Assessing the performance of two unsupervised dimensionality reduction techniques on hyperspectral APEX data for high resolution urban land-cover mapping

Despite the high richness of information content provided by airborne hyperspectral data, detailed urban land-cover mapping is still a challenging task. An important topic in hyperspectral remote sensing is the issue of high dimensionality, which is commonly addressed by dimensionality reduction techniques. While many studies focus on methodological developments in data reduction, less attention is paid to the assessment of the proposed methods in detailed urban hyperspectral land-cover mapping, using state-of-the-art image classification approaches. In this study we evaluate the potential of two unsupervised data reduction techniques, the Autoassociative Neural Network (AANN) and the BandClust method – the first a transformation based approach, the second a feature-selection based approach – for mapping of urban land cover at a high level of thematic detail, using an APEX 288-band hyperspectral dataset. Both methods were tested in combination with four state-of-the-art machine learning classifiers: Random Forest (RF), AdaBoost (ADB), the multiple layer perceptron (MLP), and support vector machines (SVM). When used in combination with a strong learner (MLP, SVM) BandClust produces classification accuracies similar to or higher than obtained with the full dataset, demonstrating the method’s capability of preserving critical spectral information, required for the classifier to successfully distinguish between the 22 urban land-cover classes defined in this study. In the AANN data reduction process, on the other hand, important spectral information seems to be compromised or lost, resulting in lower accuracies for three of the four classifiers tested. Detailed analysis of accuracies at class level confirms the superiority of the SVM/Bandclust combination for accurate urban land-cover mapping using a reduced hyperspectral dataset. This study also demonstrates the potential of the new APEX sensor data for detailed mapping of land cover in spatially and spectrally complex urban areas.     

Per-pixel and object-oriented classification methods for mapping urban land cover extraction using SPOT 5 imagery

To have sustainable management and proper decision-making, timely acquisition and analysis of surface features are necessary. Traditional pixel-based analysis is the popular way to extract different categories, but it is not comparable by the achievements that can be achieved through the object-based method that uses the additional characteristics of features in the process of classification. In this paper, three types of classification were used to classify SPOT 5 satellite image in mapping land cover; Support vector machine (SVM) pixel-based, SVM object-based and Decision Tree (DT) pixel-based classification. Normalised Difference Vegetation Index and the brightness value of two infrared bands (NIR and SWIR) were used in manually developed DT classification. The classification of the SVM (pixel based) was generated using the selected groups of pixels that represent the selected features. In addition, the SVM (object based) was implemented by using radial-based function kernel. The classified features were oil palm, rubber, urban area, soil, water and other vegetation. The study found that the overall classification of the DT was the lowest at 69.87% while those of SVM (pixel based) and SVM (object based) were 76.67 and 81.25%, respectively.     

An Analysis of Texture Measures in PCA-Based Unsupervised Classification of SAR Images

In single-band single-polarized SAR images, intensity and texture are the information source available for unsupervised land cover classification. Every textural feature measure identifies texture patterns by different approaches. For efficient land cover classification, textural measures have to be chosen suitably. Therefore, in this letter, the role of various intensity and textural measures is analyzed for their discriminative ability for unsupervised SAR image classification into various land cover types like water, urban, and vegetation areas. To make the algorithm adaptable, these textural features are fused using principal component analysis (PCA), and principal components are used for classification purposes. To highlight the effectiveness of PCA, the difference between PCA- and non-PCA-based classifications is also analyzed. Analysis of the role of texture measures for unsupervised classification of real-world SAR data with application of PCA is presented in this letter. The analysis of how every individual feature measure contributes for classification process is presented, and then, textural measures for a feature set are chosen according to their role in improving classification accuracy. By analysis, it is observed that the feature set comprising mean, variance, wavelet components, semivariogram, lacunarity, and weighted rank fill ratio provides good classification accuracy of up to 90.4% than by using individual textural measures, and this increased accuracy justifies the complexity involved in the process.     

