High Spatial Resolution Optical Images Research Articles

Extraction of Coastal Levees Using U-Net Model with Visible and Topographic Images Observed by High-Resolution Satellite Sensors.

Coastal levees play a role in protecting coastal areas from storm surges and high waves, and they provide important input information for inundation damage simulations. However, coastal levee data with uniformity and sufficient accuracy for inundation simulations are not always well developed. Against this background, this study proposed a method to extract coastal levees by inputting high spatial resolution optical satellite image products (RGB images, digital surface models (DSMs), and slope images that can be generated from DSM images), which have high data availability at the locations and times required for simulation, into a deep learning model. The model is based on U-Net, and post-processing for noise removal was introduced to further improve its accuracy. We also proposed a method to calculate levee height using a local maximum filter by giving DSM values to the extracted levee pixels. The validation was conducted in the coastal area of Ibaraki Prefecture in Japan as a test area. The levee mask images for training were manually created by combining these data with satellite images and Google Street View, because the levee GIS data created by the Ibaraki Prefectural Government were incomplete in some parts. First, the deep learning models were compared and evaluated, and it was shown that U-Net was more accurate than Pix2Pix and BBS-Net in identifying levees. Next, three cases of input images were evaluated: (Case 1) RGB image only, (Case 2) RGB and DSM images, and (Case 3) RGB, DSM, and slope images. Case 3 was found to be the most accurate, with an average Matthews correlation coefficient of 0.674. The effectiveness of noise removal post-processing was also demonstrated. In addition, an example of the calculation of levee heights was presented and evaluated for validity. In conclusion, this method was shown to be effective in extracting coastal levees. The evaluation of generalizability and use in actual inundation simulations are future tasks.

Effect of Urbanization Through Land Coverage Classification

AbstractLand cover changes from rural or natural habitats to impervious surfaces that support various human activities are called urbanization. The evaluation of policy alternatives for future expansion and the promotion of sustainable urban development therefore place great emphasis on the mapping, analysis, and monitoring of land cover changes in metropolitan environments. By assessing changes in relevant environmental indicators at a range of scales, from local to regional, this study seeks to determine the rate of urbanization and analyze its impact on the environment in and around two key cities in Tamilnadu. Chennai and Madurai are the urban areas under investigation. The results are based on classifications of medium to high spatial resolution optical satellite images from 1995 to 2020. Various classification approaches with combinations of spectral, shape, and texture input information were combined to achieve high classification accuracy. Based on the categories, environmental indicators were created and used to calculate the impact of urbanization on the environment. The findings show that each research area experienced varying degrees of urban expansion and environmental impact. Urban areas grew tenfold between 1995 and 2015, according to a study comparing Chennai and Madurai. To evaluate the spatiotemporal dynamics of urbanization and its environmental impact in various metropolitan regions, this research shows the value of combining urban and environmental indicators with remote sensing data.

Full-angle high-reflection ultrathin composite film realizing high spatial resolution of scintillation crystal array

The performance of current nuclear medicine imaging systems is largely limited by the performance of detectors, and high spatial resolution detectors require high optical yield scintillator arrays. In this work, we simulated and designed for the first time a distributed Bragg reflector (multilayer dielectric film) that covers the entire lutetium yttrium oxyorthosilicate emission spectral band and consists of three 1/4 wavelength (λ/4) primary film systems centered at 420, 500, and 575 nm. In order to achieve ultrahigh reflectivity at the full incidence angle of the scintillator emitting surface, we propose a master optical configuration combining the dielectric film with a metal film/diffuse reflection adhesive. To explain this mechanism, we also simulated the change in reflectivity of the actual inner surface light collection. Experimental results show that a combination of a highly reflective reflector can achieve full-angle high reflectance at the total angle of incidence. We find that the dielectric film does not change the total reflection structure inside the crystal, while the light-blocking layer changes and increases the angular reflection of the dielectric film about the angle. These findings provide important insights into surface treatment as well as the design of scintillation crystal arrays, with far-reaching implications for high spatial resolution optical imaging systems.

