Reducing the random seed effect on segmentation by applying an edge-preserving filter

In region-growing segmentation algorithms random seed locations are used (reference). To ensure that repeating the segmentation will produce the same result, the seed locations are following a fixed random pattern. Empirical studies show that when the image that is subjected to the segmentation is changed by adding or removing rows or columns, the resulting segments are not identical anymore, the so-called seed effect. This occurs not only at the border of the image as one would intuitively expect, but also in the center part of the image. Apparently, the exact location of a seed affects the resulting segment. In this study I investigated whether application of an Edge-Preserving Smoothing Filter to an image prior to segmentation would reduce the effect of seed locations when rows or columns are added or removed from that image, and hence make the segmentation method by random seeds more robust. Two images were included: an IKONOS image of the central part of the Netherlands with fragmented land cover of agriculture, forest and villages and a SPOT5 2.5m multi-spectral image of a semi-desert steppe area in southeastern Kazakhstan. Both images were subjected to an Edge-Preserving Smoothing Filter before segmentation. This filter calculates variance for each band in nine different directions (8 wind directions + central area) and sums them per direction. The average band values of the direction with the lowest overall variance are then assigned to the central pixel. For both areas four subsets, each measuring 500x500 pixels, were selected representing different kinds of landscape with different patterns. For the IKONOS image the subsets covered a forested area with a golf course, a business area, a residential area, and an agricultural area. The subsets of the SPOT5 image covered a floodplain, a dune area with sparse vegetation where the soil is covered by lichens, a dune area with sparse vegetation but without lichens, and a dune area with many patches without vegetation and without lichens. In total 10x2 images were available for the analysis (8 subsets, 2 full images, original and EPSF). All images were segmented at five different heterogeneity levels. To quantify the effect of the seed location, the tessellations of the subsets were compared to the tessellation of the full image by overlaying and by analyzing the length of segment borders, both for the original and the EPSF versions. The length of segment borders that coincided between the subset and the full image was divided by the length of all borders in the subset (excluding the enveloping rectangle). The outcome was subtracted from 1 and this value was taken as a measure to quantify the seed effect. The results show that the segmentation results are more similar between the subset and the full image when the EPSF filter was applied before segmentation. In the heterogeneous area in the Netherlands, the seed effect was on average reduced by 6.6% when applying the EPSF filter. In the more homogenous area in the semi-desert in Kazakhstan, the seed effect was reduced by 48.8%. The seed effect strongly increases with higher heterogeneity levels, for all subsets and for both images. The explanation for the positive effect of the EPSF filter is likely found in the creation of very small homogenous patches. The seed pixels will be grouped with pixels from the same patch first, which reduces the location effect on the final segmentation

Method to monitor and quantify the environmental impact of European agriculture : conceptual outline

European policy is currently aiming at reducing the environmental impact of agriculture. To evaluate the effect of the measures, a Europe-wide method is needed that provides standardized information for all countries. This paper presents the outline of a method intended to determine where and to what extent the environmental impact of agriculture has changed. It consists of three procedures: general change detection, determination of agricultural presence in the region, and change identification. The method offers standardized information on the location and extent of changes in environmental impact caused by agriculture. Changed regions can be located shortly after the growing season, which enables the adjustment of policy to the actual situation. Hence, it will provide a useful tool to evaluate and formulate policy measures intended to influence the environmental impact of agriculture

A comparison of conventional and geostatistical methods to replace clouded pixels in NOAA-AVHRR images

The potential of using National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) images for large areas is often limited by cloud cover. It could be increased when small clouds are replaced by estimated reflection and emission values. In this study seven replacement methods are compared, ranging from simple replacement to stratified co-kriging. Images of subsequent days serve as co-variable, enabling the use of spatial and temporal information. For validation, cloud-free pixels were replaced with four patterns of artificially clouded pixels. Co-kriging as a combination of both temporal and spatial information resulted in the best estimates, reducing the mean squared errors by 20-70%. Stratification of the image did not result in better cloud replacement. Once kriging options have been implemented in existing image processing packages, co-kriging will be an easy-to-use solution to missing values, provided that images of subsequent days of low cloud coverage are available. | The potential of using National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) images for large areas is often limited by cloud cover. It could be increased when small clouds are replaced by estimated reflection and emission values. In this study seven replacement methods are compared, ranging from simple replacement to stratified co-kriging. Images of subsequent days serve as co-variable, enabling the use of spatial and temporal information. For validation, cloud-free pixels were replaced with four patterns of artificially clouded pixels. Co-kriging as a combination of both temporal and spatial information resulted in the best estimates, reducing the mean squared errors by 20-70%. Stratification of the image did not result in better cloud replacement. Once kriging options have been implemented in existing image processing packages, co-kriging will be an easy-to-use solution to missing values, provided that images of subsequent days of low cloud coverage are available