Vegetation mapping with multitemporal NDVI in North Eastern China Transect (NECT)

Temporal changes in the normalized difference vegetation index (NDVI) have been widely used in vegetation mapping due to the usefulness of NDVI data in distinguishing characteristic seasonal differences in the phenology of greenness of vegetation cover. Research has also shown that NDVI provides potential to derive meaningful metrics that describe ecosystem functions. In this paper, we have applied both unsupervised “k-means” classification and supervised minimum distance classification as derived from temporal changes in NDVI measured in 1997 along the North Eastern China Transect (NECT), and we have also utilized the same two classification methods together with NDVI-derived metrics, namely maximum NDVI, mean NDVI, NDVI amplitude, NDVI threshold, total length of growing season, fraction of growing season during greenup, rate of greenup, rate of senescence, integrated NDVI during the growing season, and integrated NDVI during greenup/integrated NDVI during senescence to map vegetation. The main objectives of this study are: (1) to test the relative performance of NDVI temporal profile metrics and NDVI-derived metrics for vegetation cover discrimination in NECT; (2) to test the relative performance of unsupervised (k-means) and supervised (minimum distance) methods for vegetation mapping; (3) to test the accuracy of the IGBP-DIS released land cover map for NECT; (4) to provide an up-to-date vegetation map for NECT. The results suggest that the classifications based on NDVI temporal profile metrics have higher accuracies than those based on any other metrics, such as NDVI-derived metrics, or all (NDVI temporal profile metrics + NDVI-derived metrics), or 15 metrics (NDVI temporal profile + Rate of greenup, Rate of senescence, and Integrated NDVI in greenup/integrated NDVI in senescence) for both methods. And among them, unsupervised k-means classification had the highest overall accuracy of 52% and Kappa coefficient of 0.2057. Both unsupervised (k-means) and supervised (minimum distance) methods achieved similar accuracies for the same metrics. The accuracy of IGBP-DIS released land cover map had an overall accuracy of 37% and a Kappa coefficient is 0.1441, and can improve to 46% by decomposing the crop/natural vegetation mosaic to cropland and other natural vegetation types. The results support using unsupervised k-means classification based on NDVI temporal profile metrics to provide an up-to-date vegetation cover classification. However, new effort is necessary in the future in order to improve the overall performance on this issue.     

An Evaluation of Four Different Wavelet Decomposition Procedures for Spatial Feature Discrimination in Urban Areas

Texture or spatial arrangement of neighborhood objects and features plays an important role in the human visual system for pattern recognition and image classification. The traditional spectral–based image processing techniques have proven inadequate for urban land use and land cover mapping from images acquired by the current generation of fine–resolution satellites. This is because of the high frequency spatial arrangements or complex nature of urban features. There is a need for an effective algorithm to digitally classify urban land use and land cover categories using high–resolution image data. Recent studies using wavelet transforms for texture analysis have generally reported better accuracy. Based on a high–resolution ATLAS image, this study illustrates four different wavelet decomposition procedures – the standard, horizontal, vertical, and diagonal decompositions – for urban land use and land cover feature extraction with the use of 33×33 pixel samples. The standard decomposition approach was found to be the most efficient approach in urban texture analysis and classification. For comparison purposes and to better evaluate the accuracy of wavelet approaches in image classification, spatial autocorrelation techniques (Moran's I and Geary's C ) and the spatial co–occurrence matrix method were also examined. The results suggest that the wavelet transform approach is superior to all other approaches.     

基于GEE的地表覆盖与气候状态关联研究

为探究地表覆盖与气候状态间的关联性,本文选取2019年的Landsat影像数据,结合温度、降水量、PM_2.5浓度3种气候指标,利用GEE平台,结合NDVI、MNDWI、NDBI,采用SVM、RF、CART方法进行地表覆盖分类,探究气候指标与地表覆盖类型分布的关联性;提出了使用3种气候指标构建分类特征进行地表覆盖分类的方法,并通过消融试验分析了气候指标对地表覆盖分类精度的影响。结果表明:①RF有较好的分类结果,总体精度为96.0%;②3种气候指标均能提高地表覆盖分类精度,其中PM_2.5浓度效果最好;③温度与植被、水体关联性较大,PM_2.5浓度与城区、植被关联性较大,降水量与耕地关联性较大。     

土地覆盖制图：基于最优化遥感数据的支撑向量机分类

遥感数据具有在不同空间、光谱和时间尺度上获取地表测量信息的能力,使其成为获取土地覆盖信息的一个主要数据源。影像分类即把卫星影像上的相关像元划分给某类已知的土地覆盖类型的过程。支撑向量机（SVMs）是一种土地覆盖分类的新技术。三种常用的SVMs是：基于线性和多项式的SVM以及具有高斯核函数的SVM分类器,分类能否成功地应用有赖于其各自选择的最佳参数。但是海量的遥感数据使得这些参数的确定速度十分缓慢。本文研究了一种新的基于最优化遥感数据压缩技术的SVM分类方法。研究显示用于获取SVM参数的数据量能够在不影响土地覆盖的分类精度的前提下进行压缩。数据压缩成功的应用于多项式和高斯核函数的SVM分类,而线性SVM的分类精度却非常低。     

