Using vector agents to implement an unsupervised image classification algorithm

Unsupervised image classification methods conventionally use the spatial information of pixels to reduce the effect of speckled noise in the classified map. To extract this spatial information, they employ a predefined geometry, i.e., a fixed-size window or segmentation map. However, this coding of geometry lacks the necessary complexity to accurately reflect the spatial connectivity within objects in a scene. Additionally, there is no unique mathematical formula to determine the shape and scale applied to the geometry, being parameters that are usually estimated by expert users. In this paper, a novel geometry-led approach using Vector Agents (VAs) is proposed to address the above drawbacks in unsupervised classification algorithms. Our proposed method has two primary steps: (1) creating reliable training samples and (2) constructing the VA model. In the first step, the method applies the statistical information of a classified image by k-means to select a set of reliable training samples. Then, in the second step, the VAs are trained and constructed to classify the image. The model is tested for classification on three high spatial resolution images. The results show the enhanced capability of the VA model to reduce noise in images that have complex features, e.g., streets, buildings. © 2021 by the authors. Licensee MDPI, Basel, Switzerland

Corrigendum: Inexpensive Aerial Photogrammetry for Studies of Whales and Large Marine Animals

We describe a simple system enabling accurate measurement of swimming marine mammals and other large vertebrates from low-altitude single-frame photogrammetry via inexpensive modifications to a “prosumer” unmanned aerial vehicle (UAV) equipped with gimballed micro4/3 camera and 25 mm lens. Image scale is established via an independently powered LIDAR/GPS data-logging system recording altitude and GPS location at 1 Hz. Photogrammetric calibration of the camera and lens allowed distortion parameters to be rigorously accounted for during image analysis, via a custom-programmed Graphical User Interface (GUI) running in MATLAB. The datalogger, camera calibration methods and measurement software are adaptable to a wide range of UAV platforms. Mean LIDAR accuracy, measured from 10 bridges 9–39 m above water, was 99.9%. We conducted 136 flights in New Zealand's subantarctic Auckland Islands to measure southern right whales. Mean lengths of 10 individual whales, each photographed between 7 and 15 times, had CVs (SD/mean) ranging from 0.5 to 1.8% (mean = 1.2%). Repeated measurements of a floating reference target showed a mean error of c.1%. Our system is relatively inexpensive, easily put together, produces accurate, repeatable measurements from single vertical images, and hence is applicable to a wide range of ecological questions in marine and terrestrial habitats

Distributed vs. semi-distributed simulations of snowpack dynamics in alpine areas: case study in the upper Arve catchment, French Alps, 1989–2015

We evaluated distributed and semi-distributed modeling approaches to simulating the spatial and temporal evolution of snow and ice over an extended mountain catchment, using the Crocus snowpack model. The distributed approach simulated the snowpack dynamics on a 250-m grid, enabling inclusion of terrain shadowing effects. The semi-distributed approach simulated the snowpack dynamics for discrete topographic classes characterized by elevation range, aspect, and slope. This provided a categorical simulation that was subsequently spatially re-projected over the 250-m grid used for the distributed simulations. The study area (the upper Arve catchment, western Alps, France) is characterized by complex topography, including steep slopes, an extensive glaciated area, and snow cover throughout the year. Simulations were carried out for the period 1989–2015 using the SAFRAN meteorological forcing system. The simulations were compared using four observation datasets including point snow depth measurements, seasonal and annual glacier surface mass balance, snow covered area evolution based on optical satellite sensors, and the annual equilibrium-line altitude of glacier zones, derived from satellite images. The results showed that in both approaches the Crocus snowpack model effectively reproduced the snowpack distribution over the study period. Slightly better results were obtained using the distributed approach because it included the effects of shadows and terrain characteristics

Monitoring Snow Cover and Modelling Catchment Discharge With Remote Sensing in the Upper Waitaki Basin, New Zealand

