Object‐based classification of residential land use within Accra,Ghana based on QuickBird satellite data

A segmentation and hierarchical classification approach applied to QuickBird multispectral satellite data was implemented, with the goal of delineating residential land use polygons and identifying low and high socio‐economic status of neighbourhoods within Accra, Ghana. Two types of object‐based classification strategies were tested, one based on spatial frequency characteristics of multispectral data, and the other based on proportions of Vegetation–Impervious–Soil sub‐objects. Both approaches yielded residential land‐use maps with similar overall percentage accuracy (75%) and kappa index of agreement (0.62) values, based on test objects from visual interpretation of QuickBird panchromatic imagery.     

Extreme‐learning‐machine‐based land cover classification

The extreme learning machine (ELM), a single hidden layer neural network based supervised classifier is used for remote sensing classifications. In comparison to the backpropagation neural network, which requires the setting of several user‐defined parameters and may produce local minima, the ELM requires setting of one parameter, and produces a unique solution for a set of randomly assigned weights. Two datasets, one multispectral and another hyperspectral, were used for classification. Accuracies of 89.0% and 91.1% are achieved with this classifier using multispectral and hyperspectral data, respectively. Results suggest that the ELM provides a classification accuracy comparable to a backpropagation neural network with both datasets. The computational cost using the ELM classifier (1.25 s with Enhanced Thematic Mapper (ETM+) and 0.675 s with Digital Airborne Imaging Spectrometer (DAIS) data) is very small in comparison to the backpropagation neural network.     

SAR‐based land cover classification of Kuwait

Orbital synthetic aperture radar (SAR) C‐band data acquired by ERS‐1/2 in vv‐polarization and Radarsat in hh‐polarization during the period from 1996 to 1999 were used to evaluate their combined information potential for classification of land cover in the arid environment of Kuwait. Individual SAR scenes were orthorectified using a digital elevation model (DEM) of Kuwait, radiometrically adjusted for incidence angle effects, and mosaics were generated for the whole country. The data were coregistered as multichannel composites and integrated with geographical information system (GIS) layers of roads, hydrology, soils and vegetation. An adaptive spatial filter was used to increase the number of effective independent looks prior to generation of feature vectors based on SAR backscatter power values. A total of 13 classes of the joint ERS‐1/2 and Radarsat images were identified based on Bhattacharya distance and geospatial pattern. The C‐band radar backscatter observed by ERS and Radarsat was found to be related to vegetation cover, surface roughness, percentage of coarse material in the surface layer and moisture conditions. These factors are not independent, but are known to be correlated. The complexity of these dependencies made unambiguous classification of surface material difficult when using C‐band data alone. Nevertheless, class labels were assigned using a maximum likelihood supervised classification incorporating field measurements and ancillary data such as soil, and surface sediment maps. When used in a simple two‐class classification (e.g. low vs. high vegetation cover fraction, or smooth vs. rough soils), the overall accuracy of the combined ERS and Radarsat data was between 70 and 80%. The generated dataset is amenable to several label definitions based on the requirements of the intended use.     

Artificial immune‐based supervised classifier for land‐cover classification

This paper explores the potential of an artificial immune‐based supervised classification algorithm for land‐cover classification. This classifier is inspired by the human immune system and possesses properties similar to nonlinear classification, self/non‐self identification, and negative selection. Landsat ETM+ data of an area lying in Eastern England near the town of Littleport are used to study the performance of the artificial immune‐based classifier. A univariate decision tree and maximum likelihood classifier were used to compare its performance in terms of classification accuracy and computational cost. Results suggest that the artificial immune‐based classifier works well in comparison with the maximum likelihood and the decision‐tree classifiers in terms of classification accuracy. The computational cost using artificial immune based classifier is more than the decision tree but less than the maximum likelihood classifier. Another data set from an area in Spain is also used to compare the performance of immune based supervised classifier with maximum likelihood and decision‐tree classification algorithms. Results suggest an improved performance with the immune‐based classifier in terms of classification accuracy with this data set, too. The design of an artificial immune‐based supervised classifier requires several user‐defined parameters to be set, so this work is extended to study the effect of varying the values of six parameters on classification accuracy. Finally, a comparison with a backpropagation neural network suggests that the neural network classifier provides higher classification accuracies with both data sets, but the results are not statistically significant.     

Object‐oriented classification for urban land cover mapping with ASTER imagery

This document demonstrates the potential of using an object‐oriented approach to map urban land cover. One objective of this work was to test the ability of the object‐oriented classification in the generation of urban land cover maps. Anotehr was to produce an updated land cover map for the city of Beijing from Advanced Spaceborne Thermal Emission and Reflecton Radiometer (ASTER) data, with an evaluation of its accuracy.     