Pilot of production of a geomorphological map by Object-Based Image Analysis

We hebben op basis van eerder werk in de Eems-Dollard een methode ontwikkeld om biogeomorfologische kaarten te maken ten behoeve van ecotopenkaarten op grond van luchtfoto’s in kleur met nabij-infrarood. Deze methode maakt niet alleen gebruik van de spectrale informatie, maar ook van de structuur van het landschap boven water. Het resultaat is een vlakdekkende kaart met biogeomorfologische eenheden die passen in het zoute-ecotopenstelsel. De handmatig en automatisch geproduceerde kaarten zijn goed vergelijkbaar met 73% overeenkomstig oppervlak met dezelfde klasse (antwoord op onderzoeksvraag 1). De automatische methode levert veel gedetailleerdere kaarten maar de verschillen tussen beide methoden zijn niet toe te schrijven aan het detail, maar vooral aan verschillend geclassificeerde objecten (1e). Een handmatige controle van de geautomatiseerde kaart gaf aan dat 21% van het oppervlak een verkeerd label had gekregen. De minst hoge overeenkomsten werden gevonden voor eenheden met de legendaklassen hoog- en laag-energetische plaat. Een aanpassing om deze verwarring te verminderen door het creeren van kleinere objecten wordt momenteel al doorgevoerd. Toepassing van de automatische methode op de beelden van 2010 zonder het bijstellen van drempels leverde 67% overeenkomst op (2). Deze waarden moeten worden gelezen als het verschil tussen de handmatige en automatische methode, waarbij niet duidelijk is welke fouten in welke mate van één van de twee methoden afkomstig zijn. De automatische methode maakt het mogelijk om per object vast te stellen in welke stap van de methode deze worden geclassificeerd. Dit is een vorm van onzekerheid waarop de handmatige controlestappen kunnen worden geconcentreerd, zonder noodzaak om nieuwe grenzen te trekken, waarmee een expert judgementstap na productie is vereenvoudigd. De automatische methode is toepasbaar op watersysteemniveau (2), waarbij na digitaal beschikbaar stellen van geogerefereerde beelden in minimaal een week een kaart kan worden uitgerekend. De kale rekentijd bedraagt hier 24 uur waarbij er 2 tegels tegelijk geclassificeerd worden. De methode is opgezet met drempelwaarden voor brightness en NDVI die voor het gehele gebied worden opgegeven, en welke moeten worden bijgesteld voor verschillende sets foto’s in verschillende gebieden. Drempelwaarden voor uit hoogtegegevens berekende helling en variatie daarin hangen af van de ruimtelijke resolutie

A semi-automatic cropland mapping approach using GEOBIA and random forests on black-and-white aerial photography

For decades land-use and land-cover (LULC) conversions have had an important impact on land- and ecosystem degradation, accordingly (historical) LULC information is important for the assessment of such impacts. This information can be derived from black-and-white (B&W) aerial photography. Such photography is often visually interpreted, which is a very time-consuming approach. This study shows that machine learning can be applied on only brightness to derive LULC information. Cropland acreage is semi-automatically mapped by means of Geographic Object-Based Image Analysis (GEOBIA) and Random Forest classification in two study sites in Ethiopia and in The Netherlands. The result is a thematic map with two classes: 1) agricultural cropland and 2) other types of land cover. Overall mapping accuracies attained are 90 % and 96 % for the two study areas respectively. This mapping method increases the timeline at which historical cropland expansion can be mapped purely from brightness information in B&W photography up to the 1930s