Because New Zealand relies heavily on water for electricity generation, it requires strong and reliable information about its water supplies as well as better knowledge about the processes that affect them. Situated in the Southern Alps, the Waitaki basin is the most important hydro catchment in New Zealand. Three alpine sub-catchments, namely Ohau, Pukaki, and Tekapo, provide most of the discharge to the Waitaki River. In this alpine region a large part of the water resource is temporarily stored as seasonal snow cover. To utilize better the value of water in hydro lakes, improving knowledge of the timing and supply of water from seasonal snow is a priority. It has been long established that satellite remote sensing is a powerful tool to monitor snow cover in remote and inaccessible areas. In New Zealand, this technology has received only scant consideration. In addressing the remote sensing of the seasonal snow cover in the alpine catchments of the Waitaki basin, this thesis aims at filling a considerable void. This is achieved through the implementation of routine monitoring of the snow cover dynamics with the MODerate Imaging Spectro-radiometer (MODIS). Towards this goal, several advanced remote sensing techniques that are novel to MODIS are integrated in a single and operational algorithm. This research demonstrates the desirable performance of an image fusion algorithm. The algorithm enables the mapping of snow with MODIS at 250m spatial resolution instead of the 500m spatial resolution imagery currently available. Furthermore, MODIS images are standardized by means of a physically-based atmospheric and topographic correction (ATOPCOR) approach. Finally, the radiometric normalization of the time series permits the design of a robust spectral unmixing technique. This allows further enhancement of the spatial details of the snow maps through the determination of sub-pixel snow fractions at 250m spatial resolution. Together, the combination of these techniques forms a processing chain, well suited to the mountainous environment, to map snow with the highest possible amount of spatial detail. A careful assessment of the quality of the maps of snow fractions is conducted by means of comparison with high resolution reference snow maps obtained from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER). The processing of seven years of MODIS observations covering the 2000--2006 hydrological years permitted the creation of a new dataset that depicts the spatial distribution of snow. Based on this dataset, this thesis demonstrates that the current modelling approach of the snowpack by the model SnowSim tends to propagate errors that increase to an important level. Every year by the end of the ablation season SnowSim models nearly a quarter of the total water storage in locations that are free of snow according to observations from MODIS. Finally, the hydrological modelling approach enabled by the Snowmelt Runoff Model (SRM) is revisited. Daily meteorological data (i.e., temperature and precipitation) and the frequent observations of the snowpack provided by MODIS enable the daily discharge to be simulated. Unprecedented performance in the simulation of daily inflows are obtained for the three largest water reservoirs in New Zealand. This sheds new light on the relative contribution of seasonal snowmelt and ice melt to the discharge. In revealing the large daily variability of the snowmelt, new estimates of its contribution to the lake inflows are obtained. Over the study period, snowmelt accounted for 37%, 40%, and 31% of the discharge in the Lakes Ohau, Pukaki, and Tekapo catchments, respectively. Finally, this research documents the severe drought of 2005. It strongly suggests that inflows were largely mitigated by ice melt from glaciers in the Pukaki basin. A contribution of glacier melt much larger than usual is believed to have sustained the discharge to within 17% of the mean annual flow, although precipitation was reduced by 34%. This mitigating factor was less marked in Tekapo and not observed in the Ohau basin, in accordance with the relative proportion of glacierized areas in the catchments. This potentially provides a striking example of the contribution of long term storage to inflows during dry periods

Monitoring Snow Cover and Modelling Catchment Discharge With Remote Sensing in the Upper Waitaki Basin, New Zealand