Updating land cover classification using a rule‐based decision system

This paper proposes a land cover classification methodology in agricultural contexts that provides satisfactory results with a single satellite image per year acquired during the growing period. Our approach incorporates ancillary data such as cropping history (inter‐annual crop rotations), context (altitude, soil type) and structure (parcels size and shape) to compensate for the lack of radiometric data resulting from the use of a single image. The originality of the proposed method resides in the three successive steps used: S1: per‐pixel classification of a single SPOT XS image with a restricted number of land cover classes (RL) chosen to ensure good accuracy; S2: conversion of RLs into a per‐parcel classification system using ancillary parcel boundaries; and S3: parcel allocation using exhaustive land cover classes (EL) and its refinement through the application of decision rules. The method was tested on a 120?km² area (Sousson river basin, Gers, France) where exhaustive knowledge of land cover for two successive years allowed complete validation of our method. It allocated 87% of the parcels with a 75% accuracy rate according to the exhaustive list (EL). This is a satisfactory result obtained with one SPOT XS image in a small agricultural parcel context.     

The use of the Minnaert correction for land‐cover classification in mountainous terrain

Land‐cover classifications in mountainous terrain are often hampered by the topographic effect. Several strategies can be pursued to correct for this. A traditional approach is to use training areas for the same land‐cover class for different topographic positions and later merge those into one class. Other solutions involve topographic corrections, such as a Minnaert correction. In this study the classification result of the traditional training‐area approach was compared with the classification result of a Minnaert‐corrected image. In order to derive the Minnaert constants, a SPOT XS scene of the Santa Monica Mountains, USA, was divided into three visually relatively homogeneous regions. Eighty per cent of the pixels were assigned the same land cover in both classifications. Differences in classification were mainly in the section of the image that had more diverse land cover than in the more homogeneous chaparral‐covered eastern section. This supports previous findings that the Minnaert constant needs to be derived for individual land‐cover classes. The findings also suggest that after the Minnaert correction the resulting classification is comparable to the classification obtained using a more traditional approach.     

Urban land cover multi‐level region‐based classification of VHR data by selecting relevant features

The limited spatial resolution of satellite images used to be a problem for the adequate definition of the urban environment. This problem was expected to be solved with the availability of very high spatial resolution satellite images (IKONOS, QuickBird, OrbView‐3). However, these space‐borne sensors are limited to four multi‐spectral bands and may have specific limitations as far as detailed urban area mapping is concerned. It is therefore essential to combine spectral information with other information, such as the features used in visual interpretation (e.g. the degree and kind of texture and the shape) transposed to digital analysis. In this study, a feature selection method is used to show which features are useful for particular land‐cover classes. These features are used to improve the land‐cover classification of very high spatial resolution satellite images of urban areas. The useful features are compared with a visual feature selection. The features are calculated after segmentation into regions that become analysis units and ease the feature calculation.     

Object‐based land‐cover classification for the Phoenix metropolitan area: optimization vs. transportability

An object‐based approach was utilized in the development of two urban land‐cover classification schemes on high‐resolution (0.6 m), true‐colour aerial photography of the Phoenix metropolitan area, USA. An initial classification scheme was heavily weighted by standard nearest‐neighbour (SNN) functions generated by samples from each of the classes, which produced an enhanced accuracy (84%). A second classification was developed from the initial classification scheme in which SNN functions were transformed into a fuzzy‐rule set, creating a product transportable to different areas of the same imagery, or for land‐cover change detection with similar imagery. A comprehensive accuracy assessment revealed a slightly lower overall accuracy (79%) for the rule‐based classification. We conclude that the transportable classification scheme is satisfactory for general land‐cover analyses; yet classification accuracy can be enhanced at site‐specific venues with the incorporation of nearest‐neighbour functions using class samples.     

