Spectral-spatial classification for hyperspectral image by bilateral filtering and morphological features

Hyperspectral (HS) imagery contains a wealth of spectral and spatial information that can improve target detection and recognition performance. Conventional spectral-spatial classification methods cannot fully exploit both spectral and spatial information of HS image. In this paper, we propose a new method to fuse the spectral and spatial information for HS image classification. Our approach transfers the spatial structures of the whole morphological profile into the original HS image by using bilateral filtering, and obtains an enhanced HS image enriching both spectral and spatial information. Meanwhile, the enhanced HS image has the same spectral and spatial dimensions as the original HS image, which may provide a new input to improve the performances of existing HS image classification methods. Experimental results on real HS images are very encouraging. Compared to the methods using only single feature and stacking all the features together, the proposed fusion method improves the overall classification accuracy more than 10% and 5%, respectively

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The World Urban Database and Access Portal Tools (WUDAPT) is a community initiative to collect worldwide data on urban form (i.e., morphology, materials) and function (i.e., use and metabolism). This is achieved through crowdsourcing, which we define here as the collection of data by a bounded crowd, composed of students. In this process, training data for the classification of urban structures into Local Climate Zones (LCZ) are obtained, which are, like most volunteered geographic information initiatives, of unknown quality. In this study, we investigated the quality of 94 crowdsourced training datasets for ten cities, generated by 119 students from six universities. The results showed large discrepancies and the resulting LCZ maps were mostly of poor to moderate quality. This was due to general difficulties in the human interpretation of the (urban) landscape and in the understanding of the LCZ scheme. However, the quality of the LCZ maps improved with the number of training data revisions. As evidence for the wisdom of the crowd, improvements of up to 20% in overall accuracy were found when multiple training datasets were used together to create a single LCZ map. This improvement was greatest for small training datasets, saturating at about ten to fifteen sets

The potential of OBIA for SAR-based flood mapping

International audienceIn our changing world, floods are a threat of increasing importance causing major fatalities and economic losses. Within this perspective, flood extent mapping is of great importance for both damage assessment and improving flood forecasts. While flood mapping through optical imagery is often hampered by the presence of clouds, Synthetic Aperture Radar (SAR) sensors are capable of sensing in all weather conditions during both day and night. Moreover, recently launched missions such as the Sentinel-1 and COSMO-SkyMed constellations provide improved temporal and spatial resolutions, thus even further increasing the potential of SAR for systematic flood mapping and monitoring. Due to their specular reflectance properties, open water surfaces typically appear dark and homogeneous on SAR images. Classification of these images is generally performed using a pixel-based approach. Frequently used algorithms include histogram thresholding, active contour models and pixel-based change detection methods. The use of object-based approaches for SAR-based flood mapping remains rare, although a couple of studies have worked with a segmentation step. However, pixel-based approaches suffer from quite some drawbacks. Especially thresholding typically results in classification products that still include a large number of dispersed misclassified pixels, thus requiring a post-processing step. Although computationally more expensive, active contour models mostly lead to higher accuracies, which indicates the importance of spatial context. An object-based approach allows taking into account this spatial context, as well as some other relevant properties such as object shape, proximity and homogeneity. Moreover, it is possible to include some additional information sources such as elevation data, land cover data and optical imagery. This study aims at further investigating the potential of OBIA for SAR-based flood mapping applications. A range of established pixel-based approaches will serve as a benchmark. Preliminary results demonstrate the benefit of both segmenting the image into objects as well as incorporating additional information sources

From Pixels to Geographic Objects in Remote Sensing Image Analysis

Traditional image analysis methods are mostly pixel-based and use the spectral differences of landscape elements at the Earth surface to classify these elements or to extract element properties from the Earth Observation image. Geographic object-based image analysis (GEOBIA) has received considerable attention over the past 15 years for analyzing and interpreting remote sensing imagery. In contrast to traditional image analysis, GEOBIA works more like the human eye–brain combination does. The latter uses the object’s color (spectral information), size, texture, shape and occurrence to other image objects to interpret and analyze what we see. GEOBIA starts by segmenting the image grouping together pixels into objects and next uses a wide range of object properties to classify the objects or to extract object’s properties from the image. Significant advances and improvements in image analysis and interpretation are made thanks to GEOBIA. In June 2010 the third conference on GEOBIA took place at the Ghent University after successful previous meetings in Calgary (2008) and Salzburg (2006). This special issue presents a selection of the 2010 conference papers that are worked out as full research papers for JAG. The papers cover GEOBIA applications as well as innovative methods and techniques. The topics range from vegetation mapping, forest parameter estimation, tree crown identification, urban mapping, land cover change, feature selection methods and the effects of image compression on segmentation. From the original 94 conference papers, 26 full research manuscripts were submitted; nine papers were selected and are presented in this special issue. Selection was done on the basis of quality and topic of the studies. The next GEOBIA conference will take place in Rio de Janeiro from 7 to 9 May 2012 where we hope to welcome even more scientists working in the field of GEOBIA