Because New Zealand relies heavily on water for electricity generation, it requires strong and reliable information about its water supplies as well as better knowledge about the processes that affect them. Situated in the Southern Alps, the Waitaki basin is the most important hydro catchment in New Zealand. Three alpine sub-catchments, namely Ohau, Pukaki, and Tekapo, provide most of the discharge to the Waitaki River. In this alpine region a large part of the water resource is temporarily stored as seasonal snow cover. To utilize better the value of water in hydro lakes, improving knowledge of the timing and supply of water from seasonal snow is a priority. It has been long established that satellite remote sensing is a powerful tool to monitor snow cover in remote and inaccessible areas. In New Zealand, this technology has received only scant consideration. In addressing the remote sensing of the seasonal snow cover in the alpine catchments of the Waitaki basin, this thesis aims at filling a considerable void. This is achieved through the implementation of routine monitoring of the snow cover dynamics with the MODerate Imaging Spectro-radiometer (MODIS). Towards this goal, several advanced remote sensing techniques that are novel to MODIS are integrated in a single and operational algorithm. This research demonstrates the desirable performance of an image fusion algorithm. The algorithm enables the mapping of snow with MODIS at 250m spatial resolution instead of the 500m spatial resolution imagery currently available. Furthermore, MODIS images are standardized by means of a physically-based atmospheric and topographic correction (ATOPCOR) approach. Finally, the radiometric normalization of the time series permits the design of a robust spectral unmixing technique. This allows further enhancement of the spatial details of the snow maps through the determination of sub-pixel snow fractions at 250m spatial resolution. Together, the combination of these techniques forms a processing chain, well suited to the mountainous environment, to map snow with the highest possible amount of spatial detail. A careful assessment of the quality of the maps of snow fractions is conducted by means of comparison with high resolution reference snow maps obtained from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER). The processing of seven years of MODIS observations covering the 2000--2006 hydrological years permitted the creation of a new dataset that depicts the spatial distribution of snow. Based on this dataset, this thesis demonstrates that the current modelling approach of the snowpack by the model SnowSim tends to propagate errors that increase to an important level. Every year by the end of the ablation season SnowSim models nearly a quarter of the total water storage in locations that are free of snow according to observations from MODIS. Finally, the hydrological modelling approach enabled by the Snowmelt Runoff Model (SRM) is revisited. Daily meteorological data (i.e., temperature and precipitation) and the frequent observations of the snowpack provided by MODIS enable the daily discharge to be simulated. Unprecedented performance in the simulation of daily inflows are obtained for the three largest water reservoirs in New Zealand. This sheds new light on the relative contribution of seasonal snowmelt and ice melt to the discharge. In revealing the large daily variability of the snowmelt, new estimates of its contribution to the lake inflows are obtained. Over the study period, snowmelt accounted for 37%, 40%, and 31% of the discharge in the Lakes Ohau, Pukaki, and Tekapo catchments, respectively. Finally, this research documents the severe drought of 2005. It strongly suggests that inflows were largely mitigated by ice melt from glaciers in the Pukaki basin. A contribution of glacier melt much larger than usual is believed to have sustained the discharge to within 17% of the mean annual flow, although precipitation was reduced by 34%. This mitigating factor was less marked in Tekapo and not observed in the Ohau basin, in accordance with the relative proportion of glacierized areas in the catchments. This potentially provides a striking example of the contribution of long term storage to inflows during dry periods

Atmospheric circulation drivers of lake inflow for the Waitaki River, New Zealand

Hydro-electricity is a critical resource in New Zealand, and as such improved understanding of the drivers of water resource availability is a key research goal. Large-scale atmospheric circulation is the principal driver of surface climate and water resource variability over New Zealand. Focusing on the Waitaki River (located in the South Island; one of the most important rivers for hydro-electricity in New Zealand), a comprehensive analysis is presented of the large-scale atmospheric circulation drivers of monthly inflow to the three main headwater lakes, Ohau, Pukaki and Tekapo. Analyses are undertaken using composite, correlation, partial least-squares (PLS) regression and cross-wavelet analyses. Environment-to-climate composite analysis indicates that variation in lake inflow is driven primarily by the strength of the NE–SW pressure gradient over the three lakes (i.e. parallel to the axis of the Southern Alps, from which the lakes are fed). Relatively strong winds from a north-westerly direction are associated with high lake inflow; weaker winds from a more south-westerly direction occur during times of low inflow. Climate-to-environment composites of lake inflow, together with correlation, PLS and wavelet analysis, indicate that inflow is described well by the MZ1 and MZ2 New Zealand-based circulation indices, but not larger-scale modes of atmospheric circulation. The MZ1 and MZ2 indices have rarely been considered previously as explanatory variables for water resources in the South Island of New Zealand, but here it is suggested that these indices represent a promising new direction for future studies, particularly relating to season-ahead prediction of water resource availability

