibility of retrospective mapping of forest structure using archived aerial photographs. The main objective of the study was to evaluate the accuracy of photo-lidar CHMs by comparing them to reference lidar CHMs. The assessment revealed that stereo-matching parameters and left-right image dissimilarities caused by sunlight and viewing geometry have a significant influence on the quality of the photo DSMs. Our study showed that photo-lidar CHMs are well correlated to their lidar counterparts on a pixel-wise basis (r up to 0.89 in the best stereo-matching conditions), but have a lower resolution and accuracy. It also demonstrated that plot metrics extracted from the lidar and photo-lidar CHMs, such as height at the 95th percentile of 20 m×20 m windows, are highly correlated (r up to 0.95 in general matching conditions). [ABSTRACT FROM AUTHOR]

An object-based approach was utilized in the development of two urban land-cover classification schemes on high-resolution (0.6 m), true-colour aerial photography of the Phoenix metropolitan area, USA. An initial classification scheme was heavily weighted by standard nearest-neighbour (SNN) functions generated by samples from each of the classes, which produced an enhanced accuracy (84%). A second classification was developed from the initial classification scheme in which SNN functions were transformed into a fuzzy-rule set, creating a product transportable to different areas of the same imagery, or for land-cover change detection with similar imagery. A comprehensive accuracy assessment revealed a slightly lower overall accuracy (79%) for the rule-based classification. We conclude that the transportable classification scheme is satisfactory for general land-cover analyses; yet classification accuracy can be enhanced at site-specific venues with the incorporation of nearest-neighbour functions using class samples. [ABSTRACT FROM AUTHOR]

Remotely sensed data have been utilized for environmental change study over the past 30 years. Large collections of remote sensing imagery have made it possible for spatio-temporal analyses of the environment and the impact of human activities. This research attempts to develop both conceptual framework and methodological implementation for land cover change detection based on medium and high spatial resolution imagery and temporal trajectory analysis. Multi-temporal and multi-scale remotely sensed data have been integrated from various sources with a monitoring time frame of 30 years, including historical and state-of-the-art high-resolution satellite imagery. Based on this, spatio-temporal patterns of environmental change, which is largely represented by changes in land cover (e.g., vegetation and water), were analysed for the given timeframe. Multi-scale and multi-temporal remotely sensed data, including Landsat MSS, TM, ETM and SPOT HRV, were used to detect changes in land cover in the past 30 years in Tarim River, Xinjiang, China. The study shows that by using the auto-classification approach an overall accuracy of 85-90% with a Kappa coefficient of 0.66-0.78 was achieved for the classification of individual images. The temporal trajectory of land-use change was established and its spatial pattern was analysed to gain a better understanding of the human impact on the fragile ecosystem of China's arid environment. [ABSTRACT FROM AUTHOR]

Using appropriate techniques to fill the data gaps in SLC-off ETM+ imagery may enable more scientific use of the data. The local linear histogram-matching technique chosen by USGS has limitations if the scenes being combined exhibit high temporal variability and radical differences in target radiance due, for example, to the presence of clouds. This study proposes using an alternative interpolation method, the kriging geostatistical technique, for filling the data gaps. The case study shows that the ordinary kriging techniques may provide a powerful tool for interpolating the missing pixels in the SLC-off ETM+ imagery. While the standardized ordinary cokriging has been shown to be particularly useful when samples of the variable to be predicted are sparse and samples of a second, related variable are plentiful, the case study demonstrates that it provides little improvement in interpolating the data gap in the SLC-off imagery. [ABSTRACT FROM AUTHOR]

We demonstrate the potential of monitoring coherent wave fronts on the tropical ocean surface using an advanced detection algorithm applied to visible sunglint-affected imagery from geostationary satellites. Data from MeteoSat are processed with the advanced analysis technique to reveal transient wave activity near and to the west of the Mascarene Ridge of the western Indian Ocean. The technique is based on a wavelet-based singularity analysis which enhances the subtle but highly coherent wave patterns in the meteorological satellite data. It is likely that the patterns represent internal ocean waves, a pathway to ocean mixing and climate. The prospect for using this detection algorithm in greatly expanded satellite global surveys of wave activity is outlined. [ABSTRACT FROM AUTHOR]

