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A sensor installation and evaluation programme at the Institute of Ocean Sciences, Sidney, BC, Canada, shows that the weather buoys operating round Canada's coasts can be used as important new sources of surface information for satellite sensor calibration and validation. The standard instruments on the buoys provide real-time measurements to forecasters--of wind, waves, water and air temperature and air pressure. The present programme is evaluating the addition of sensors measuring chlorophyll fluorescence, Photosynthetically Available Radiation (PAR), salinity and water-leaving radiance. Time-series of these data are also of value for a variety of fisheries, ecosystem and climate studies. Data can be viewed on the web at http://www-sci.pac.dfo-mpo.gc.ca/ecobuoys. [ABSTRACT FROM AUTHOR]

The technique of vicarious calibration is used in connection with an atmospheric correction to improve the Sea viewing Wide Field of view Sensor (SeaWiFS) normalized water-leaving radiance by the first determination of mean vicarious calibration coefficients from in situ measurements in the Baltic Sea. A necessary adjustment of the SeaWiFS pre-flight calibration slope was found to be +3.5%, +0.3%, -1.7%, -0.4%, +0.8% and -1.3% for the first six SeaWiFS channels. The derived mean vicarious calibration coefficients are higher than the coefficients in the standard SeaWiFS Data Analysis System (SeaDAS) software but with similar shape and good agreement with other research results. The coefficients were used to obtain better normalized water-leaving radiance from SeaWiFS measurements in the Baltic Sea. The deviations of calculated to measured radiances in the open Baltic Sea are between 3% and 47% in the channels 412 to 670 nm, with the trend of higher deviations in the blue channels. The objective of radiance determination in all SeaWiFS channels with a maximum uncertainty of 5% in clear water regions is probably not reachable in the turbid water of the Baltic Sea. [ABSTRACT FROM AUTHOR]

Synoptic coverage of the temporal and spatial variability of aerosol distribution patterns can only be achieved with satellites. Results from the first ocean colour sensor, the Coastal Zone Color Scanner (CZCS), indicate an annual cycle of the major mineral aerosol plumes that is consistent with the published literature. Seasonality and interannual aerosol variability observed with the CZCS agrees well with that found by ground data measurements and other satellite platforms used to monitor aerosols. The successor to the CZCS--the Sea viewing Wide Field of view Sensor (SeaWiFS)--provides estimates of aerosol load and particle size, both on a global scale. Seasonal maps of both of these aerosol optical properties are in accord with well-known distribution patterns and also with independent satellite estimates. These results indicate that ocean colour sensors are capable of monitoring the variability of global aerosol loads and, more recently, with the retrieval of aerosol particle size, they can be used to characterize different aerosol events. [ABSTRACT FROM AUTHOR]

The salinity of the open ocean is important for understanding ocean dynamics and for modelling energy exchange with the atmosphere. But existing data are sparse and much of the ocean is unsampled. Sea surface salinity can be measured remotely with passive microwave sensors operating near 1.4 GHz (L-band). Salinity differences have been observed from space and aircraft instruments have demonstrated that salinity can be measured with an accuracy of better than 1 psu. Sensor technology has improved sufficiently to seriously propose a satellite system to map salinity over the open oceans. [ABSTRACT FROM AUTHOR]

For the year 1999 all Sea viewing Wide Field of view Sensor (SeaWiFS) scenes of the Danish waters from the North Sea to the Baltic Sea were browsed, and a total of 47 SeaWiFS scenes with reasonably low cloud cover and, therefore, potential in situ match-ups were found and processed. The in situ data used as match-ups were collected on routine monitoring cruises by Danish and Swedish environmental authorities. A few stations in the North Sea, Skagerak and the western Baltic Sea were sampled, while most stations were located in Kattegat and the inner Danish waters. A turbid water SeaWiFS atmospheric correction algorithm was applied, since the standard SeaWiFS algorithm for chlorophyll- a (CHL) has been shown to be fairly inaccurate in turbid coastal waters. This is due to both inaccurate atmospheric and to relatively high and variable abundance of yellow substance. The application of the turbid atmospheric correction substantially improved the SeaWiFS CHL estimates. Regressions between SeaWiFS estimates using the OC2 and OC4 algorithms used in the SeaDAS software (versions 3.3 and 4.0, respectively) and in situ CHL values were made as well, and regression with a number of other possible reflectance ratios with SeaWiFS channels. The best correlation was found to be R 2 =0.54 using a double-ratio algorithm using both R510/R555 and R443/R670, while the OC4v4 had the second best correlation of R 2 =0.39. Among other single ratios, the R510/R555 had the highest correlation with CHL, which was expected since this is also the ratio that OC4v4 most often switches to in the waters investigated here. The range of CHL concentrations in this study was rather limited (all but three points from 0.5-3 mg m -3 ) so there is a need for inclusion of more data to expand the concentration range. This should be possible using also data from 2000, 2001 and onwards and, hereafter, a more 'stable' empirical algorithm can be derived for the Danish waters. [ABSTRACT FROM AUTHOR]

The National Aeronautics & Space Administration (NASA) Sea viewing Wide Field of view Sensor (SeaWiFS) began operational measurement of ocean colour in September 1997. Upgrades to the SeaWiFS data processing system (SeaDAS) have occurred frequently and the effects of these revisions on the remotely sensed estimates of chlorophyll- a concentration (chl- a ) have been significant. Measurements of chl- a from research work in the Bay of Biscay and Gulf of Cadiz during 1998-1999 are used to validate the SeaWiFS chl- a product generated using the current version of SeaDAS (version 4.1). The validation data cover coastal and offshore waters, including those dominated by inorganic suspended sediment, and an intense dinoflagellate bloom where shipboard chl- a measurements exceeded 50 mg m -3 . The standard SeaWiFS chlorophyll algorithm (OC4v4) generally performed well, but significantly over-estimated chl- a where inorganic suspended sediment was present. The algorithm is only applicable to chl- a values up to 64 mg m -3 , which was less than chl- a at the centre of the bloom. A novel algorithm for chl- a , which first estimates the inherent optical properties of the water, was applied to the SeaWiFS measurements but failed on over 90% of the pixels, perhaps because SeaWiFS is under-estimating water reflectance at the extreme blue end of the visible spectrum. [ABSTRACT FROM AUTHOR]