PCA‐based land‐use change detection and analysis using multitemporal and multisensor satellite data

Remote‐sensing change detection based on multitemporal, multispectral, and multisensor imagery has been developed over several decades and provided timely and comprehensive information for planning and decision‐making. In practice, however, it is still difficult to select a suitable change‐detection method, especially in urban areas, because of the impacts of complex factors. This paper presents a new method using multitemporal and multisensor data (SPOT‐5 and Landsat data) to detect land‐use changes in an urban environment based on principal‐component analysis (PCA) and hybrid classification methods. After geometric correction and radiometric normalization, PCA was used to enhance the change information from stacked multisensor data. Then, a hybrid classifier combining unsupervised and supervised classification was performed to identify and quantify land‐use changes. Finally, stratified random and user‐defined plots sampling methods were synthetically used to obtain total 966 reference points for accuracy assessment. Although errors and confusion exist, this method shows satisfying results with an overall accuracy to be 89.54% and 0.88 for the kappa coefficient. When compared with the post‐classification method, PCA‐based change detection also showed a better accuracy in terms of overall, producer's, and user's accuracy and kappa index. The results suggested that significant land‐use changes have occurred in Hangzhou City from 2000 to 2003, which may be related to rapid economy development and urban expansion. It is further indicated that most changes occurred in cropland areas due to urban encroachment.     

Study on optimization‐based layered classification for separation of wetlands

The classification and dynamics monitoring of wetlands using remotely sensed data is a complicated, time‐consuming process involving high costs, and the accuracy varies depending on the techniques used for image processing and analysis, and the time and costs required for training, etc. This paper presents an optimization‐based layered classification method for the classification and dynamics monitoring of wetlands based on a decision procedure of multiple objectives. Four driven factors including techniques to be used, classification accuracy, and the time and cost needed for the classification, were selected as the indicators of criteria in optimization of the layered classification. This method was applied to Thematic Mapper (TM) image classification of the wetlands in Minjiang River estuary and led to the overall correct percentage of 85.13% for seven categories of the wetlands.     

GIS and remote sensing as tools for the simulation of urban land‐use change

The Intensity Hue Saturation (IHS) transform, a popular image fusion technique to exploit the complementary nature of multi-sensor image data, can be expressed in spherical or cylindrical coordinates. This letter discusses the differences between both transforms with respect to image fusion.     

Effects of land‐use categorization on landscape metrics: A case study in urban landscape of Shenzhen,China

Using remotely sensed data, landscape pattern analysis based on landscape metrics has been one of the major topics of landscape ecology, and more attention has been focused on the effects of spatial scale and the accuracy of remotely sensed data on landscape metrics. However, few studies have been conducted to assess the change of landscape metrics under the influence of land‐use categorization. In this paper, we took the Bao'an district of Shenzhen city as the study area, to analyse how land‐use categorization would influence changes in 24 landscape metrics. The results showed a significant influence, and based on the characteristics of the response curves of landscape metrics associated with the change in land‐use categorization in regression analysis, and the predictability of these relations, the 24 landscape metrics fell into three groups. (1) Type I included 12 landscape metrics, and showed a strong predictability with changing of land‐use categorization with simple function relations in regression analysis. (2) Type II included seven indices, and exhibited complicated behaviours against changing of land‐use categorization. The response curves of these metrics, which were not easy to predict, consisted of two subsections and could not be described by a single function. (3) Type III included five indices, and showed unpredictable behaviours against the change of the land‐use categorization. Their response curves could not be described by a certain function. This study highlights the need for the analysis of effects of land‐use categorization on landscape metrics so as to clearly quantify landscape patterns, and provides insights into the selection of landscape metrics for comparative research on a given area under different land‐use categorizations.     

Fuzzy segmentation for object‐based image classification

This Letter proposes an object‐based image classification procedure which is based on fuzzy image‐regions instead of crisp image‐objects. The approach has three stages: (a) fuzzification in which fuzzy image‐regions are developed, resulting in a set of images whose digital values express the degree of membership of each pixel to target land‐cover classes; (b) feature analysis in which contextual properties of fuzzy image‐regions are quantified; and (c) defuzzification in which fuzzy image‐regions are allocated to target land‐cover classes. The proposed procedure is implemented using automated statistical techniques that require very little user interaction. The results indicate that fuzzy segmentation‐based methods produce acceptable thematic accuracy and could represent a viable alternative to current crisp image segmentation approaches.     

RVM‐based multi‐class classification of remotely sensed data

The relevance vector machine (RVM), a Bayesian extension of the support vector machine (SVM), has considerable potential for the analysis of remotely sensed data. Here, the RVM is introduced and used to derive a multi‐class classification of land cover with an accuracy of 91.25%, a level comparable to that achieved by a suite of popular image classifiers including the SVM. Critically, however, the output of the RVM includes an estimate of the posterior probability of class membership. This output may be used to illustrate the uncertainty of the class allocations on a per‐case basis and help to identify possible routes to further enhance classification accuracy.     