Sandy deserts and desertified lands (SDDL) cover most of north-western China, and desertification is a severe environmental issue in this region. In this study, we classify SDDL into mobile, semi-mobile, semi-anchored, and anchored classes, and divide the corresponding severity of desertification into four levels: extremely severe, severe, moderate, and slight. Using SDDL databases derived from Landsat TM and ETM images in 1986 and 2000, we discuss the evolution of SDDL and the processes and causes of desertification. The desertified area increased by 16 101 km2 between 1986 and 2000, and most of the desertified land was classified as severely or extremely severely desertified. However, 3437 km2 of SDDL were also rehabilitated during this period. The area of SDDL increased in the Qinghai and Xinjiang regions, and in the western part of Inner Mongolia, but decreased in the Shaanxi, Gansu, and Ningxia regions. The causes of desertification differed among regions, and different measures to control desertification are proposed for the different regions. [ABSTRACT FROM AUTHOR]

The Brahmaputra River is one of the largest sand-bar braided rivers in the world having a seasonal high discharge, high sediment load and characterized by frequent channel pattern changes and shift. Following natural phenomena, the river has been responding to landform/ground level changes effected by tectonic activity of the region. A satellite image mosaic and temporal Landsat data enabled detailed mapping of the Brahmaputra River revealing some prominent features with regard to change in its course, new channels and influence of landforms and tectonics. Northward shift of the river course are inferred at three different places, which are (1) in the uppermost part of Brahmaputra; (2) in the region north of the Kopili Gap (KG); (3) and north of the Mikir Hills (MH). However, the river appears to have developed a tendency to shift back in the area north of the MH. These shifts are influenced by tectonic activity in the Himalayan foothills. The Brahmaputra River shows remarkable changes downstream of Guwahati, by forming multiple channels south of the island as well as the formation of channels closer to the Shillong Plateau. The sudden and sharp south-westward to southward turn of the river just after longitude 90° E is in fact caused by a landform north of Dhubri. Development of new channels from the main river along the western and northern edges of the Shillong Massif (SM) and the shift of Kopili River towards the SM are indicators of tectonic activities that the massif suffered. [ABSTRACT FROM AUTHOR]

The “watershed transformation” is a well‐known powerful tool for automated image segmentation. However, it is often computationally expensive and can produce over‐segmentation in situations of high gradient noise, quantity error and detailed texture. Here, a new method has been designed to overcome these inherent drawbacks. After pre‐processing the imagery using a nonlinear filter in order to filter the noise, an optimized watershed transformation is applied to provide an initial segmentation result. Then, a multi‐scale, multi‐characteristic merging algorithm is used to refine the segmentation. Preliminary results show promise in term of both segmentation quality and computational efficiency. [ABSTRACT FROM AUTHOR]

Five aspatial and spatial classification methods were compared in this study: standard per‐pixel maximum likelihood classification; Kettig and Landgrebe's ECHO classification; maximum likelihood classification using the segment mean; classification using the segment divergence index; and maximum likelihood classification using the segment probability density function (PDF). The five classification methods were compared using test data from digital aerial imagery with a nominal 1‐m pixel size, and four multispectral bands, acquired over Morgantown, West Virginia, USA. Classification using the segment divergence index produced the lowest accuracy, followed by ECHO, standard maximum likelihood classification and classification with segment mean. The highest accuracy was obtained from classification using the segment PDF. [ABSTRACT FROM AUTHOR]