One of the limitations of remote sensing is the low spatial resolution of the open-access multispectral sensors, generating a mixture of spatial information. The mixed pixels can be modelled as the linear combination of fundamental components, called endmember, with a weighted contribution or abundance. The development of linear unmixing algorithms considering spatial and spectral information has recently increased. Some unmixing methods have relied on segmentation to integrate spatial data, and one of the most used is superpixel-based segmentation. However, previous work in superpixel-based unmixing focuses on using superpixels as uniform regions. Commonly, linear unmixing is used on hyperspectral imagery, and limited literature is found with multispectral images. This paper aims to propose a new preprocessing approach for multispectral linear unmixing called Superpixel Linear Independent Preprocessing. The proposed approach generates a set of candidates to endmembers based on spatial-spectral information; these are the input of traditional endmember extraction methods for multispectral unmixing. Experimental results show that the proposed preprocessing improves the performance of endmember extraction. [ABSTRACT FROM AUTHOR]

The advancement of ultra-hyperspectral imaging technology, exemplified by the AisaIBIS sensor, has enabled a leap from hyperspectral data (hundreds of bands) to ultra-hyperspectral data (thousands of bands). It provides immense potential for precise ground object recognition within intricate scenes. However, the complexities inherent to features of the ground objects, coupled with the copious redundant information within the ultra-hyperspectral data, pose substantial challenges for accurate object recognition. Therefore, this paper proposed a comprehensive framework to explore the optimal precise classification strategy of ultra-hyperspectral data in complex scenes (12 vegetation and non-vegetation classes). (a) Our investigation delves into the influence of diverse feature subsets and a range of machine learning classifiers on the precision of ground objects recognition. The proposed strategy is up to an overall accuracy of 88.44%, effectively avoiding the curse of dimension, and significantly enhancing the capability to recognize the complex ground objects. (b) Furthermore, based on the simulation of hyperspectral images with different spectral resolutions, we compared the classification results of ultra-hyperspectral data (0.11 nm) and the hyperspectral datasets (10 nm, 5 nm, and 1 nm) by machine learning methods. Compared with the hyperspectral datasets, ultra-hyperspectral data improved the classification accuracy by 5.30–6.38%. This substantiates the pronounced advantages of ultra-hyperspectral data in precision land cover classification. This study provides a valuable reference for the application of ultra-hyperspectral data in recognition of complex ground objects scenes, and urban accurate monitoring. [ABSTRACT FROM AUTHOR]

Ship detection for SAR imagery plays a crucial rule in the field of remote-sensing image processing. Superpixel methods have attracted enormous interest in the recent years, resulting in superpixel-based CFAR (Constant False Alarm Rate) ship detection methods for SAR images become a hot research issue. However, existing methods take superpixel generation as a preprocessing, and CFAR detection is essentially carried out at the pixel level, and the influence of speckle noise has not been fundamentally overcome. This paper innovatively proposed a real superpixel-level CFAR detection method, which takes superpixels instead of pixels as the minimum processing primitives on the basis of reliable superpixel segmentation. Firstly, in order to obtain the background superpixel region of each under test superpixel more accurately and efficiently, we designed a ring topology, which was specially developed for non-Euclidean structure data like superpixels. Secondly, the Johnsonsu function is used to fit the superpixel clutters from the background ring, and the detection threshold was obtained by a given false alarm rate. Finally, the candidate targets were further optimized to carry out accurate detection results. Experimental results based on multiple sets of real SAR images shown that our method has significant advantages in terms of detection rate and false alarm rate compared to the traditional K-CFAR and the existing superpixel-based CFAR methods. [ABSTRACT FROM AUTHOR]

Recently, there has been renewed interest in object detection in computer vision. Several attempts have been made to improve small objects recognition, particularly in aerial images, due to their weak representative features and low resolution. However, these instances are still a challenge. The principal objective of this paper is to introduce a Fused RetinaNet detector, an enhanced RetinaNet with a novel context fusion module instead of the feature pyramid network (FPN), to improve low layers semantic information and top layers spatial resolution. This module firstly aggregates multi-scale backbone feature maps at once to make a robust shallow layer. Then, a parallel-branch dilated module is proposed in front of each network level to present more context information. Finally, aggregation and dilated modules are laterally connected at each backbone level via a bottom-up path approach to compensate for information loss during downsampling. Comprehensive experiments are carried out on NWPU VHR-10 and DOTA aerial image datasets. The evaluation reveals the proposed Fused RetinaNet superiority in aerial images small target detection. It achieves a mean average precision (mAP) value of 91.83% and 57.13% on NWPU VHR-10 and DOTA, respectively. Also, it scores a mAP of 62.59% on DOTA when the k-means clustering algorithm is used. [ABSTRACT FROM AUTHOR]

The fusion of hyperspectral (HS) and multispectral (MS) images with complementary high spectral and high spatial resolution information has been successfully applied to improve the low spatial resolution limitation of current satellite sensors with high spectral resolution. Fusion methods based on spectral unmixing have led to state-of-the-art results with the advantage of obtaining pure spectral signatures and their proportion in the image of analysis as a result of the fusion process. However, pure spectral signatures observed in HS images are affected by variations in atmospheric, environmental and illumination conditions which are significant sources of errors in HS image analysis. Conventional unmixing-based fusion methods neglect the spectral variability of HS images introducing and propagating errors through the fusion process, affecting further inference tasks such as classification, target detection, and change detection. Therefore, this paper proposes HS-MS image fusion algorithm based on spectral unmixing accounting for spectral variability through an augmented linear mixing model (ALMM). An alternating optimization strategy is combined with the alternating direction method of multipliers (ADMM) to efficiently minimize the underlying cost function of the fusion problem. Simulation results on realistic data demonstrate that the proposed method outperforms state-of-the-art fusion methods in different metrics. Additionally, the fusion of crop images acquired with DESIS and Sentinel-2 sensors in a region of Colombia confirms the advantages of the proposed fusion method. [ABSTRACT FROM AUTHOR]

Fast and accurate object detection in aerial images remains a challenging task. Usually, to better describe an object, oriented bounding boxes (OBBs) can better fit objects. Due to high background complexity and large object scale variation, single-angle anchor-based two-stage detectors are widely adopted, which offer better accuracy. However, the single-angle prediction has a small error tolerance for objects with a large aspect ratio, and the hyperparameters of the anchor-based network are difficult to adjust, and the number of hyperparameters is extremely large. Furthermore, the two-stage detection inference speed is slow, and it is difficult to achieve real-time detection. In this paper, we propose Dual-Det, a keypoint-based oriented object detector. We firstly propose a dual-angle with a short-side and ratio regression strategy (DASR), which uses the object centre and the length and angles of two diagonals to represent an object. A short side guided (SSG) loss is further added to guide the direction of the diagonal regression box. To improve the detection performance for dense and tiny objects, a lightweight supervised pixel attention learner is finally proposed. The experiment results show that Dual-Det achieves 90.23 % mAP at 46FPS on HRSC2016, 90.83 % mAP at 46FPS on UCAS-AOD and 72.00 % mAP at 0.018 s per image in the inference phase on DOTA. The code will be open source on . [ABSTRACT FROM AUTHOR]