A multiresolution spectral angle‐based hyperspectral classification method

Due to the lack of training samples, hyperspectral classification often adopts the minimum distance classification method based on spectral metrics. This paper proposes a novel multiresolution spectral‐angle‐based hyperspectral classification method, where band subsets will be selected to simultaneously minimize the average within‐class spectral angle and maximize the average between‐class spectral angle. The method adopts a pairwise classification framework (PCF), which decomposes the multiclass problem into two‐class problems. Based on class separability criteria, the original set of bands is recursively decomposed into band subsets for each two‐class problem. Each subset is composed of adjacent bands. Then, the subsets with high separability are selected to generate subangles, which will be combined to measure the similarity. Following the PCF, the outputs of all the two‐class classifiers are combined to obtain the final output. Tested with an Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data set for a six‐class problem, the results demonstrate that our method outperforms the previous spectral metric‐based classification methods.     

MERIS‐FR potential for land use–land cover mapping in Spain

Abstract

Since 1984, national and international agencies have sought to improve their ability to forecast famine in sub-Saharan Africa. A number of early warning systems have been implemented for this purpose that monitor physical and social variables that may indicate the likelihood and magnitude of famine. Several famine early warning systems use satellite remote sensing data to supplement ground-based observations. These systems have demonstrated the advantages in timeliness and consistency of remote sensing data. Although user needs have not been clearly defined, experience gained in the operation of early warning systems and the results of related research suggest that: (a) at the continental scale AVHRR GAC data offer many advantages over traditional, ground data sources;

(b) quantitative crop yield estimates might be improved through consideration of both photosynthetic activity of the vegetation and length of growing season;

(c)qualitative comparisons of crop years have provided useful inputs to current early warning needs; and (d) stratification of the region into coherent geographical areas would improve all estimates:     

What are the effects of Agro-Ecological Zones and land use region boundaries on land resource projection using the Global Change Assessment Model?

Understanding potential impacts of climate change is complicated by spatially mismatched land representations between gridded datasets and models, and land use models with larger regions defined by geopolitical and/or biophysical criteria. Here we quantify the sensitivity of Global Change Assessment Model (GCAM) outputs to the delineation of Agro-Ecological Zones (AEZs), which are normally based on historical (1961–1990) climate. We reconstruct GCAM's land regions using projected (2071–2100) climate, and find large differences in estimated future land use that correspond with differences in agricultural commodity prices and production volumes. Importantly, historically delineated AEZs experience spatially heterogeneous climate impacts over time, and do not necessarily provide more homogenous initial land productivity than projected AEZs. We conclude that non-climatic criteria for land use region delineation are likely preferable for modeling land use change in the context of climate change, and that uncertainty associated with land delineation needs to be quantified.     

An object‐oriented approach for analysing and characterizing urban landscape at the parcel level

This paper presents an object‐oriented approach for analysing and characterizing the urban landscape structure at the parcel level using high‐resolution digital aerial imagery and LIght Detection and Ranging (LIDAR) data. Additional spatial datasets including property parcel boundaries and building footprints were used to both facilitate object segmentation and obtain greater classification accuracy. The study area is the Gwynns Falls watershed, which includes portions of Baltimore City and Baltimore County, MD. A three‐level hierarchical network of image objects was generated, and objects were classified. At the two lower levels, objects were classified into five classes, building, pavement, bare soil, fine textured vegetation and coarse textured vegetation, respectively. The object‐oriented classification approach proved to be effective for urban land cover classification. The overall accuracy of the classification was 92.3%, and the overall Kappa statistic was 0.899. Land cover proportions as well as vegetation characteristics were then summarized by property parcel. This exercise resulted in a knowledge base of rules for urban land cover classification, which could potentially be applied to other urban areas.     

Evaluation of segment‐based gap‐filled Landsat ETM+ SLC‐off satellite data for land cover classification in southern Saskatchewan,Canada

This paper describes single‐date and multi‐date land‐cover classification accuracy results using segment‐based, gap‐filled Landsat 7 Enhanced Thematic Mapper data compared with Landsat 5 Thematic Mapper data captured one day apart. Maximum likelihood and Decision tree classification algorithms were evaluated. The same training and verification sets of ground data were used for each classification evaluation. For the comparison with the single‐date classification, an average decrease of 2.8% in the classification accuracy was obtained with the use of the gap‐filled Landsat data. Area estimates for the mid‐summer images differed, on average, from 0.6% to 1.9% for a four‐class and eight‐class classification, respectively. A multi‐date land‐cover classification was also completed with the addition of a late spring Landsat 5 image, resulting in an average decrease in classification accuracy of 1.8%.