A comparative assessment between object and pixel-based classification approaches for land use/land cover mapping using SPOT 5 imagery

Abstract

Land use/land cover (LULC) classification with high accuracy is necessary, especially in eco-environment research, urban planning, vegetation condition study and soil management. Over the last decade a number of classification algorithms have been developed for the analysis of remotely sensed data. The most notable algorithms are the object-oriented K-Nearest Neighbour (K-NN), Support Vector Machines (SVMs) and the Decision Trees (DTs) amongst many others. In this study, LULC types of Selangor area were analyzed on the basis of the classification results acquired using the pixel-based and object-based image analysis approaches. SPOT 5 satellite images with four spectral bands from 2003 and 2010 were used to carry out the image classification and ground truth data were collected from Google Earth and field trips. In pixel-based image analysis, a supervised classification was performed using the DT classifier. On the other hand, object-oriented (K-NN) image analysis was evaluated using standard nearest neighbour as classifier. Subsequently SVM object-based classification was performed. Five LULC categories were extracted and the results were compared between them. The overall classification accuracies for 2003 and 2010 showed that the object-oriented (K-NN) (90.5% and 91%) performed better results than the pixel-based DT (68.6% and 68.4%) and object-based SVM (80.6% and 78.15%). In general, the object-oriented (K-NN) performed better than both DTs and SVMs. The obtained LULC classification maps can be used to improve various applications such as change detection, urban design, environmental management and zooning.     

Vegetation cover type mapping in mouling national park in Arunachal Pradesh, Eastern Himalayas- an integrated geospatial approach

Improving image classification and its techniques have been of interest while handling satellite data especially in hilly regions with evergreen forests particularly with indistinct ecotones. In the present study an attempt has been made to classify evergreen forests/vegetation in Moulirig National Park of Arunachal Pradesh in Eastern Himalayas using conventional unsupervised classification algorithms in conjunction with DEM. The study area represents climax vegetation and can be broadly classified into tropical, subtropical, temperate and sub-alpine forests. Vegetation pattern in the study area is influenced strongly by altitude, slope, aspect and other climatic factors. The forests are mature, undisturbed and intermixed with close canopy. Rugged terrain and elevation also affect the reflectance. Because of these discrimination among the various forest/vegetation types is restrained on satellite data. Therefore, satellite data in optical region have limitations in pattern recognition due to similarity in spectral response caused by several factors. Since vegetation is controlled by elevation among other factors, digital elevation model (DEM) was integrated with the LISS III multiband data. The overall accuracy improved from 40.81 to 83.67%. Maximum-forested area (252.80 km2) in national park is covered by sub-tropical evergreen forest followed by temperate broad-leaved forest (147.09 km²). This is probably first attempt where detailed survey of remote and inhospitable areas of Semang sub-watershed, in and around western part of Mouling Peak and adjacent areas above Bomdo-Egum and Ramsingh from eastern and southern side have been accessed for detailed ground truth collection for vegetation mapping (on 1:50,000 scale) and characterization. The occurrence of temperate conifer forests and Rhododendron Scrub in this region is reported here for the first time. The approach of DEM integrated with satellite data can be useful for vegetation and land cover mapping in rugged terrains like in Himalayas.     

Fusion of RADARSAT-2 and multispectral optical remote sensing data for LULC extraction in a tropical agricultural area

In this study, we investigated the performance of different fusion and classification techniques for land cover mapping in Hilir Perak, Peninsula Malaysia using RADAR and Landsat-8 images in a predominantly agricultural area. The fusion methods used are Brovey Transform, Wavelet Transform, Ehlers and Layer Stacking and their results classified into seven different land cover classes which include (1) pixel-based classifiers (spectral angle mapper (SAM), maximum likelihood (ML), support vector machine (SVM)) and (2) Object-based (rule-based and standard nearest neighbour (NN)) classifiers. The result shows that pixel-based classification achieved maximum accuracy of the optical data classification using SVM in Landsat-8 with 74.96% accuracy compared to SAM and ML. For multisource data classification, the highest overall accuracy recorded for layer stacking (SVM) was 79.78%, Ehlers fusion (SVM) with 45.57%, Brovey fusion (SVM) with 63.70% and Wavelet fusion (SVM) 61.16%. And for object-based classifiers, the overall classification accuracy is 95.35% for rule-based and 76.33% for NN classifier, respectively. Based on the analysis of their performances, object-based and the rule-based classifiers produced the best classification accuracy from the fused images.     