Fast and precise object detection for hgigh-resolution aerial images has been a challenging task over the years. Due to the sharp variations in object scale, rotation, and aspect ratio, most existing methods are inefficient and imprecise. In this paper, we propose a different approach polar method. We locate an object by centre-point, direct it by four polar angles, and measure it by polar ratio system. Our polar coordinate-based method, PolarDet, is a faster, simpler, and more accurate one-stage object detector. Also, our detector introduces a sub-pixel centre semantic structure to further improve classifying veracity. PolarDet achieves nearly all state-of-the-art (SOTA) performance in aerial object detection tasks with faster inference speed. In detail, our approach obtains the SOTA results on authoritative remote sensing object detection datasets DOTA, UCAS-AOD, and HRSC2016 with 76.64% mAP (mean average precision), 97.01% mAP, and 90.46% mAP respectively. Most noticeably, our PolarDet gets the best performance and reaches the fastest speed (32fps) at the UCAS-AOD dataset. [ABSTRACT FROM AUTHOR]

Coastal communities, land covers, and intertidal habitats are vulnerable receptors of erosion, flooding or both in combination. This vulnerability is likely to increase with sea level rise and greater storminess over future decadal-scale time periods. The accurate, rapid, and wide-scale determination of shoreline position, and its migration, is therefore imperative for future coastal risk adaptation and management. This paper develops and applies an automated tool, VEdge_Detector, to extract the coastal vegetation line from high spatial resolution (Planet's 3 to 5 m) remote-sensing imagery, training a very deep convolutional neural network (holistically nested edge detection), to predict sequential vegetation line locations on annual to decadal timescales. Red, green, and near-infrared (RG-NIR) was found to be the optimum image spectral band combination during neural network training and validation. The VEdge_Detector outputs were compared with vegetation lines derived from ground-referenced positional measurements and manually digitized aerial photographs, which were used to ascertain a mean distance error of <6 m (two image pixels) and >84% producer accuracy (PA) at six out of the seven sites. Extracting vegetation lines from Planet imagery of the rapidly retreating cliffed coastline at Covehithe, Suffolk, United Kingdom, has identified a landward retreat rate >3 m year−1 (2010–2020). Plausible vegetation lines were successfully retrieved from images in The Netherlands and Australia, which were not used to train the neural network, although significant areas of exposed rocky coastline proved to be less well recovered by VEdge_Detector. The method therefore promises the possibility of generalizing to estimate retreat of sandy coastlines from Planet imagery in otherwise data-poor areas, which lack ground-referenced measurements. Vegetation line outputs derived from VEdge_Detector are produced rapidly and efficiently compared to more traditional non-automated methods. These outputs also have the potential to inform upon a range of future coastal risk management decisions, incorporating future shoreline change. [ABSTRACT FROM AUTHOR]

Crop mapping through classification of Satellite Image Time-Series (SITS) data can provide precious information for several agricultural applications, such as crop monitoring, yield forecasting, and crop inventory. However, several issues affect the classification performance of SITS data. As one of the most challenging problems, constituent images of time-series provide different levels of information about crops. These differences are the result of dynamic spectral responses of crops and also the variable atmospheric and sensor conditions. The second issue is the unavailability of adequate high-quality samples for training the classifier. In this study, we proposed a novel computationally efficient Multi-Domain Active Learning (MDAL) method which takes advantage of Multiple Kernel Learning (MKL) and Active Learning (AL) algorithms to address these two issues. The proposed method uses MKL algorithms to address the issues associated with different information level of the data, which generally cannot be modelled using the well-known classification algorithms. AL algorithms were also used for semi-automatic selection of training samples. However, most of the MKL algorithms are very computationally demanding. Consequently, using them in the MDAL method can dramatically increase the computational costs. Thus, in this paper, we presented the similarity-based MKL algorithms. Thanks to their low computational complexities, these algorithms are the most suitable MKL algorithms that can be used in the MDAL method. We evaluated the proposed method using two multispectral SITS datasets, acquired by RapidEye and SPOT sensors. The obtained results of the MDAL method for these datasets respectively showed 8.2% and 5.87% increase in the overall accuracy of classification as compared to the accuracy of the standard AL algorithm. The results also showed that in the case of adopting the SimpleMKL algorithm (a common MKL algorithm in the literature) the computational time of the MDAL method is 577 and 474 seconds for RapidEye and SPOT datasets, respectively. However, in the case of adopting the similarity-based MKL algorithms, these computational times respectively decreases to 4 and 2 seconds. [ABSTRACT FROM AUTHOR]

This paper presents a novel adaptive spatially constrained fuzzyc-means (ASCFCM) algorithm for multispectral remotely sensed imagery clustering by incorporating accurate local spatial and grey-level information. In this algorithm, a novel weighted factor is introduced considering spatial distance and membership differences between the centred pixel and its neighbours simultaneously. This factor can adaptively estimate the accurate spatial constrains from neighbouring pixels. To further enhance its robustness to noise and outliers, a novel prior probability function is developed by integrating the mutual dependency information in the neighbourhood to obtain accurate spatial contextual information. The proposed algorithm is free of any experimentally adjusted parameters and totally adaptive to the local image content. Not only the neighbourhood but also the centred pixel terms of the objective function are all accurately estimated. Thus, the ASCFCM enhances the conventional fuzzyc-means (FCM) algorithm by producing homogeneous regions and reducing the edge blurring artefact simultaneously. Experimental results using a series of synthetic and real-world images show that the proposed ASCFCM outperforms the competing methodologies, and hence provides an effective unsupervised method for multispectral remotely sensed imagery clustering. [ABSTRACT FROM PUBLISHER]

Many factors influence the quality and value of a classification accuracy assessment and evaluation programme. This paper focuses on the size of the testing set(s) used with particular regard to the impacts on accuracy assessment and comparison. Testing set size is important as the use of an inappropriately large or small sample could lead to limited and sometimes erroneous assessments of accuracy and of differences in accuracy. Here, some of the basic statistical principles of sample size determination are outlined, including a discussion of Type II errors and their control. The paper provides a discussion on some of the basic issues of sample size determination for accuracy assessment and includes factors linked to accuracy comparison. With the latter, the researcher should specify the effect size (minimum meaningful difference in accuracy), significance level and power used in an analysis and ideally also fit confidence limits to derived estimates. This will help design a study and aid the use of appropriate sample sizes, as well as facilitate interpretation of results. In particular, it will help avoid problems, such as under-powered analyses, and provide a richer information base for classification evaluation. The paper includes equations that could be used to determine sample sizes for common applications in remote sensing, using both independent and related samples. [ABSTRACT FROM AUTHOR]