AOHSR: attention mechanism-based optical image-guided hyperspectral image super-resolution

ABSTRACT With the rapid development of hyperspectral imaging technology, hyperspectral image (HSI) has been applied in various fields. However, due to the constraint of hyperspectral imaging devices, the spatial resolution of HSI remains too low to accurately meet the needs of target detection and recognition, which shackles the further application of HSI. In this paper, an optical image-guided HSISR algorithm based on the attention mechanism (AOHSR) was proposed in order to effectively improve the resolution of HSI. With the help of the attention mechanism, the proposed algorithm can effectively retain the spectral information of HSI and the spatial information of the optical image. Even without high spatial resolution optical imaging in the test stage, favourable reconstruction results can still be achieved. Firstly, the framework of the proposed optical image-guided HSI SR algorithm based on the attention mechanism was introduced. The algorithm consists of three modules: spectral reservation module (SRM), optical image guidance module (OIGM) and spatial-spectral recovery module (SSRM). The SRM uses an improved channel attention mechanism to extract spectral features from low-resolution (LR) HSIs to preserve spectral information. Through an improved spatial attention mechanism, the OIGM extracts spatial information from high-resolution (HR) optical images, supplementing the information extracted from LR HSI. The SSRM is used to fuse the spectral information on HSI and the spatial information on optical images to reconstruct HR HSI. Then, a joint training algorithm for reconstruction loss and optical image content loss was also proposed, which can effectively guide to learn the reconstruction of detailed information of the spatial texture while ensuring the preservation of spectral information. The results of experiments on three open datasets reveal that the HSIs reconstructed by the proposed network performed better than the existing algorithms regardless of whether optical images in the test stage exist.

Ultra-diffuse Galaxies as Extreme Star-forming Environments. I. Mapping Star Formation in H i-rich UDGs

Ultra-diffuse galaxies (UDGs) are both extreme products of galaxy evolution and extreme environments in which to test our understanding of star formation. In this work, we contrast the spatially resolved star formation activity of a sample of 22 H i-selected UDGs and 35 low-mass galaxies from the NASA Sloan Atlas (NSA) catalog within 120 Mpc. We employ a new joint spectral energy distribution fitting method to compute star formation rate and stellar mass surface density maps that leverage the high spatial resolution optical imaging data of the Hyper Suprime-Cam Subaru Strategic Program and the UV coverage of the Galaxy Evolution Explorer, along with H i radial profiles estimated from a subset of galaxies that have spatially resolved H i maps. We find that UDGs have low star formation efficiencies as a function of their atomic gas down to scales of 500 pc. We additionally find that the stellar mass-weighted sizes of our UDG sample are unremarkable when considered as a function of their H i mass—their stellar sizes are comparable to NSA dwarfs at fixed H i mass. This is a natural result in the picture where UDGs are forming stars normally, but at low efficiencies. We compare our results to predictions from contemporary models of galaxy formation, and find in particular that our observations are difficult to reproduce in models where UDGs undergo stellar expansion due to vigorous star formation feedback should bursty star formation be required down to z = 0.

A method for detecting plastic waste floating using Sentinel 2 high spatial resolution image: a case study in the coastal area of Vietnam

Ocean plastic waste pollution is now becoming a serious environmental problem, especially for a country with a long coastline and wide sea like Vietnam. The remote sensing method is considered suitable and effective in early detection and classification of ocean plastic waste due to the difference in spectral reflectance of plastic waste compared to the surrounding sea. This paper presents the results of identification and classification of plastic mesh in coastal areas of Vietnam by using Sentinel 2 MSI high spatial resolution optical images. First, water was extracted from Sentinel 2 image by thresholding method on a near-infrared band. Then, the plastic mesh was identified and classified based on Float Debris Index (FDI) index using Otsu thresholding algorithm. In the study, spectral indices such as NDVI, NDWI were also used to improve the accuracy in classifying plastic mesh. In the study, Google high spatial resolution satellite images were also used to evaluate the accuracy of plastic mesh classification. The obtained results show that, in 02 test areas, the proposed method allows detecting plastic mesh with an accuracy of over 90 %. The results obtained in the study can be used to provide input information for models of forecasting and assessing the impact of ocean plastic waste pollution on coastal environments.

Urban functional zone mapping by integrating high spatial resolution nighttime light and daytime multi-view imagery