Geographical Vector Agent Modelling for Image Classification: Initial Development

Peer Reviewe

Assessment of the performance of image fusion for the mapping of snow

The assessment of the performance of multi-resolution image fusion, or image sharpening methods, is difficult. In the context of binary classification of snow targets in mountainous terrain, fusion methods were applied to help achieve more accurate mapping. To quantify objectively the gain of information that can be attributed to an increase in spatial resolution, we investigate the Mean Euclidean Distance (MED) between the snowline obtained from the classification, and a reference snowline (or a ground truth line), as a relevant indicator to measure both the discrepancy between datasets at different spatial resolutions, and the accuracy of the mapping process. First, a theoretical approach based on aggregating detailed reference images from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) showed that the MED has a linear relationship with the pixel size that makes it suitable to assess images of different resolutions. Secondly, we tested the MED to snow maps obtained ‘with’ or ‘without’ applying a fusion method to the MODerate Resolution Imaging Spectroradiometer (MODIS). We demonstrated that the MED identified a significant value added, in terms of mapping accuracy, which can be attributed to the fusion process. When the fusion method was applied to four different images, the MED overall decreased by more than 30%. Finally, such a ‘feature based’ quality indicator can also be interpreted as a statistical assessment of the planimetric accuracy of natural pattern outlines.PublishedPeer ReviewedAiazzi, B., Alparone, L., Argenti, F. & Baronti, S. (1999). “Wavelet and pyramid techniques for multisensor data fusion: a performance comparison varying with scale ratios” In S. B. Serpico (ed.), Proceedings of the SPIE Image Signal Process. For Remote Sensing V. Vol. 3871 SPIE SPIE. pp. 251–262. Alparone, L., Baronti, S., Garzelli, A. & Nencini, F. (2004). “A global quality measurement of pan-sharpened multispectral imagery” IEEE Geoscience and Remote Sensing Letters. 1(4): 313–317. Alparone, L., Wald, L., Chanussot, J., Thomas, Gamba, P. & Bruce, L. M. (2007). “Comparison of pansharpening algorithms: outcome of the 2006 GRS-S data fusion contest” IEEE Transactions on Geoscience and Remote Sensing. 45(10): 3012–3021. Amolins, K., Zhang, Y. & Dare, P. (2007). “Wavelet based image fusion techniques – An introduction, review and comparison” Journal of Photogrammetry and Remote Sensing. 62(4): 249–263. Bendjoudi, H. (2002). “The gravelius compactness coefficient: critical analysis of a shape index for drainage basins” Hydrological Sciences Journal. 47(6): 921–930. Chen, H. & Varshney, P. K. (2007). “A human perception inspired quality metric for image fusion based on regional information” Information Fusion. 8(2): 193–207. Crane, R. G. & Anderson, M. R. (1984). “Satellite discrimination of snow / cloud surfaces” International Journal of Remote Sensing. 