This paper presents the background to the special issue of the International Journal of Remote Sensing which is being published to celebrate the 20th anniversary of the coming into effect in January 1989 of the Montreal Protocol on substances that damage the amospheric ozone layer. Starting from the discovery of ozone and the ozone layer, we recall the proposition of Molina and Rowland that man-made CFCs pose a major threat to the ozone layer. This was followed by about 15 years of scientific research, scientific discussion, intense political discussions and international negotiations which led to the formulation of the Montreal Protocol that prohibits the manufacture and use of ozone-destroying substances. The papers in this special issue of the Journal are concerned with addressing the role of remote sensing in monitoring and assessing the success, or otherwise, of the Montreal Protocol. [ABSTRACT FROM AUTHOR]

This paper describes the state of the art in land mine detection technology and algorithms. Landmine detection is a growing concern due to the danger of buried landmines to people's lives, economic growth and development. Most of the injured people have no connection with the reason why the mines were placed. There are 50-100 million landmines in more than 80 countries around the world. Deactivation is estimated at 100 000 mines per year, against the nearly 2 million mines laid annually. In this paper we describe and analyse sensor technology available including state-of-the-art technology such as ground penetrating radar (GPR), electromagnetic induction (EMI) and nuclear quadrupole resonance (NQR) among others. Robotics, data processing and algorithms are mentioned, considering support vectors, sensor fusion, neural networks, etc. Finally, we establish conclusions highlighting the need to improve not only the way images are acquired, but the way this information is processed and compared. [ABSTRACT FROM AUTHOR]

The detection of surfaces cultivated with coca plants is essential to all activities related to monitoring and combating the production of illicit drugs such as cocaine. Moreover, deforestation processes can often be associated with the presence of coca plantations in the Andean region. In this context, the use of remote sensing technologies has proven to be effective for building an operational monitoring capacity, but more effort is needed to establish a robust computational frame for automatic detection procedures. This paper contributes to this effort by analysing the discrimination power of different textural measures and by proposing a new Mature Coca Index (MCI) derived from the textural analysis of 1-m resolution satellite imagery. The approach can be applied to the latest generation optical sensors such as IKONOS and QUICKBIRD, and improves the current state-of-the-art of recognition procedures for satellite data based on radiometric discrimination. The paper describes the MCI concept, its implementation, and application to a data sample derived from the IKONOS sensor. [ABSTRACT FROM AUTHOR]

In this paper, the characteristics of vegetation cover and variation in China have been studied by using the AVHRR NDVI time-series data from 1981 to 2001. The Harmonic Analysis of Time Series (HANTS) method was successfully applied to eliminate the clouds on remote sensing data and reconstruct cloud-free time series images. Then, the Fourier components of NDVI time series data were calculated. Finally, the physical meaning of Fourier components was analysed, and the relationship between Fourier components and land vegetation cover variation was investigated. The mean NDVI, or zeroth-order harmonic, indicates overall vegetation cover level. The first harmonics of the HANTS summarizes the amplitude and phase of annual values of NDVI data, and the second harmonics of the HANTS summarizes those of biannual values of NDVI data. The amplitude of the first harmonic indicates the variability of vegetation productivity over the year. The phase of the first harmonic summarizes the timing of vegetation green-up, while the second harmonic indicates the strength and timing of biannual vegetation cover variation. The Fourier components calculated by HANTS algorithm reveal the vegetation distribution and growing cycle characteristics. The physical meaning of Fourier components are significant to the land-surface vegetation variation study of China. The methodology proposed in this paper is an effective method for the processing, analysis and application of long-time-series remote sensing data. [ABSTRACT FROM AUTHOR]

In this paper we present the results of time series analysis for a coal mining region based on partial unmixing. We test the method also known as mixture tuned matched filtering on an eight image Landsat 5 TM and Landsat 7 ETM+ time series covering the period from 1987 to 2003. Common change detection methods often include the comparison of two interactively generated classification results, such as derived from Maximum Likelihood classification. These approaches often yield highly accurate results. However, disadvantages include a strong analyst bias and hardly repeatable results. For a quantitative monitoring of a single surface class' development over time they are often not recommendable. Our goal was to test an unbiased quantitative way to assess the development of coal surfaces, such as outcropping coal seams, coal storage piles, coal waste piles, and coal washery discard, within multiple date satellite imagery. Partial unmixing approaches were developed to detect one or few target materials surrounded by—or mixed with—an unknown background material. The main advantage is that only the spectral characteristics of the material of interest must be known, and the desired material can furthermore occur with subpixel coverage. Crisp pixel classificators like maximum likelihood on the other hand require knowledge of all classes. They can only map materials which dominate a pixel. Linear unmixing procedures such as partial unmixing require a thorough radiometric pre-processing of data. Furthermore, the accuracy and representativity of selected input spectra must be granted. In this paper we demonstrate that partial unmixing is a powerful method to detect and extract single landcover classes of interest relatively fast and unbiased. The subpixel fraction percentages should be interpreted in a relative way only. We furthermore show that partial unmixing represents a standardized method for time series analyses and allows for a quantitative assessment of the temporal development of an area. Challenges lie in the validation of partial unmixing results, which we realized through thresholding of unmixing results and accuracy assessment with ground truth polygons mapped in situ. Furthermore, we performed an indirect comparison with results of a multi-endmember unmixing. [ABSTRACT FROM AUTHOR]

This paper proposes the work flow of multi-scale information extraction from high resolution remote sensing images based on features: rough classification - parcel unit extraction (subtle segmentation) - expression of features - intelligent illation - information extraction or target recognition. This paper then analyses its theoretical and practical significance for information extraction from enormous amounts of data on a large scale. Based on the spectrum and texture of images, this paper presents a region partition method for high resolution remote sensing images based on Gaussian Markov Random Field (GMRF)-Support Vector Machine (SVM), that is the image classification based on GMRF-SVM. This method integrates the advantages of GMRF-based texture classification and SVM-based pattern recognition with small samples and makes it convenient to utilize a priori knowledge. Finally, the paper reports tests on Ikonos images. The experimental results show that the method used here is superior to GMRF-based segmentation in terms of both the time expenditure and processing effect. In addition, it is actually meaningful for the stage of information extraction and target recognition. [ABSTRACT FROM AUTHOR]