Functional zones are the basic units of cities and the mapping work is fundamental to urban management, investigation, and research. The existing urban functional zone (UFZ) mapping methods usually utilize visual features from high spatial resolution optical images, and focus on two-dimensional image features, such as texture and landscape. However, UFZ is a comprehensive concept including geographical, social, and economic aspects. Therefore, it is reasonable to simultaneously take into account the characteristics of both human activities and image visual features for its accurate interpretation. Multi-view optical satellite images can delineate the physical characteristics of a city, i.e., 3D structures; on the other hand, high spatial resolution nighttime light images are important signals of human activities. These two data sources can be complementary in representing urban landscapes. However, to our knowledge, in the current literature, neither multi-view images nor high spatial resolution nighttime light images have been used for UFZ mapping, and it is poorly understood whether their individual or combined use can achieve satisfactory results. Therefore, in this study, a daytime and nighttime data fusion method, that is, the fusion of daytime multi-view optical images (Ziyuan3-01, 2.1 m) and high spatial resolution nighttime light images (Jilin1-07, 0.92 m), was proposed for UFZ mapping. In particular, a building enhanced nighttime light index (BENI) was proposed to improve the ability of the nighttime light images in discriminating between different functional zones. To verify the effectiveness of the proposed method, experiments were conducted in two megacities of China, i.e., Beijing and Wuhan. Our results indicated that: 1) an OA of ~80% was obtained by the spectral based method; 2) the addition of multi-view features led to ~84% OA, an increment of 4% compared with the spectral features; and 3) the inclusion of nighttime features achieved 85–90% OA, which further improved the OA of daytime features by 1–6%. It was also shown that the proposed BENI was superior to the original nighttime light brightness in identifying functional zones. In general, this study verified the effectiveness and complementarity of daytime (including multispectral and multi-view images) and nighttime images in UFZ mapping, and provided new thoughts for day and night data fusion and urban mapping.

High resolution imaging with anomalous saturated excitation

The nonlinear fluorescence emission has been widely applied for high spatial resolution optical imaging. Here, we studied the fluorescence anomalous saturating effect of the nitrogen vacancy defect in diamond. The fluorescence reduction was observed with high power laser excitation. It increased the nonlinearity of the fluorescence emission, and changed the spatial frequency distribution of the fluorescence image. We used a differential excitation protocol to extract the high spatial frequency information. By modulating the excitation laser’s power, the spatial resolution of imaging was improved approximately 1.6 times in comparison with the confocal microscopy. Due to the simplicity of the experimental setup and data processing, we expect this method can be used for improving the spatial resolution of sensing and biological labeling with the defects in solids.

Geolocation Accuracy Evaluation of GF-4 Geostationary High-Resolution Optical Images over Coastal Zones and Offshore Areas

Wei, Y.; Zhang, Z.; Mu, B.; Li, Y.; Wang, Q., and Liu, R., 2020. Geolocation accuracy evaluation of GF-4 geostationary high-resolution optical images over coastal zones and offshore areas. In: Jung, H.-S.; Lee, S.; Ryu, J.H., and Cui, T. (eds.), Advances in Geospatial Research of Coastal Environments. Journal of Coastal Research, Special Issue No. 102, pp. 326-333. Coconut Creek (Florida), ISSN 0749-0208.GF-4 is the first high-resolution geostationary satellite launched by China in 2015 for disaster prevention and mitigation. It can provide high spatial (up to 50 m) and temporal resolution optical images. Autonomous geolocation accuracy is key for the application of satellite images, particularly for coastal zones, offshore areas, and even oceans with few ground control points (GCPs). Hence, this study characterizes the autonomous geometric accuracy of GF-4 panchromatic and multispectral sensor images over coastal zones and offshore areas, which is of particular importance for the monitoring of moving targets. The evaluation of nine GF-4 images based on 630 GCPs in coastal zones and offshore areas of the Bohai Sea and Yellow Sea indicates a geolocation uncertainty of 1925 ± 976 m (39 ± 20 pixels). For GF-4 images with similar imaging geometry, the geolocation discrepancy is lower than that with different imaging geometry, with the geolocation discrepancy of 1 ± 1 and 30 ± 13 pixels, respectively. Although with the lower geolocation discrepancy, noticeable uncertainty occurs in the estimated velocity of moving targets for images with similar imaging geometry, particularly for low-speed targets, such as macro-algae. This suggests that geometric correction is needed before the speed of the moving target is extracted. The geolocation accuracy of GF-4 images through rational polynomial coefficient orthorectification without GCPs is also poor, with the geolocation uncertainty of 1350 ± 864 m, whereas the geolocation accuracy significantly improved by 8.2 times after five GCPs were added (distributed in the four corners and center of the image).