Landsat 8 vs. Landsat 5: A comparison based on urban and peri-urban land cover mapping

An image dataset from the Landsat OLI spaceborne sensor is compared with the Landsat TM in order to evaluate the excellence of the new imagery in urban landcover classification. Widely known pixel-based and object-based image analysis methods have been implemented in this work like Maximum Likelihood, Support Vector Machine, k-Nearest Neighbor, Feature Analyst and Sub-pixel. Classification results from Landsat OLI provide more accurate results comparing to the Landsat TM. Object-based classifications produced a more uniform result, but suffer from the absorption of small rare classes into large homogenous areas, as a consequence of the segmentation, merging and the spatial parameters in the spatial resolution (30 m) of Landsat images. Based exclusively on the overall accuracy reports, the SVM pixel-based classification from Landsat 8 proved to be the most accurate for the purpose of mapping urban land cover, using medium spatial resolution imagery.     

Support Vector Machine (SVM) Classification: Comparison of Linkage Techniques Using a Clustering-Based Method for Training Data Selection

Support vectors, which usually compose a subset of training sets, determine the decision function of support vector machine (SVM) classification. Selecting a subset including the support vectors through reducing a large training set is a challenge. This paper examines how different linkage techniques in a clustering-based reduction method affect classification accuracy for semiarid vegetation mapping. The investigated linkage techniques include single, complete, weighted pairgroup average, and unweighted pair-group average. Using a multiple-angle remote sensing data set, there is no loss of SVM accuracy when the original training set is reduced to 21%, 14%, 20%, and 20% for these four linkage techniques, respectively.     

全球地表覆盖制图研究新范式

全球土地覆盖制图在过去的10年中取得重要进展,空间分辨率从300 m增加至30 m,分类详细程度也有所提高,从10余个一级类到包含29类的二级分类体系。然而,利用光学遥感数据在大空间范围制图方面仍有诸多挑战。本文主要介绍在农田、居住区、水体和湿地制图方面的挑战,讨论在使用多时相和多传感器遥感数据上的困难,这将是未来遥感应用的趋势。由于各种地表覆盖数据产品有自己定义的地表覆盖类型体系和处理流程,通过调和以及集成各种全球土地覆盖制图产品能够满足新的应用目的,并且可以最大程度地利用已有的土地覆盖数据。然而,未来全球土地覆盖制图需要能够按照新应用需求动态生成地表覆盖数据产品的能力。过去的研究表明有效地提高局部尺度制图的分类精度,更好的算法、更多种特征变量(新类型的数据或特征)以及更具代表性的训练样本都非常重要。我们却认为特征变量的使用更重要。本文提出了一个全球土地覆盖制图的新范式。在这个新范式中,地表覆盖类型的定义被分解为定性指标的类、定量指标的植被郁闭度和高度。非植被类型通过它们的光谱和纹理信息提取。复合考虑类、郁闭度和高度3种指标来定义和区别包含植被的地表覆盖类型。郁闭度和高度不能在分类算法中提取,需要借助其他直接测量或间接反演方法。新的范式还表明,一个普遍适用的训练样本集有效地提高了在非洲大陆尺度土地覆盖分类。为了确保更加容易地实现从传统的土地覆盖制图到全球土地覆盖制图新范式的转变,建议构建一体化的数据管理和分析系统。通过集成相关的观测数据、样本数据和分析算法,逐步建成全球土地覆盖制图在线系统,构建全球地表覆盖制图门户网站,为数据生产者、数据用户、专业研究人员、决策人员搭建合作互助的平台。     