Colour is widely used in remote sensing work. In many instances, the use of colour conveys additional information both visually and scientifically. Remote sensing satellites view the earth in different spectral bands, viz. near infrared (NIR), red, green, and blue bands, in a conventional multispectral imaging system. In the absence of a blue channel, colour images can be generated using near infrared, red, and green bands in what is known as a false colour composite (FCC) and does not look natural, like the image we see with the naked eye. For a trained interpreter, this does not pose any problems. However, when the intended use is a fly‐through of a draped terrain, visual interpretation, or a display, meant for the non‐remote sensing professional, this becomes a handicap. To overcome this, there is a requirement to generate natural colour composites (NCC) from the given false colour composite, which demands the simulation of a blue band to be combined with green and red bands. This paper describes a unique method of generating a blue band to form natural colour images from a given false colour image set. We use a spectral transformation method to establish a relationship between the false colour and true colour image pairs provided by a sensor with all the four bands, which has a broader spectral coverage. A transformation function is fitted by selecting radiometric control points along the line of geometric registration to find a set of coefficients to be used for simulating a blue band. This blue band, along with the green and red bands, provides a near true colour or ‘ natural colour ’ on the display. In this paper, we present a set of adjustable radiometric transformation coefficients to accommodate variation in spatial and dynamic range offered by sensors to generate natural colour. These coefficients seem to work on a large number of images of different seasons, provided similar spectral bands and terrain are used. The proposed ‘ natural colour generator ’ can be used in changing false colour images to natural colour images with the aim of ‘what you get is what you would have seen’. [ABSTRACT FROM AUTHOR]

Natural and anthropogenic processes at the Earth's surface operate at a range of spatial and temporal scales. Different scales of observation are required to match the spatial scales of the processes under observation. At the same time, the temporal sampling rate of the observing systems must be reconciled with the dynamics of the processes observed. Bringing together these issues requires insight, innovation and, inevitably, compromise. This paper reviews spatial and temporal considerations in remote sensing and introduces the papers in this Special Issue on ‘Scales and Dynamics in Observing the Environment’. The review comprises three main sections. The first section focuses on spatial variability in remote sensing, while the second section focuses on temporal variability in remote sensing. The third section links these two issues, focusing on the interplay of space and time in remote sensing. The review is primarily theoretical, explaining spatial and temporal properties of remote sensing and remotely sensed phenomena. Where appropriate, however, practical examples are included to demonstrate how remote sensing is used in environmental applications. Following the review, the papers included in the Special Issue are introduced, outlining their significance in the context of ‘Scales and Dynamics in Observing the Environment’. [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]

The orthoimage usually serves as a valuable base layer in GIS. With an increasing demand in many urban GIS applications, orthoimages in urban areas are required to represent spatial objects in their true positions. However, the traditional methods for orthoimage generation did not consider features (e.g. occlusion, shadow, etc.) of spatial objects (e.g. bridges and buildings), resulting in that spatial objects in the created orthoimages cannot be located in their true positions. This paper presents our research and experimental results of true orthoimage generation in extremely tall urban areas using lidar and multi-view large-scale aerial images. Lidar data are used for the extraction of an urban digital surface model (DSM), further for the extraction of a digital building model (DBM) and a digital terrain model (DTM). Data structure and a data model for managing urban spatial objects, such as buildings and bridges, are developed. The photogrammetric geometry is used for the detection of occluded and shadowed areas in true orthoimage generation. For the occluded and shadowed areas, lost information is compensated from a conjugate area in adjacent images, for which a new mosaicking method, which automatically chooses the 'best' imagery and automatically optimizes the seam line, has been developed. Experimental results from central Denver, Colorado and Lower Manhattan, New York City demonstrated that the proposed true orthoimage generation scheme in this paper is capable of truly orthorectifying the relief displacement in aerial images and significantly reducing occlusion and shadow defects. [ABSTRACT FROM AUTHOR]

In this paper, we propose a new algorithm for remotely sensed image texture classification and segmentation. We observe that the traditional method least square error (LSE) is unstable in practical applications. This motivates us to develop a more stable method. We have proposed the regularization technique to suppress the instability of LSE in previous research. Our contribution in this paper is that we propose a new stable method, which is based on the total variation (TV) for reducing instability in texture analysis, and apply it to remotely sensed image texture classification and segmentation. Experimental results on remotely sensed images demonstrate that our new algorithm is superior to LSE and seems promising in applications. [ABSTRACT FROM AUTHOR]

The end user often needs to define extremely complex interpretation tasks and to require the analysis results to be quantitatively proven for each pixel without the test data. To this end, this paper extends the ideas underlying the model-based unsupervised classification method previously proposed by us ( Koltunov and Ben-Dor 2001 ). Consistently with that method, the quality of assigning a pixel to a cluster is defined as the lower confidence bound (l.c.b.) of the corresponding posterior probability estimate. We propose to compute the l.c.b.s in an approximate way using the Fisher information matrix instead of the bootstrap scheme suggested previously, leading to an l.c.b.-estimation procedure that is faster by a factor of hundreds to thousands, while being reasonably accurate. The issue of selecting the number of clusters is considered in accordance with the quantitative requirements for the level of detail and the reliability of the thematic interpretation . Specifically, the l.c.b.s form a novel selection criterion that allows the most detailed landscape descriptions to be provided with at least a pre-specified value of confidence. This implies detection of highly overlapping clusters, leading to very detailed segmentations. Consequently, numerous thematic classification and object detection problems can be solved, based on single clustering and assuming that thematic classes are unions of components. Then the thematic classification accuracy can be computed in a well-founded manner for each separate pixel, using the obtained covariance matrices of the posterior probability estimators of component membership. The procedures for thematic mapping and object detection are described. The accuracy of the l.c.b. estimation and stability of the new criterion in choosing the number of clusters are illustrated on simulated datasets. The hyperspectral data analysis experiment performed demonstrates part of the developments described in this paper. Several issues that are relevant for remote sensing data interpretation are addressed constructively. In particular, we draft the novel algorithms that use model-based cluster analysis for detection and recognition of remotely sensed objects based on prior information on their size and shape. In addition, we introduce a generalized approach to unsupervised feature extraction from data acquired by a plurality of sensors of different physical nature. A new data model generalizing the traditional Gaussian mixture model is also presented. [ABSTRACT FROM AUTHOR]

Change detection from remote sensing data is often done by simple overlay of classified maps. However, such analyses can contain a significant proportion of boundary errors, especially when combining data from different sensors. This paper presents a protocol that allows reliable post-classification comparisons by taking into account classification accuracies, landscape fragmentation, planimetric accuracies, pixel sizes and grid origins. The proposed protocol has been applied, with little extra effort, in a fragmented agricultural Mediterranean zone using MSS (1970s) and TM (1990s) images. Applying the protocol, change detection had an accuracy of 85.1%, while for a direct overlay it was only 43.9% accurate. The drawback of this method is that it reduces the useful area of comparison. As the accuracy of individual classifications is critical, the paper also describes and tests a hybrid classifier that combines an unsupervised classification approach with training areas. This approach has proved more successful than maximum likelihood classifiers. [ABSTRACT FROM AUTHOR]