5: 213–223. Du, Y., Vachon, P. W. & van der Sanden, J. J. (2003). “Satellite image fusion with multiscale wavelet analysis for marine applications: preserving spatial information and minimizing artifacts (PSIMA)” Canadian Journal of Remote Sensing. 29(1): 14–23. Gao, X., Wang, T. & Li, J. (2005). “A content-based image quality metric” Lecture Notes in Computer Science. 3642: 231–240. to get. Garguet-Duport, B., Girel, J., Chassery, J.-M. & Pautou, G. (1996). “The use of multi-resolution analysis and wavelet transform for merging SPOT panchromatic and multispectral imagery data” Photogrammetric Engineering and Remote Sensing. 62(9): 1057–1066. González-Audicana, M., Otazu, X., Fors, O. & Alvarez-Mozos, J. (2006). “A low computational-cost method to fuse IKONOS images using the spectral response function of its sensors” IEEE Transactions on Geoscience and Remote Sensing. 44(6): 1683–1691. González-Audicana, M., Saleta, J. L., Catalán, R. G. & Garc´ıa, R. (2004). “Fusion of multispectral and panchromatic images using improved IHS and PCA mergers based on wavelet decomposition” IEEE Transactions on Geoscience and Remote Sensing. 42(6): 1291–1299. Goodchild, M. (1980). “Fractals and the accuracy of geographical measures” Mathematical Geology. 12(2): 85–98. Goodchild, M. F. & Mark, D. M. (1987). “The fractal nature of geographic phenomena” Annals of Association of American Geographers. 77(2): 265–278. Jin, X. Y. & Davis, C. H. (2005). “Automated building extraction from high-resolution satellite imagery in urban areas using structural, contextual, and spectral information” EURASIP Journal of Applied Signal Processing. 2005(14): 2196–2206. Keshava, N. (2003). “A survey of spectral unmixing algorithms” Lincoln Laboratory Journal. 14(1): 55–78. Lam, N. S.-N. & Quattrochi, D. A. (1992). “On the issues of scale, resolution, and fractal analysis in the mapping sciences” Professional Geographer. 44(1): 88–98. Laporterie-Déjean, F., de Boissezon, H., Flouzat, G. & Lefèvre-Fonollosa, M.-J. (2005). “Thematic and statistical evaluations of five panchromatic/multispectral fusion methods on simulated PLEIADES-HR images” Information Fusion. 6(3): 193–212. Lasaponara, R. & Masini, N. (2005). “QuickBird-based analysis for the spatial characterization of archaeological sites: case study of the Monte Serico medieval village” Geophysical Research Letter. 32(12): L12313. Li, X., He, H. S., Bu, R., Wen, Q., Chang, Y., Hu, Y. & Li, Y. (2005). “The adequacy of different landscape metrics for various landscape patterns” Pattern Recognition. 38(12): 2626–2638. Malpica, J. A. (2007). “Hue adjustment to IHS pan-sharpened IKONOS imagery for vegetation enhancement” IEEE Geoscience and Remote Sensing Letters. 4(1): 27–31. Nichol, J. & Wong, M. S. (2005). “Satellite remote sensing for detailed landslide inventories using change detection and image fusion” International Journal of Remote Sensing. 26(9): 1913–1926. Pasqualini, V., Pergent-Martini, C., Pergent, G., Agreil, M., Skoufas, G., Sourbes, L. & Tsirika, A. (2005). “Use of SPOT 5 for mapping seagrasses: An application to Posidonia oceanica” Remote Sensing of Environment. 