Monitoring trends of mangrove disturbance at the tin mining area of Bangka Island using Landsat time series and LandTrendr

Open-pit tin mining systems in Bangka Island have an outrageous impact due to massive changes in the landscape. The land clearing process in the coastal area before the mining activity and its tailings might cause the mangrove disruption. However, to date, spatially-distributed information on mangrove disturbance due to mining is limited. This research hence aims to fill this gap using remote sensing technology. Trends/dynamics in mangrove disturbance and recovery are studied for the period 1988 to 2021 using the LandTrendr-algorithm implemented on the Google Earth Engine platform. NBR (Normalized Burn Ratio), NDMI (Normalized Difference Moisture Index), NDVI (Normalized Difference Vegetation Index), and Enhanced Vegetation Index (EVI) were used as vegetation indicators to detect the disturbance in mangroves areas. The results show that a significant disturbance occurred, shown by the decrease in NBR, NDMI, NDVI, and EVI values. Some observation areas are still experiencing ongoing disturbance by the end of observing time (2021)

Integration of Satellite Imagery, Topography and Human Disturbance Factors Based on Canonical Correspondence Analysis Ordination for Mountain Vegetation Mapping: A Case Study in Yunnan, China

The integration between vegetation data, human disturbance factors, and geo-spatial data (Digital Elevation Model (DEM) and image data) is a particular challenge for vegetation mapping in mountainous areas. The present study aimed to incorporate the relationships between species distribution (or vegetation spatial distribution pattern) and topography and human disturbance factors with remote sensing data, to improve the accuracy of mountain vegetation maps. Two different mountainous areas located in Lancang (Mekong) watershed served as study sites. An Artificial Neural Network (ANN) architecture classification was used as image classification protocol. In addition, canonical correspondence analysis (CCA) ordination was applied to address the relationships between topography and human disturbance factors with the spatial distribution of vegetation patterns. We used ordinary kriging at unobserved locations to predict the CCA scores. The CCA ordination results showed that the vegetation spatial distribution patterns are strongly affected by topography and human disturbance factors. The overall accuracy of vegetation classification was significantly improved by incorporating DEM or four CCA axes as additional channels in both the northern and southern study areas. However, there was no significant difference between using DEM or four CCA axes as extra channels in the northern steep mountainous areas because of a strong redundancy between CCA axes and DEM data. In the southern lower mountainous areas, the accuracy was significantly higher using four CCA axes as extra bands, compared to using DEM as an extra band. In the southern study area, the variance of vegetation data explained by human disturbance factors was larger than the variance explained by topographic attributes

Revealing Kunming's (China) Historical Urban Planning Policies Through Local Climate Zones

status: publishe

Heat risk assessment for the Brussels capital region under different urban planning and greenhouse gas emission scenarios

Urban residents are exposed to higher levels of heat stress in comparison to the rural population. As this phenomenon could be enhanced by both global greenhouse gas emissions (GHG) and urban expansion, urban planners and policymakers should integrate both in their assessment. One way to consider these two concepts is by using urban climate models at a high resolution. In this study, the influence of urban expansion and GHG emission scenarios is evaluated at 100 m spatial resolution for the city of Brussels (Belgium) in the near (2031-2050) and far (2081-2100) future. Two possible urban planning scenarios (translated into local climate zones, LCZs) in combination with two representative concentration pathways (RCPs 4.5 and 8.5) have been implemented in the urban climate model UrbClim. The projections show that the influence of GHG emissions trumps urban planning measures in each period. In the near future, no large differences are seen between the RCP scenarios; in the far future, both heat stress and risk values are twice as large for RCP 8.5 compared to RCP 4.5. Depending on the GHG scenario and the LCZ type, heat stress is projected to increase by a factor of 10 by 2090 compared to the present-day climate and urban planning conditions. The imprint of vulnerability and exposure is clearly visible in the heat risk assessment, leading to very high levels of heat risk, most notably for the North Western part of the Brussels Capital Region. The results demonstrate the need for mitigation and adaptation plans at different policy levels that strive for lower GHG emissions and the development of sustainable urban areas safeguarding livability in cities.status: publishe