Over the years, making or creating a choice for a specific platform from which to conduct remote sensing observations of specific targets brings in many factors related to the target characteristics and how the data are going to be used. Attempts to measure Earth's diverse objects have generated a wide range of platform alternatives, from geostationary satellites to low-flying aircraft. Now several additional options possessing unique attributes are available: the International Space Station (ISS) and Un-inhabited Aerial Vehicles (UAVs). This paper explores some of the tradeoffs among these alternatives for the special problem of remotely sensing the littoral zone, but especially the shallow ecosystems. Though the surface area of the littoral zone is relatively large, it is geographically disbursed and somewhat linear. Also, the spatial, spectral and temporal variability of ecosystems in this zone is very high, and signals are masked by the overlying water column. Ideally, a frequent revisit time would be desirable to monitor their health and changing condition. These characteristics place important constraints on platform choice as one tries to design a system to monitor these critical ecosystems and provide useful information for managing them. This paper discusses these tradeoff issues as offered mainly by three platform choices: free-flying satellites, ISS, UAVs and other aircraft. [ABSTRACT FROM AUTHOR]

This article presents a hybrid fuzzy classifier for effective land-use/land-cover (LULC) mapping. It discusses a Bayesian method of incorporating spatial contextual information into the fuzzy noise classifier (FNC). The FNC was chosen as it detects noise using spectral information more efficiently than its fuzzy counterparts. The spatial information at the level of the second-order pixel neighbourhood was modelled using Markov random fields (MRFs). Spatial contextual information was added to the MRF using different adaptive interaction functions. These help to avoid over-smoothing at the class boundaries. The hybrid classifier was applied to advanced wide-field sensor (AWiFS) and linear imaging self-scanning sensor-III (LISS-III) images from a rural area in India. Validation was done with a LISS-IV image from the same area. The highest increase in accuracy among the adaptive functions was 4.1% and 2.1% for AWiFS and LISS-III images, respectively. The paper concludes that incorporation of spatial contextual information into the fuzzy noise classifier helps in achieving a more realistic and accurate classification of satellite images. [ABSTRACT FROM PUBLISHER]

The primary objective of this paper is to evaluate the utility of different Indian remote sensing sensors for detection, mapping and patch size estimation of Lantana camara L. (Kurri). The latter, of the family Verbinaceae, is one of the most aggressive invasive plant species and has colonized large areas of forest land in the Himalayan foothills (Shiwalik range). The State Forest Departments of India are planning to develop a suitable strategy to halt its invasion. The first step in this direction is to have accurate information on the location and spread of the plant in spatial format. The test site is part of the forest of the Rajaji National Park, Uttarakhand. Indian Remote Sensing-Linear Imaging Self-Scanning Sensor (IRS-LISS) III (multi-spectral, 23.5 m), IRS-LISS IV (multi-spectral, 5.8 m), Cartosat-1 (Panchromatic, 2.5 m) and a merged image of LISS IV and Cartosat-1 using Brovey fusion techniques were used to map Lantana camara L. Further improvement was obtained using texture analysis. The study demonstrates the potentiality of LISS IV and Cartosat-1 data for detection and mapping of Lantana camara L. The results show the feasibility of developing a semi-automated procedure to map and analyse the distribution of Lantana in forest areas. [ABSTRACT FROM AUTHOR]

In this paper radar scattering models based on coherent and incoherent formulations for an African grassland (Sahelian) are examined. The coherent model is used to account for the structure of the grass plants and the results are compared with the same model assuming random placement and orientation of scatters, and the radiative transfer model. The validity of the three models applied to grass vegetation is determined by comparing the model predictions with Envisat Advanced Synthetic Aperture Radar (ASAR) data gathered in 2005 over Sahelian grassland. The Agoufou site, as defined in the African Monsoon Multidisciplinary Analysis (AMMA) project, is selected as the test target and a set of ground data was collected during 2004 and 2005. Through a comprehensive data comparison, it is shown that the coherent scattering model with a generator considering botanical information is the best model to predict the backscattering data that matches Envisat measurements well (correlation = 0.92). At low incidence angles (<30°), the radar backscatter shows a strong dependence on soil moisture variations. The analysis of the different contributions leads to a study of the main scattering mechanisms. For high incidence angles, the backscattering coefficient at HH polarization shows a marked seasonal variation associated with grass presence. [ABSTRACT FROM AUTHOR]

This paper reviews the current status of the use of remote sensing for the detection, extraction and monitoring of coastlines. The review takes the US system as an example. However, the issues at hand can be applied to any other part of the world. Visual interpretation of airborne remote sensing data is still widely and popularly used for coastal delineation. However, a variety of remote sensing data and techniques are available to detect, extract and monitor the coastline. The developed techniques have reached a level of maturity such that they are applied in operational settings. [ABSTRACT FROM AUTHOR]

This paper presents a method of validating the MODIS (Moderate-Resolution Imaging Spectroradiometer) surface reflectance (MOD09) based on ground spectral measurements and high-resolution remote sensing images. Given that the spatial resolution of the MODIS image is too low to directly compare ground measurements with the MODIS pixels, registration is first made between MODIS and a high-resolution image. Afterwards, the same sizable sample area to one MODIS pixel is chosen in a high resolution image (a pixel in MODIS corresponds to many pixels in a high-resolution image), after which image classification is conducted in each sample area. Finally, reflectance is measured for each class in each area. Taking the ratio of every class area to each sample area as the weight, the weighted mean reflectance is then calculated for each sample area (a pixel for MODIS), which is treated as the measured reflectance of the corresponding pixel in the MODIS image. A contradistinctive analysis is carried out between measured reflectance and MODIS retrieval reflectance from the space in order to determine the accuracy of the MODIS land surface reflectance product and correct its error. The experiment indicates that a linear positive correlativity between the MODIS pixel retrieval reflectance from space and the measured (calculated) surface reflectance exists. From this, we can build an error correction model of the MODIS surface reflectance product by linear regression. The accuracy of the MODIS surface reflectance products after error correction is remarkably improved. Likewise, the relative error can be controlled by 10%, thereby satisfying the requirements of various remote sensing applications. [ABSTRACT FROM AUTHOR]