94(1): 39–45. Petrovic, V. (2007). “Subjective tests for image fusion evaluation and objective metric validation” Information Fusion. 8(2): 208–216. Pohl, C. & Genderen, J. L. V. (1998). “Multisensor image fusion in remote sensing: concepts, methods and applications” International Journal of Remote Sensing. 19(5): 823–854. Ranchin, T., Aiazzi, B., Alparone, L., Baronti, S. & Wald, L. (2003). “Image fusion–the ARSIS concept and some successful implementation schemes” Journal of Photogrammetry and Remote Sensing. 58: 4–18. Ranchin, T. & Wald, L. (2000). “Fusion of high spatial and spectral resolution images : the ARSIS concept and its implementation” Photogrammetric Engineering and Remote Sensing. 66(1): 49–61. Richter, R. (1998). “Correction of satellite imagery over mountainous terrain” Applied Optics. 37(18): 4004–4015. Shi, W., Zhu, C., Tian, Y. & Nichol, J. (2005). “Wavelet-based image fusion and quality assessment” International Journal of Applied Earth Observation and Geoinformation. 6: 241–251. Sirguey, P., Mathieu, R., Arnaud, Y., Khan, M. M. & Chanussot, J. (2008). “Improving MODIS spatial resolution for snow mapping using wavelet fusion and ARSIS concept” IEEE Geoscience and Remote Sensing Letters. 5(1): doi:10.1109/LGRS.2007.908884. In Press. Toet, A. & Franken, E. M. (2003). “Perceptual evaluation of different image fusion schemes” Displays. 24(1): 25–37. Tu, T.-M., Huang, P. S., Hung, C.-L. & Chang, C.-P. (2004). “A fast Intensity–Hue–Saturation fusion technique with spectral adjustment for IKONOS imagery” IEEE Geoscience and Remote Sensing Letters. 1(4): 309–312. Wald, L. (2000). “Quality of high resolution synthesized images: is there a simple criterion?” Proceedings of the International Conference on Fusion of Earth Data. SEE Gr éca Sophia Antipolis, France pp. 99–105. Wald, L., Ranchin, T. & Mangolini, M. (1997). “Fusion of satellite images of different spatial resolution: assessing the quality of resulting images” Photogrammetric Engineering and Remote Sensing. 63(6): 691–699. Wang, Z. & Bovik, A. C. (2002). “A universal image quality index” IEEE Signal Processing Letters. 9(3): 81–84. Woodcock, C. E. & Strahler, A. H. (1987). “The factor of scale in remote sensing” Remote Sensing of Environment. 21(3): 311–332. Xydeas, C. S. & Petrovic, V. (2000). “Objective image fusion performance measure” Electronics Letters. 36(4): 308–309. Zhai, G., Zhang, W., Yang, X. & Xu, Y. (2005). “Image quality assessment metrics based on multi-scale edge presentation” Proceedings of the IEEE Workshop on Signal Processing Systems Design and Implementation. IEEE IEEE. pp. 331–336. Zhan, Q., Molenaar, M., Tempfli, K. & Shi, W. (2005). “Quality assessment for geo-spatial objects derived from remotely sensed data” International Journal of Remote Sensing. 26(14): 2953–2974. Zhang, W. J. & Kang, J. Y. (2006). “QuickBird panchromatic and multi-spectral image fusion using wavelet packet transform” Intelligent Control and Automation. 344: 976–981. Zhang, Y. (2004). “Understanding image fusion” Photogrammetric Engineering and Remote Sensing. 70(6): 657–661