The Utility of Very High Spatial Resolution Images to Identify Urban Objects

Abstract

Landsat MSS, TM and SPOT XS imageries were used in conjunction with unsupervised, supervised and hybrid classilication techniques to classify land cover types in semi‐arid savannas of Mathison Pastoral Station in the Katherine region of northern Australia. Accuracy assessment was based on field data from 246 ground survey sites over a 745‐km² study area. Of 14 land cover classes identified by traditional mapping means, all combinations of imageries and classification techniques differentiated at least seven land cover types. The overall accuracy for these classifications ranged between 43% and 67%. SPOT XS image delivered the best accuracy followed by TM and MSS; unsupervised classification performed better than supervised and hybrid methods. User's and producer's accuracy of individual land units ranged from 0% to 100%. Riparian woodlands, woodland on limestone slopes, shrubland on clay plains, woodland on limestone plains and shadows were the best‐mapped classes. The land units that were associated with undulating hills were not mapped accurately. However, incorporation of a digital elevation model (DEM) in a GIS improved the overall accuracy. The user's and producer's accuracy of dominant land cover types were also enhanced. The classification results and the efficacy of the techniques at Mathison were similar to those found for a nearby semi‐arid area (Kidman Springs) about 200 km from Mathison. However, the overall accuracy was lower at Mathison than at Kidman Springs. Spectral classification masks were developed from the SPOT XS and TM imageries at Kidman Springs, and were applied to classify SPOT XS and TM imageries at Mathison. Initial results showed that the classification mask could be successfully extrapolated to map dominant land cover types but only with moderate accuracy (50%).     

Evaluation of Landsat TM data for forest cover type and landuse classification in subtropical forests of Kumaon Himalaya (U.P.)

LANDSAT-TM has been evaluated for forest cover type and landuse classification in subtropical forests of Kumaon Himalaya (U.P.) Comparative evaluation of false colour composite generated by using various band combinations has been made. Digital image processing of Landsat-TM data on VIPS-32 RRSSC computer system has been carried out to stratify vegetation types. Conventional band combination in false colour composite is Bands 2, 3 and 4 in Red/Green/Blue sequence of Landsat TM for landuse classification. The present study however suggests that false colour combination using Landsat TM bands viz., 4, 5 and 3 in Red/Green/Blue sequence is the most suitable for visual interpretation of various forest cover types and landuse classes. It is felt that to extract full information from increased spatial and spectral resolution of Landsat TM, it is necessary to process the data digitally to classify land cover features like vegetation. Supervised classification using maximum likelihood algorithm has been attemped to stratify the forest vegetation. Only four bands are sufficient enough to classify vegetaton types. These bands are 2,3,4 and 5. The classification results were smoothed digitaly to increase the readiability of the map. Finally, the classification carred out using digital technique were evaluated using systematic sampling design. It is observed that forest cover type mapping can be achieved upto 80% overall mapping accuracy. Monospecies stand Chirpine can be mapped in two density classes viz., dense pine (<40%) with more than 90% accuracy. Poor accuracy (66%) was observed while mapping pine medium dense areas. The digital smoothening reduced the overall mapping accuracy. Conclusively, Landsat-TM can be used as operatonal sensor for forest cover type mapping even in complex landuse-terrain of Kumaon Himalaya (U.P.)     

Improved coastal wetland mapping using very-high 2-meter spatial resolution imagery

Accurate wetland maps are a fundamental requirement for land use management and for wetland restoration planning. Several wetland map products are available today; most of them based on remote sensing images, but their different data sources and mapping methods lead to substantially different estimations of wetland location and extent. We used two very high-resolution (2 m) WorldView-2 satellite images and one (30 m) Landsat 8 Operational Land Imager (OLI) image to assess wetland coverage in two coastal areas of Tampa Bay (Florida): Fort De Soto State Park and Weedon Island Preserve. An initial unsupervised classification derived from WorldView-2 was more accurate at identifying wetlands based on ground truth data collected in the field than the classification derived from Landsat 8 OLI (82% vs. 46% accuracy). The WorldView-2 data was then used to define the parameters of a simple and efficient decision tree with four nodes for a more exacting classification. The criteria for the decision tree were derived by extracting radiance spectra at 1500 separate pixels from the WorldView-2 data within field-validated regions. Results for both study areas showed high accuracy in both wetland (82% at Fort De Soto State Park, and 94% at Weedon Island Preserve) and non-wetland vegetation classes (90% and 83%, respectively). Historical, published land-use maps overestimate wetland surface cover by factors of 2–10 in the study areas. The proposed methods improve speed and efficiency of wetland map production, allow semi-annual monitoring through repeat satellite passes, and improve the accuracy and precision with which wetlands are identified.     