Estimation of noise contained within a remote sensing image is often a prerequisite to dealing with the deleterious effects of noise on the signal. Image based methods to estimate noise are attractive to researchers for a range of applications because they are in many cases automatic and do not depend on external data or laboratory measurement. In this paper, the geostatistical method for estimating image noise was applied to Compact Airborne Spectrographic Imager (CASI) imagery. Three CASI wavebands (0.46-0.49 μm (blue), 0.63-0.64 μm (red), 0.70-0.71 μm (near-infrared)) and four land covers (coniferous woodland, grassland, heathland and deciduous woodland) were selected for analysis. Five sub-images were identified per land cover resulting in 20 example cases per waveband. As in previous studies, the analysis showed that noise was related to land cover type. However, the noise estimates were not related to the mean of the signal in any waveband. Rather, the noise estimates were related to the square root of the semivariogram sill, which represents the variability in the underlying signal. These results suggest that the noise estimates produced using the geostatistical method may be inflated where the variance in the image is large. Regression of the noise estimates on the square root of the sill may lead to a stable noise estimate (i.e. the regression intercept), which is not affected by the variability in the image. This provides a refined geostatistical (GS) method that avoids the problems outlined above. [ABSTRACT FROM AUTHOR]

Object-oriented remote sensing software provides the user with flexibility in the way that remotely sensed data are classified through segmentation routines and user-specified fuzzy rules. This paper explores the classification and uncertainty issues associated with aggregating detailed 'sub-objects' to spatially coarser 'super-objects' in object-oriented classifications. We show possibility theory to be an appropriate formalism for managing the uncertainty commonly associated with moving from 'pixels to parcels' in remote sensing. A worked example with habitats demonstrates how possibility theory and its associated necessity function provide measures of certainty and uncertainty and support alternative realizations of the same remotely sensed data that are increasingly required to support different applications. [ABSTRACT FROM AUTHOR]

The multipurpose ultraviolet-visible (UV-Vis) remote sensing equipment SPATRAM (SPectrometer for Atmospheric TRAcers Monitoring) is a scanning spectrometer for measurement of electromagnetic radiation in the 250-950 nm spectral range. In this paper SPATRAM will be presented and new solutions will be discussed. The monochromator is based on the one installed in GASCOD (Gas Analyzer Spectrometer Correlating Optical Differences) developed during the 1990s. The most important improvements of SPATRAM relative to GASCOD are: (i) the wider spectral range scanned, allowing for the detection of more atmospheric compounds than with GASCOD; (ii) an increased number of inputs, resulting in the possibility of quasi-simultaneous measurements from different optical devices; (iii) the focusing optic system, which permits a simple optical alignment procedure and low cost; (iv) electronic self-thermoregulation, allowing for reliable spectral measurements unaffected by mechanical deformation caused by variation of temperature; (v) adoption of a CCD sensor, resulting in an increase of equipment sensitivity and therefore an enhancement in time resolution of the measurements; (vi) the use of an advanced CPU and a standard OS, guaranteeing full stability of the equipment; and (vii) the development of a new software tool for complete control of the whole instrument and for pre-processing of the measured data. [ABSTRACT FROM AUTHOR]

This paper investigates the potential of accounting for temporal contextual information in order to improve the accuracy of land-cover classification in summer with Synthetic Aperture Radar (SAR) data. Bi-temporal multi-sensor datasets collected in the Nonsan area of Korea were used to illustrate this approach. Multi-sensor data, including Japanese Earth Resources Satellite (JERS)-1 Optical Sensor (OPS) data acquired in April, and three different SAR sensor datasets from European Resource Satellite (ERS)-2, JERS-1, and Radarsat-1 obtained in the following July, were used for supervised classification in July. By comparing the classification result in April with a training set in July, transition probabilities between land-cover classes in the April-July period were empirically estimated and regarded as the temporal contextual information. A tau model is applied as a main integration methodology to combine multiple SAR data and the temporal contextual information. From the evaluation of the classification results in terms of accuracy statistics, using multiple SAR sensor data showed an increase of about 29% in overall accuracy compared with the case of single SAR sensor data. The incorporation of temporal contextual information into scattering information greatly contributed to a significant improvement of about 25% in overall accuracy over multiple SAR sensor integration only, and showed the best discrimination capability. [ABSTRACT FROM AUTHOR]

Quantitative mapping by means of hyperspectral remote sensing (HRS) can be hampered by reflectance anisotropy emerging in large field of view (FOV) optics, and may contain spectral radiometric distortions. This paper presents an algorithm for the rectification of reflectance anisotropy for rough terrain. A new method is offered for correction of radiometric bias caused by topography and sensing geometry. The correction of HRS data of lawn grass is demonstrated, and the method is tested on a large park area. To record elevation we used airborne laser scanning data to obtain a digital surface model (DSM). The Compact Airborne Spectral Imager (CASI) recorded reflectance of the same area. Anisotropy of reflectance was recorded by a laboratory spectro-goniometer. An analysis of the effect of correction on the normalized difference vegetation index (NDVI) shows that even moderate slopes, medium sensor FOV and high illumination conditions will result in reflectance anisotropy. Further analysis shows a clear inverse relationship between sensitivity of interpretation and spatial or spectral resolutions. We conclude with an outlook on the utilization of this method among other pre-processing tasks. [ABSTRACT FROM AUTHOR]

Currently, there are two types of measure in image classification accuracy assessment: pixel-level measures and category-level/map-level measures. These have their own limitations for representing the uncertainty at pixel and category/map levels. In addition, some of these measures derived from the error matrix are obtained by collecting reference data and then they may be affected by factors related to the sampling. This paper uses rough set theory to obtain the rough degree, rough entropy, quality of approximation and accuracy of approximation. Incorporating traditional measures, they compose one kind of three-level architecture for the classified image, which contains pixel-level measures, object/category-level measures and map-level measures. Unlike some conventional measures, these new measures can be derived directly from the supervised classification result without collecting reference data. A case study on the Landsat TM image is used to substantiate the conceptual arguments. The results demonstrate that the proposed measures are valid for measuring the accuracy of classified remotely sensed imagery and can provide additional information to conventional measures. [ABSTRACT FROM AUTHOR]

The use of land cover change maps is subject to the propagation of errors involved in classifying multi-temporal land cover maps. Understanding the link between classification processes and error propagation helps to determine appropriate classification models to mitigate the error propagation rate. In this paper, we present a simulation analysis on error propagation in land cover change detection using three classification models: a non-contextual model, a contextual model based on spatial smoothing, and a contextual model based on Markov random fields (MRF). A spatial simulation approach based on simulated annealing was developed with careful experimental designs to control two related factors including the spatial/temporal patterns of estimation errors associated with spectral probabilities. The results showed that the contextual classification model based on MRF had the smallest error propagation rate while the non-contextual classification model had the largest rate under all scenarios. The two factors had different effects on the error propagation for different classification models. For the non-contextual model, increasing temporal correlation of errors could reduce the error propagation rate while spatial autocorrelation of errors did not have a big impact on the error propagation. For the two contextual classification models, the use of contextual information significantly reduced the error propagation rate. However, the value of contextual information in mitigating error propagation was highly dependent on the spatial autocorrelation of the errors. The impact of the temporal correlation of errors was weakened in the contextual models. [ABSTRACT FROM AUTHOR]