Geographical Vector Agent Modelling for Image Classification: Initial Development

Peer Reviewe

Assessment of the performance of image fusion for the mapping of snow

The assessment of the performance of multi-resolution image fusion, or image sharpening methods, is difficult. In the context of binary classification of snow targets in mountainous terrain, fusion methods were applied to help achieve more accurate mapping. To quantify objectively the gain of information that can be attributed to an increase in spatial resolution, we investigate the Mean Euclidean Distance (MED) between the snowline obtained from the classification, and a reference snowline (or a ground truth line), as a relevant indicator to measure both the discrepancy between datasets at different spatial resolutions, and the accuracy of the mapping process. First, a theoretical approach based on aggregating detailed reference images from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) showed that the MED has a linear relationship with the pixel size that makes it suitable to assess images of different resolutions. Secondly, we tested the MED to snow maps obtained ‘with’ or ‘without’ applying a fusion method to the MODerate Resolution Imaging Spectroradiometer (MODIS). We demonstrated that the MED identified a significant value added, in terms of mapping accuracy, which can be attributed to the fusion process. When the fusion method was applied to four different images, the MED overall decreased by more than 30%. Finally, such a ‘feature based’ quality indicator can also be interpreted as a statistical assessment of the planimetric accuracy of natural pattern outlines.PublishedPeer ReviewedAiazzi, B., Alparone, L., Argenti, F. & Baronti, S. (1999). “Wavelet and pyramid techniques for multisensor data fusion: a performance comparison varying with scale ratios” In S. B. Serpico (ed.), Proceedings of the SPIE Image Signal Process. For Remote Sensing V. Vol. 3871 SPIE SPIE. pp. 251–262. Alparone, L., Baronti, S., Garzelli, A. & Nencini, F. (2004). “A global quality measurement of pan-sharpened multispectral imagery” IEEE Geoscience and Remote Sensing Letters. 1(4): 313–317. Alparone, L., Wald, L., Chanussot, J., Thomas, Gamba, P. & Bruce, L. M. (2007). “Comparison of pansharpening algorithms: outcome of the 2006 GRS-S data fusion contest” IEEE Transactions on Geoscience and Remote Sensing. 45(10): 3012–3021. Amolins, K., Zhang, Y. & Dare, P. (2007). “Wavelet based image fusion techniques – An introduction, review and comparison” Journal of Photogrammetry and Remote Sensing. 62(4): 249–263. Bendjoudi, H. (2002). “The gravelius compactness coefficient: critical analysis of a shape index for drainage basins” Hydrological Sciences Journal. 47(6): 921–930. Chen, H. & Varshney, P. K. (2007). “A human perception inspired quality metric for image fusion based on regional information” Information Fusion. 8(2): 193–207. Crane, R. G. & Anderson, M. R. (1984). “Satellite discrimination of snow / cloud surfaces” International Journal of Remote Sensing. 5: 213–223. Du, Y., Vachon, P. W. & van der Sanden, J. J. (2003). “Satellite image fusion with multiscale wavelet analysis for marine applications: preserving spatial information and minimizing artifacts (PSIMA)” Canadian Journal of Remote Sensing. 29(1): 14–23. Gao, X., Wang, T. & Li, J. (2005). “A content-based image quality metric” Lecture Notes in Computer Science. 3642: 231–240. to get. Garguet-Duport, B., Girel, J., Chassery, J.-M. & Pautou, G. (1996). “The use of multi-resolution analysis and wavelet transform for merging SPOT panchromatic and multispectral imagery data” Photogrammetric Engineering and Remote Sensing. 62(9): 1057–1066. González-Audicana, M., Otazu, X., Fors, O. & Alvarez-Mozos, J. (2006). “A low computational-cost method to fuse IKONOS images using the spectral response function of its sensors” IEEE Transactions on Geoscience and Remote Sensing. 44(6): 1683–1691. González-Audicana, M., Saleta, J. L., Catalán, R. G. & Garc´ıa, R. (2004). “Fusion of multispectral and panchromatic images using improved IHS and PCA mergers based on wavelet decomposition” IEEE Transactions on Geoscience and Remote Sensing. 42(6): 1291–1299. Goodchild, M. (1980). “Fractals and the accuracy of geographical measures” Mathematical Geology. 12(2): 85–98. Goodchild, M. F. & Mark, D. M. (1987). “The fractal nature of geographic phenomena” Annals of Association of American Geographers. 77(2): 265–278. Jin, X. Y. & Davis, C. H. (2005). “Automated building extraction from high-resolution satellite imagery in urban areas using structural, contextual, and spectral information” EURASIP Journal of Applied Signal Processing. 