Learning with transductive SVM for semisupervised pixel classification of remote sensing imagery

Land cover classification using remotely sensed data requires robust classification methods for the accurate mapping of complex land cover area of different categories. In this regard, support vector machines (SVMs) have recently received increasing attention. However, small number of training samples remains a bottleneck to design suitable supervised classifiers. On the other hand, adequate number of unlabeled data is available in remote sensing images which can be employed as additional source of information about margins. To fully leverage all of the precious unlabeled data, integration of filtering in a transductive SVM is proposed.Using two labeled image datasets of small size and two large unlabeled image datasets, the effectiveness of the proposed method is explored. Experimental results show that the proposed technique achieves average overall accuracies of around 4.5–7.8%, 0.8–2.6% and 0.9–2.2% more than the standard inductive SVM (ISVM), progressive transductive SVM (PTSVM) and low density separation (LDS) classifiers, respectively on larger domains in case of labeled datasets. Using image datasets, visual interpretation from the classified images as well as the segmentation quality reveal that the proposed method can efficiently filter informative data from the unlabeled samples.     

Evaluating the Performance of Multi-Class and Single-Class Classification Approaches for Mountain Agriculture Extraction Using Time-Series NDVI

Supervised multi-class classification (MCC) approach is widely being used for regional-level land use–land cover (LULC) mapping and monitoring. However, it becomes inefficient if the end user wants to map only one particular class. Therefore, an improved single-class classification (SCC) approach is required for quick and reliable map production purpose. In this regard, the current study attempts to evaluate the performance of MCC and SCC approaches for extracting mountain agriculture area using time-series normalized differential vegetation index (NDVI). At first, samples of eight LULC classes were acquired using Google Earth image, and corresponding temporal signatures (TS) were extracted from time-series NDVI to perform classification using minimum distance to mean (MDM) and spectral angle mapper (i.e., multi-class SAM—MCSAM) under MCC approach. Secondly, under SCC approach, the TS of three agriculture classes (i.e., agriculture, mixed agriculture and plantation) were utilized as a reference to extract agriculture extent using Euclidean distance (ED) and SAM (i.e., single-class SAM—SCSAM) algorithms. The area of all four maps (i.e., MDM—19.77% of total geographical area (TGA), MCSAM—21.07% of TGA, ED—15.23% of TGA, SCSAM—13.85% of TGA) was compared with reference agriculture area (14.54% of TGA) of global land cover product, and SCC-based maps were found to have close agreement. Also, the class-wise detection accuracy was evaluated using random sample point-based error matrix which reveals the better performance of ED-based map than rest three maps in terms of overall accuracy and kappa coefficient.     

Mapping the Land Cover of Mexico Using AVHRR Time‐Series Data Sets

Abstract

An important methodological and analytical requirement for analyzing spatial relationships between regional habitats and species distributions in Mexico is the development of standard methods for mapping the country's land cover/land use formations. This necessarily involves the use of global data such as that produced by the Advanced Very High Resolution Radiometer (AVHRR). We created a nine‐band time‐series composite image from AVHRR Normalized Difference Vegetation Index (NDVI) bi‐weekly data. Each band represented the maximum NDVI for a particular month of either 1992 or 1993. We carried out a supervised classification approach, using the latest comprehensive land cover/vegetation map created by the Mexican National Institute of Geography (INEGI) as reference data. Training areas for 26 land cover/vegetation types were selected and digitized on the computer's screen by overlaying the INEGI vector coverage on the NDVI image. To obtain specific spectral responses for each vegetation type, as determined by its characteristic phenology and geographic location, the statistics of the spectral signatures were subjected to a cluster analysis. A total of 104 classes distributed among the 26 land cover types were used to perform the classification. Elevation data were used to direct classification output for pine‐oak and coastal vegetation types. The overall correspondence value of the classification proposed in this paper was 54%; however, for main vegetation formations correspondence values were higher (60‐80%). In order to obtain refinements in the proposed classification we recommend further analysis of the signature statistics and adding topographic data into the classification algorithm.     

A Hybrid Approach for Land Use/Land Cover Classification

Atlanta has continuously changed its physical landscape as well as its socioeconomic appearance over the past decades. A hybrid image processing approach, which integrated unsupervised, supervised, and spectral mixture analysis (SMA) classification methods, was used to identify urban land use/land cover changes over a decade (from 1990 to 2000) in the Atlanta metropolitan area. During this process, SMA was proven to be an effective analytical approach for characterizing mixed feature areas, such as a metropolitan area. According to accuracy assessment, the classification results were acceptable.