A methodology to determine the optimal quadrat size for desert vegetation surveying based on unmanned aerial vehicle (UAV) RGB photography

ABSTRACT Quadrat sampling is one of the most widely accepted methods for conducting vegetation surveys over the world for centuries. However, it is difficult to determine an optimal size to adequately represent the community compositions in quadrat samplings. Traditional labour-intensive census-based quadrat sampling is also very time consuming and insufficient to represent spatial community characters outside of the quadrat extent. In order to improve the above deficiencies, an unmanned aerial vehicle (UAV)/red-green-blue (RGB) photography based vegetation survey methodology was proposed in this study. The essential steps in the proposed method include: 1) obtaining high spatial resolution optical images from the UAV; 2) extracting species based on the orthographic image after mosaic; 3) performing statistics on a given species within the image through different quadrat sizes; 4) determining an optimal quadrat size according to the changing trend of the statistical data. In addition, a case study applied this proposed methodology was conducted in a desert area in Northwest China, where Ammopiptanthus mongolicus and Zygophyllum xanthoxylon dominated. The results show that the remote sensing UAV method could obtain RGB images and orthoimage with flexible control. The statistical data of species density decreased with the increase of quadrat size, but the values changed slightly after 20 m × 20 m, which was larger than the typical quadrat size (10 m × 10 m) used to investigate shrubs. An analysis based on the relationship among species density, plants distribution, and quadrat size further indicated the reasonability of determining the optimal size. Based on the results, it is concluded that a minimum quadrat size of 20 m × 20 m should be adopted for investigating the density and spatial pattern characteristics of A. mongolicus and Z. xanthoxylon, and the proposed UAV-based method provides an alternative for vegetation survey with high efficiency and accuracy.

Residential land extraction from high spatial resolution optical images using multifeature hierarchical method

Residential land (RL), as a typical kind of urban functional zone, plays an important role in urban planning and land census. Recent years have witnessed frequent changes in RL via the process of urbanization. The extraction of RL from high spatial resolution optical images can reflect the status quo of land use/land cover to a certain extent, which is of great significance to land census and urban planning. We adopt a scene classification strategy to extract RL and mainly focus on the extraction of four common types of RL in China: old-style village, low-density high-rise, medium-density low-rise, and low-density low-rise. We design a multifeature hierarchical (MFH) algorithm for RL extraction. First, RL is extracted based on the gray level concurrence matrix and a fuzzy classification algorithm. Then an improved bag-of-visual-words algorithm is introduced to further realize the extraction of RL. The effectiveness of our proposed method is analyzed with a sample dataset and large images. We also analyze the separability among different kinds of RL. We compare the experimental results with those of three other algorithms, and the results demonstrate that the MFH algorithm performs better in terms of the accuracy and efficiency of the RL extraction. The results can provide services for land surveying and urban planning, and the technological processes and experimental design in the algorithm can provide a reference for the research in related fields.

Water Body Extraction from Very High Spatial Resolution Remote Sensing Data Based on Fully Convolutional Networks

This paper studies the use of the Fully Convolutional Networks (FCN) model in the extraction of water bodies from Very High spatial Resolution (VHR) optical images in the case of limited training samples. Two different seasonal GaoFen-2 images with a spatial resolution of 0.8 m in the south of the Beijing metropolitan area were used to extensively validate the FCN model. Four key factors including input features, training data, transfer learning, and data augmentation related to the performance of the FCN model were empirically analyzed by using 36 combinations of various parameter settings. Our findings indicate that the FCN-based method can work as a robust and cost-effective tool in the extraction of water bodies from VHR images. The FCN-based method trained on a small amount of labeled L1A data can also significantly outperform the Normalized Difference Water Index (NDWI) based method, the Support Vector Machine (SVM) based method, and the Sparsity Model (SM) based method, even when radiometric normalization and spatial contexts are introduced to preprocess the input data for the latter three methods. The advantages of the FCN-based method are mainly due to its capability to exploit spatial contexts in the image, especially in urban areas with mixed water and shadows. Though the settings of four key factors significantly affect the performance of the FCN based method, choosing a qualified setting for the FCN model is not difficult. Our lessons learned from the successful use of the FCN model for the extraction of water from VHR images can be extended to extract other land covers.

Study on structure stability based on image quality guarantee for GEO high resolution optical imaging satellite

By contrast to one on low earth orbit(LEO),high spatial resolution optical imaging earth observation satellite on geostationary orbit has some remarkable advantages with much higher time resolution,more wider imaging field、much longer target tracking and monitoring time,and now is becoming one of important development foucs. Corresspondently GEO optical imaging satellite has much higher control accuracy requirement with stability than LEO, greatly promote development difficulties.The paper makes dicussion for key structure influence factors on attitude & structure stability for image quality guarantee under complex space circumstance and conducts stimulation to account for the their effect,finally present some solution options.