2005(14): 2196–2206. Keshava, N. (2003). “A survey of spectral unmixing algorithms” Lincoln Laboratory Journal. 14(1): 55–78. Lam, N. S.-N. & Quattrochi, D. A. (1992). “On the issues of scale, resolution, and fractal analysis in the mapping sciences” Professional Geographer. 44(1): 88–98. Laporterie-Déjean, F., de Boissezon, H., Flouzat, G. & Lefèvre-Fonollosa, M.-J. (2005). “Thematic and statistical evaluations of five panchromatic/multispectral fusion methods on simulated PLEIADES-HR images” Information Fusion. 6(3): 193–212. Lasaponara, R. & Masini, N. (2005). “QuickBird-based analysis for the spatial characterization of archaeological sites: case study of the Monte Serico medieval village” Geophysical Research Letter. 32(12): L12313. Li, X., He, H. S., Bu, R., Wen, Q., Chang, Y., Hu, Y. & Li, Y. (2005). “The adequacy of different landscape metrics for various landscape patterns” Pattern Recognition. 38(12): 2626–2638. Malpica, J. A. (2007). “Hue adjustment to IHS pan-sharpened IKONOS imagery for vegetation enhancement” IEEE Geoscience and Remote Sensing Letters. 4(1): 27–31. Nichol, J. & Wong, M. S. (2005). “Satellite remote sensing for detailed landslide inventories using change detection and image fusion” International Journal of Remote Sensing. 26(9): 1913–1926. Pasqualini, V., Pergent-Martini, C., Pergent, G., Agreil, M., Skoufas, G., Sourbes, L. & Tsirika, A. (2005). “Use of SPOT 5 for mapping seagrasses: An application to Posidonia oceanica” Remote Sensing of Environment. 94(1): 39–45. Petrovic, V. (2007). “Subjective tests for image fusion evaluation and objective metric validation” Information Fusion. 8(2): 208–216. Pohl, C. & Genderen, J. L. V. (1998). “Multisensor image fusion in remote sensing: concepts, methods and applications” International Journal of Remote Sensing. 19(5): 823–854. Ranchin, T., Aiazzi, B., Alparone, L., Baronti, S. & Wald, L. (2003). “Image fusion–the ARSIS concept and some successful implementation schemes” Journal of Photogrammetry and Remote Sensing. 58: 4–18. Ranchin, T. & Wald, L. (2000). “Fusion of high spatial and spectral resolution images : the ARSIS concept and its implementation” Photogrammetric Engineering and Remote Sensing. 66(1): 49–61. Richter, R. (1998). “Correction of satellite imagery over mountainous terrain” Applied Optics. 37(18): 4004–4015. Shi, W., Zhu, C., Tian, Y. & Nichol, J. (2005). “Wavelet-based image fusion and quality assessment” International Journal of Applied Earth Observation and Geoinformation. 6: 241–251. Sirguey, P., Mathieu, R., Arnaud, Y., Khan, M. M. & Chanussot, J. (2008). “Improving MODIS spatial resolution for snow mapping using wavelet fusion and ARSIS concept” IEEE Geoscience and Remote Sensing Letters. 5(1): doi:10.1109/LGRS.2007.908884. In Press. Toet, A. & Franken, E. M. (2003). “Perceptual evaluation of different image fusion schemes” Displays. 24(1): 25–37. Tu, T.-M., Huang, P. S., Hung, C.-L. & Chang, C.-P. (2004). “A fast Intensity–Hue–Saturation fusion technique with spectral adjustment for IKONOS imagery” IEEE Geoscience and Remote Sensing Letters. 1(4): 309–312. Wald, L. (2000). “Quality of high resolution synthesized images: is there a simple criterion?” Proceedings of the International Conference on Fusion of Earth Data. SEE Gr éca Sophia Antipolis, France pp. 99–105. Wald, L., Ranchin, T. & Mangolini, M. (1997). “Fusion of satellite images of different spatial resolution: assessing the quality of resulting images” Photogrammetric Engineering and Remote Sensing. 63(6): 691–699. Wang, Z. & Bovik, A. C. (2002). “A universal image quality index” IEEE Signal Processing Letters. 9(3): 81–84. Woodcock, C. E. & Strahler, A. H. (1987). “The factor of scale in remote sensing” Remote Sensing of Environment. 21(3): 311–332. Xydeas, C. S. & Petrovic, V. (2000). “Objective image fusion performance measure” Electronics Letters. 36(4): 308–309. Zhai, G., Zhang, W., Yang, X. & Xu, Y. (2005). “Image quality assessment metrics based on multi-scale edge presentation” Proceedings of the IEEE Workshop on Signal Processing Systems Design and Implementation. IEEE IEEE. pp. 331–336. Zhan, Q., Molenaar, M., Tempfli, K. & Shi, W. (2005). “Quality assessment for geo-spatial objects derived from remotely sensed data” International Journal of Remote Sensing. 26(14): 2953–2974. Zhang, W. J. & Kang, J. Y. (2006). “QuickBird panchromatic and multi-spectral image fusion using wavelet packet transform” Intelligent Control and Automation. 344: 976–981. Zhang, Y. (2004). “Understanding image fusion” Photogrammetric Engineering and Remote Sensing. 70(6): 657–661