Climate and land-atmosphere models rely on accurate land-surface parameters, such as Leaf Area Index (LAI). It is crucial that the estimation of LAI represents actual ground truth. Yet it is known that the LAI values retrieved from remote sensing images suffer from scaling effects. The values retrieved from coarse resolution images are generally smaller. Scale transformations aim to derive accurate leaf area index values at a specific scale from values at other scales. In this paper, we study the scaling effect and the scale transformation algorithm of LAI in regions with different vegetation distribution characteristics, and analyse the factors that can affect the scale transformation algorithm, so that the LAI values derived from a low resolution dataset match the average LAI values of higher resolution images. Using our hybrid reflectance model and the scale transformation algorithm for continuous vegetation, we have successfully calculated the LAI values at different scales, from reflectance images of 2.5 m and 10 m spatial resolution SPOT-5 data as well as 250 m and 500 m spatial resolution MODIS data. The scaling algorithm was validated in two geographic regions and the results agreed well with the actual values. This scale transformation algorithm will allow researchers to extend the size of their study regions and eliminate the impact of remote sensing image resolution. [ABSTRACT FROM AUTHOR]

The launch of the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard the Terra and Aqua satellites improved the ability to evaluate several surface biophysical parameters, including Leaf Area Index (LAI), which is provided as an operational MODIS product, available at 1-km spatial resolution and at 8-day intervals. However, for heterogeneous and sparse planted forests that are common to the semi-arid eastern Mediterranean region, the data at low spatial resolution may be significantly biased by the contribution of different background elements to the total surface reflectance received by the sensor and cannot therefore correctly reflect the real forest phenology. In the current paper the performance of the MODIS LAI product was examined over a dryland Mediterranean forest in southern Israel. The study found a significant discrepancy between ground-based and MODIS LAI datasets. In general, MODIS LAI values were c.51% of the ground-based LAI measurements. In addition ground based LAI peaked in the summer due to the natural growth cycle of the pine trees, while MODIS values peaked in the winter. The MODIS seasonal course could be explained by the development of annuals and crypto- and micro-phytes in the understorey and the clearing areas during the mid winter months that are included in the MODIS LAI product but not in the ground based measurements. However, for that period MODIS estimates should have exceeded ground-based estimates while in fact they were still lower. The relationship between MOD12C1 Land Cover Type 3 and MOD15A2 products is discussed. [ABSTRACT FROM AUTHOR]

Airborne laser scanning (ALS) is increasingly applied as a tool for extracting forest inventory data. In recent years most applications for the assessment of forest standing volume rely on a single tree recognition approach, which generally gives satisfactory results in coniferous forests. The aim of this paper is to apply a raster-based approach for the assessment of forest standing volume based on field measurements and a Digital Canopy Model (DCM) derived from ALS data. In addition, we explore the potential of hot spot analysis of DCM data for automatic forest gap detection, as a means to improve the accuracy of the estimation of forest standing volume by traditional estimation methods. [ABSTRACT FROM AUTHOR]

Remote sensing technologies are one of the major research topics in fusing multisource data and generating high-quality satellite images. In this paper, a new two-dimensional empirical mode decomposition (EMD) method is presented and used to fuse high-resolution panchromatic and multispectral images. First, the new two-dimensional EMD technique, which includes determining the ending criterion of the sifting procedure, the method of boundary effect resolving and seeking extrema, is presented. Then, based on the proposed two-dimensional EMD, an image fusion method for high-resolution panchromatic and multispectral images is proposed. Finally, two experiments were conducted to verify the performance of the proposed method. The experimental results demonstrate that the new method is effective and promising in terms of both visual effect and quantitative analysis. [ABSTRACT FROM AUTHOR]

Land use can be defined as the intentional use of a specific piece of land resulting in patterns of ecological responses that are visible in land cover and landscape. The responses to land use often result in a heterogeneous combination of classes of land cover. Existing methods used in the classification of satellite imagery are limited in their capacity to handle categories consisting of heterogeneous or multiple land cover classes. Accordingly, a spatial relational post-classification (SRPC) method has been developed which uses a spatial relational post-classification of land cover classes based on the incorporation of information about identified land use qualities. This paper explains how this method works, and presents the results from a case study of the surroundings of Sotåsa village located in southern Sweden. Different land cover classes were aggregated semantically into two land use quality classes. In conclusion, it is argued that it is possible to make the semantic shift from reflectance to land use qualities using the developed method on satellite data, and that this provides considerable scope for the future analysis of land use. [ABSTRACT FROM AUTHOR]

A new target detection algorithm named the spectra sort encoding (SSE) algorithm is presented in this paper. It has two steps. Firstly, relative error (cErr) between the reference and image spectra is calculated. When cErr is greater than the relative error defined by the user (rErr), the current pixel is regarded as none-target and encoded as zero. Otherwise, the second step is executed to confirm whether or not the current pixel is target. In this second step, the similarity between reference and image spectra is calculated by sorting them respectively. When the similarity is greater than the identification error (the least error limits between reference and image spectra) defined by the user, the current pixel is encoded as one; otherwise, it is encoded as zero. A detailed description is provided of the backgrounds and principles of SSE, and its accuracy is evaluated using multiple categories. Experiments indicated that when the identification error is 15%, the mean accuracy of SSE is 95%, which is 41.9% higher than that of constrained energy minimization (CEM) and 46.9% higher than that of spectrally second-order derivative (SSD). Results of target detection experiments using Enhanced Thematic Mapper Plus (ETM+) and Hyperion images revealed that it could be used in both multispectral and hyperspectral remote sensing. [ABSTRACT FROM AUTHOR]

This paper investigates road centreline extraction from high-resolution imagery. A novel road detection system is proposed based on multiscale structural features and support vector machines (SVMs). The salient aspects of the strategy are: (1) structural features are exploited because road objects are narrow and extensive, with large perimeters and small radii; (2) the object-based approach is used to extract multiscale information so as to reduce the local spectral variation caused by vehicles, shadows, road markings, etc.; (3) the hybrid spectral-structural features are analysed using the SVM classifier; and (4) multiple object levels are integrated because a multiscale approach can exploit the rich spatial information and detect multiscale road objects. Experiments were conducted on two IKONOS multispectral datasets and the results validated the proposed method. [ABSTRACT FROM AUTHOR]