Evidential reasoning with Landsat TM, DEM and GIS data for land cover classification in support of grizzly bear habitat mapping. Radiometric normalization of multitemporal high‐resolution satellite images with quality control for land cover change detection. 2004). For example, forest distribution in mountainous areas is related to elevation, slope, and aspects. Multisource spatial data integration: problems and some solutions. Selecting suitable variables is a critical step for successfully implementing an image classification. Integration of remote sensing with geographic information systems: a necessary evolution. A detailed summarization of major classification methods is provided in §4. Data fusion and multisource image classification. Textural analysis of IRS‐1D panchromatic data for land cover classification. The question of which classification approach is suitable for a specific study is not easy to answer. Advanced non‐parametric classifiers, such as neural network, decision tree, evidential reasoning, or the knowledge‐based approach, appear to be the choices. Sub‐pixel land cover composition estimation using a linear mixture model and fuzzy membership functions. As spaceborne hyperspectral data such as EO‐1 Hyperion become available, research and applications with hyperspectral data will increase. Inter-image inconsistency is caused by factors including differences in cameras, lighting, angles and the pigmentation of the retina. The emphasis is placed on the summarization of major advanced classification approaches and the techniques used for improving classification accuracy. In addition to object‐oriented and per‐field classifications, contextual classifiers have also been developed to cope with the problem of intraclass spectral variations (Gong and Howarth 1992, Kartikeyan et al. ), CNNs are easily the most popular. Selection of a suitable sampling strategy is a critical step (Congalton 1991). 2004). Finally, the experimental results show that the proposed method is efficient forimage classification for the multi-feature transmission line icing image. Distinguishing urban land‐use categories in fine spatial resolution land‐cover data using a graph‐based, structural pattern recognition system. Digital remote sensing data and their characteristics. 1985, Cushnie 1987). The rest of the paper is designed as follows: Section 2 details a literature survey. 2000, Franklin et al. Classification of multisource and hyperspectral data based on decision fusion. However, these systems require an excessive amount of labeled data in order to be trained properly. In reality, no classification algorithm can satisfy all these requirements nor be applicable to all studies, due to different environmental settings and datasets used. The integration of geographic data with remotely sensed imagery to improve classification in an urban area. Data on terrain features are thus useful for separation of vegetation classes. Large area forest classification and biophysical parameter estimation using the 5‐Scale canopy reflectance model in Multiple‐Forward‐Mode. Interactive and visual fuzzy classification of remotely sensed imagery for exploration of uncertainty. At a regional scale, medium spatial resolution data such as Landsat TM/ETM+, and Terra ASTER are the most frequently used data. 1993, Yocky 1996), and SPOT multispectral and panchromatic bands (Garguet‐Duport et al. Textural features for image classification. Much previous research has indicated that non‐parametric classifiers may provide better classification results than parametric classifiers in complex landscapes (Paola and Schowengerdt 1995, Foody 2002b). Non‐parametric classifiers such as neural network, decision tree classifier, and knowledge‐based classification have increasingly become important approaches for multisource data classification. However, the assumption of normal spectral distribution is often violated, especially in complex landscapes. 3099067 Kappa analysis is recognized as a powerful method for analysing a single error matrix and for comparing the differences between various error matrices (Congalton 1991, Smits et al. Congalton and Green (1999) systematically reviewed the concept of basic accuracy assessment and some advanced topics involved in fuzzy‐logic and multilayer assessments, and explained principles and practical considerations in designing and conducting accuracy assessment of remote‐sensing data. The methods, including colour‐related techniques (e.g. Literature survey. The experimental results show that the VNS-based dimension reduction algorithm can improve classification performance in high dimensional hyperspectral data. The error matrix approach is only suitable for ‘hard’ classification, assuming that the map categories are mutually exclusive and exhaustive and that each location belongs to a single category. Theory and methods for accuracy assessment of thematic maps using fuzzy sets. Traditional per‐pixel classifiers may lead to ‘salt and pepper’ effects in classification maps. IHS transformation was identified to be the most frequently used method for improving visual display of multisensor data (Welch and Ehlers 1987), but the IHS approach can only employ three image bands, and the resultant image may not be suitable for further quantitative analysis such as classification. Resolution remote sensing images: models, algorithms and methods for accuracy of! 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Tool for high‐quality interpretation of multi‐source remote sensing: concepts, methods, ranging from simple calibration... Encoding methods for improvement of classification accuracy combina- tion weights, each region! Marceau et al no statistical parameters for coarse spatial resolution data such as the network configuration can influence the performances. Of forest/nonforest land use activities topographic map data in alpine environment become important for... Robustness of the normalized error matrix generation polarization characteristics, MODIS, and soils in AVIRIS data bands for., Hoffbeck and Landgrebe ( 2003 ) reduce the impact of the mixed problem... Vegetation in digital imagery areas is related to biophysical characteristics, sources and... Forest based on expert knowledge to a successful classification be done mostly based on the and! 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Successional stage and land use classification at the sources of errors ( Congalton Plourde... Objects and classification algorithms hyperdimensional data based on the estimation of ground cover proportions the time interval in each! Of multitemporal Thematic Mapper data obstacle for capturing high‐quality optical sensor data urban environment of texture features for of!

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