Mapping water-logging damage on winter wheat at parcel level using high spatial resolution satellite data

Damage to agricultural crops due to water-logging is becoming more frequent in this era of global climate change. In this study, we investigated the effect of water-logging on winter wheat growth (LAI and aboveground biomass) and yield at a parcel scale using high spatial resolution satellite data. For this purpose, a dynamic mapping of winter wheat LAI and biomass during growth under water-logging conditions was implemented. In addition, yield mapping and yield loss estimation were also carried out to arrive at the potential impacts of water-logging on both winter wheat growth and grain yield. Two varieties of winter wheat were investigated (Yang Mai 14 and Yang Mai 18) at tillering, jointing to booting and flowering. Field conditions were representative of water-logging treatment (contrast check (CK), waterlogging (WL) and flooding (FL)). A total of eight high spatial resolution optical satellite images, including WorldView-2, WorldView-3, Pleiades-1A, SPOT-6, GeoEye-1 and SPOT-7 were obtained at time points concurrent with eight temporal field observations. Seven widely used vegetation indices (VIs) were utilized as independent variables for LAI, biomass and yield estimation. Optimal estimation models were selected according to coefficient of determination (R2), correlation coefficient (R) and root mean square error (RMSE) for subsequent mapping of LAI, biomass and yield. Results show that the power model, having EVI as the independent variable, had the highest accuracy for biomass estimation (R2 of 0.78 and RMSE of 0.076 kg/m2) while the exponential model with NDVI is the optimal model for LAI estimation (with 0.74 in R2 and 0.64 in RMSE). GNDVI is the optimal VI for yield mapping, with 0.563 in Rcv (cross validated R) and 843.76 kg/hm2 in RMSEcv (cross validated RMSE). As for the assessment of water-logging damage on winter wheat growth, FL showed a more serious impact than that of WL, based on results of the dynamic mapping of biomass and LAI at the same development stages. Water-logging from jointing to booting showed a more serious damage impact with respect to biomass and yield than that at the tillering and flowering stages. Our results have demonstrated the potential of multi-source and high spatial resolution remotely-sensed optical data for quantifying the impact of water-logging on winter wheat growth and grain yield at parcel scales, a research direction that has rarely been reported in agricultural remote sensing literature. This study thus provides a fundamental basis and the possibility for crop yield loss evaluation, water-logging monitoring, and for the design of mitigation strategies that would benefit even small-holder and subsistence farmers.

Monitoring mangrove forests after aquaculture abandonment using time series of very high spatial resolution satellite images: A case study from the Perancak estuary, Bali, Indonesia

Revegetation of abandoned aquaculture regions should be a priority for any integrated coastal zone management (ICZM). This paper examines the potential of a matchless time series of 20 very high spatial resolution (VHSR) optical satellite images acquired for mapping trends in the evolution of mangrove forests from 2001 to 2015 in an estuary fragmented into aquaculture ponds. Evolution of mangrove extent was quantified through robust multitemporal analysis based on supervised image classification. Results indicated that mangroves are expanding inside and outside ponds and over pond dykes. However, the yearly expansion rate of vegetation cover greatly varied between replanted ponds. Ground truthing showed that only Rhizophora species had been planted, whereas natural mangroves consist of Avicennia and Sonneratia species. In addition, the dense Rhizophora plantations present very low regeneration capabilities compared with natural mangroves. Time series of VHSR images provide comprehensive and intuitive level of information for the support of ICZM.

A Fuzzy-GA Based Decision Making System for Detecting Damaged Buildings from High-Spatial Resolution Optical Images

In this research, a semi-automated building damage detection system is addressed under the umbrella of high-spatial resolution remotely sensed images. The aim of this study was to develop a semi-automated fuzzy decision making system using Genetic Algorithm (GA). Our proposed system contains four main stages. In the first stage, post-event optical images were pre-processed. In the second stage, textural features were extracted from the pre-processed post-event optical images using Haralick texture extraction method. Afterwards, in the third stage, a semi-automated Fuzzy-GA (Fuzzy Genetic Algorithm) decision making system was used to identify damaged buildings from the extracted texture features. In the fourth stage, a comprehensive sensitivity analysis was performed to achieve parameters of GA leading to more accurate results. Finally, the accuracy of results was assessed using check and test samples. The proposed system was tested over the 2010 Haiti earthquake (Area 1 and Area 2) and the 2003 Bam earthquake (Area 3). The proposed system resulted in overall accuracies of 76.88 ± 1.22%, 65.43 ± 0.29%, and 90.96 ± 0.15% over Area 1, Area 2, and Area 3, respectively. On the one hand, based on the concept of the proposed Fuzzy-GA decision making system, the automation level of this system is higher than other existing systems. On the other hand, based on the accuracy of our proposed system and four advanced machine learning techniques, i.e., bagging, boosting, random forests, and support vector machine, in the detection of damaged buildings, it seems that our proposed system is robust and efficient.