A methodology is proposed, to assess land surface cover classification using a geostatistical methodology of stochastic simulation, direct sequential cosimulation, to combine field observations with remotely sensed data classified with the classical algorithm of maximum likelihood classification. This procedure has two main advantages: (1) incorporation of a spatial continuity statistics; and (2) integration of different scales of information, contained in polygons (training areas) and point information (field observations), which also involves different qualities of information that is less reliable and more reliable, respectively. Moreover, this methodology allows production not only of a classified map, but also of maps of occupation proportions and of uncertainty for each thematic class. Local co‐regionalization models are applied to account for local differences in both field data availability and distribution, and the correlation between these hard data and the classified satellite images as soft data. The methodology is based on two criteria: the influence of the hard data dependent on their availability and proportional to their proximity; and the influence of the soft data dependent on their local correlation to the hard data. The method is applied to a study of four economically important forest tree species on the Setúbal Peninsula (south of Lisbon, Portugal). The results show more contiguous forest covers, i.e . more spatial contiguity, than the classical classification. In comparison to a contemporary field inventory, the proposed method improved forest cover estimations, showing a difference of only 3%. [ABSTRACT FROM AUTHOR]

SPOT VEGETATION is a recent sensor at 1 km resolution for land surface studies. Cloud detection based on this sensor is complicated by the absence of a thermal band. An artificial neural network was thus trained for the cloud detection on atmospherically corrected S1 daily data and on top of the atmosphere reflectance P data, from the SPOT VEGETATION system. It consists of a multi-layer perceptron with one hidden sigmoid layer, trained with the Levenberg-Marquardt back-propagation algorithm and generalized by the Bayesian regularization. Two neural networks allowed optimal cloud detections to be obtained. The first used all four bands of S1 data with 13 hidden nodes, and the second employed all four bands of P data with 11 hidden nodes. The multiple-layer perceptrons lead to a cloud detection accuracy of 98.0% and 97.6% for S1 and P data, respectively, when trained to map three predefined values that classify cloud, water and land. The network was further evaluated using three SPOT VEGETATION images taken at different rates. The network detected not only bright thick clouds but also thin or less bright clouds. The analysis demonstrated the superior classification of the network over the standard cloud masks provided with the data. [ABSTRACT FROM AUTHOR]

A damage detection method using middle-resolution satellite images, the Image Fluctuation Model method, is proposed, which employs a stochastic model of the digital number (DN) fluctuation in a normal condition and its significance test. The DN fluctuation model is formulated by considering an imaging process of a satellite sensor and an image registration process. A resulting thematic map is created based on a confidence level (1-significance level), which is defined on a pixel-by-pixel basis as follows: the minimum significance level at which the null hypothesis, that the pixel DN can be considered as a sample of the DN fluctuation model, is rejected. The confidence level provides the model-based probability of ground surface change. The method is applied to the 2003 Bam, Iran earthquake using images acquired by Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) of Terra. The receiver operating characteristic curves of the method showed better detection performance than temporal image differencing or temporal image ratioing. Though the detection performance of building damage was not comparable to visual inspection on a building basis using high-resolution images of QuickBird, the confidence level map shows similarity at the district level to damage assessment results using high-resolution images. [ABSTRACT FROM AUTHOR]

Subsurface and surface coal fires form serious environmental, economic and safety problems in coal-producing countries like China and India. Remote sensing offers the possibility of detecting and studying thermal anomalies due to coal fires. Emissivity plays an important role in determining the surface temperature of a body using remotely sensed data. In the present study an attempt is made to use satellite-derived emissivity to estimate the surface temperature in Wuda, north China. With the use of multispectral thermal Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data (five bands in 8.125–11.65 µm region) in combination with a Temperature/Emissivity Separation (TES) algorithm, the anomalous pixels due to coal fires can be extracted from the background to achieve a qualitative study of coal fires. In the present study, during night-time overpass of ASTER, satellite images have been recorded and simultaneous field measurements were collected. These field measurements were used to process the satellite thermal data and to validate the results obtained. Using the TES approach, satellite-based temperature corresponded well with actual field measurements at selected locations. [ABSTRACT FROM AUTHOR]