Early Detection of Summer Crops Using High Spatial Resolution Optical Image Time Series

In the context of climate change, agricultural managers have the imperative to combine sufficient productivity with durability of the resources. Many studies have shown the interest of recent satellite missions as suitable tools for agricultural surveys. Nevertheless, they are not predictive methods. A system able to detect summer crops as early as possible is important in order to obtain valuable information for a better water management strategy. The detection of summer crops before the beginning of the irrigation period is therefore our objective. The study area is located near Toulouse (southwestern France), and is a region of mixed farming with a wide variety of irrigated and non-irrigated crops. Using the reference data for the years concerned, a set of fixed thresholds are applied to a vegetation index (the Normalized Difference Vegetation Index, NDVI) for each agricultural season of multi-spectral satellite optical imagery acquired at decametric spatial resolutions from 2006 to 2013. The performance (i.e., accuracy) is contrasted according to the agricultural practices, the development states of the different crops and the number of acquisition dates (one to three in the results presented here). The detection of summer crops reaches 64% to 88% with a single date, 80% to 88% with two dates and 90% to 99% with three dates. The robustness of this method is tested for several years (showing an impact of meteorological conditions on the actual choice of images), several sensors and several resolutions.

Improved Early Crop Type Identification By Joint Use of High Temporal Resolution SAR And Optical Image Time Series

High temporal and spatial resolution optical image time series have been proven efficient for crop type mapping at the end of the agricultural season. However, due to cloud cover and image availability, crop identification earlier in the season is difficult. The recent availability of high temporal and spatial resolution SAR image time series, opens the possibility of improving early crop type mapping. This paper studies the impact of such SAR image time series when used in complement of optical imagery. The pertinent SAR image features, the optimal working resolution, the effect of speckle filtering and the use of temporal gap-filling of the optical image time series are assessed. SAR image time series as those provided by the Sentinel-1 satellites allow significant improvements in terms of land cover classification, both in terms of accuracy at the end of the season and for early crop identification. Haralik textures (Entropy, Inertia), the polarization ratio and the local mean together with the VV imagery were found to be the most pertinent features. Working at at 10 m resolution and using speckle filtering yield better results than other configurations. Finally it was shown that the use of SAR imagery allows to use optical data without gap-filling yielding results which are equivalent to the use of gap-filling in the case of perfect cloud screening, and better results in the case of cloud screening errors.

High gain and high spatial resolution optical parametric amplification imaging under continuous-wave laser irradiation

This paper presents a novel design for non-collinear optical parametric amplification imaging where an intense ultrashort light pulse chain is used to pump and thereby amplify a continuous-wave (CW) illuminating laser. The setup needs no temporal synchronization between the pump and the CW signal, so it is simple and compact. This design allows us to realize optical imaging for different spectral demands by flexibly changing the CW (tunable) illuminating laser sources, meanwhile with high gain and broad spatial bandwidth, by using an ultrashort pulse laser system as pump. Experimentally, a wavelength-shifted image is achieved with optical gain up to 106 and a two-dimensional space-bandwidth product of more than 20 000 based on this design.

Do we need another neural correlate of contour integration?

Gilad and colleagues use an elegant combination of voltage-sensitive dyes and high temporal and spatial resolution optical imaging to visualize a differential response to collinear contour elements in monkey V1. This result adds to the literature on the neural correlates of contour integration, but does not yet tackle (or seek to tackle) the question as to whether contour integration is mediated by lateral connections within an area (e.g., V1), through pooling of feedfoward connections, or feedback mechanisms. Moreover, while Gilad et al. find that their differential response is correlated with the behavioral performance of each monkey, there are reasons to suspect that the correlation they observe is a consequence of processing in higher regions, and that the differential V1 response may not play a critical role in integrating contour elements, or in generating the monkey's response. Moreover, this differential V1 response was not observed in a monkey who was not trained on the task, a result that can only be reconciled, if one assumes that the monkey could not see the contour prior to training. If valid, this could raise doubts as to whether the study of contour integration really provides insights into the processes by which normal visual perception is achieved.