The effects of spatial resolution on the classification of Thematic Mapper data. 1997, Garcia‐Haro et al. Many texture measures have been developed (Haralick et al. Spatial metrics and image texture for mapping urban land use. 2004). The signatures generated from the training samples are then used to train the classifier to classify the spectral data into a thematic map. A comparison of methods for multi‐class support vector machines. A comparison of contextual classification methods using Landsat TM. Fully‐fuzzy supervised classification of sub‐urban land cover from remotely sensed imagery: statistical neural network approaches. In this literature survey, we have briefly introduced a number of typical DL models that may be used to perform RS image classification, including: CNNs, SAEs and DBNs. No GIS vector data are used. Uncertainty and error propagation in the image‐processing chain is an important factor influencing classification accuracy. 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. average divergence, transformed divergence, Bhattacharyya distance, Jeffreys–Matusita distance) have been used to identify an optimal subset of bands (Jensen 1996). Multi‐source image classification II: an empirical comparison of evidential reasoning and neural network approaches. Effectively using these relationships in a classification procedure has proven effective in improving classification accuracy. Classification of remotely sensed data by an artificial neural network: issues related to training data characteristics. Moshi Shibie yu Rengong Zhineng/Pattern Recognition and Artificial Intelligence. Meanwhile, many authors, such as Congalton (1991), Janssen and van der Wel (1994), Smits et al. Textural and contextual land‐cover classification using single and multiple classifier systems. The training process means, For a specific study, it is often difficult to identify a suitable texture because texture varies with the characteristics of the landscape under investigation and the image data used. The Kappa coefficient is a measure of overall statistical agreement of an error matrix, which takes non‐diagonal elements into account. The radiometric normalization of multitemporal Thematic Mapper imagery of the midlands of Ireland—a case study. Bolstad and Lillesand (1992) found that a rule‐based classification with Landsat TM, soil, and terrain data yielded higher land‐cover classification accuracy than a standard spectral‐based classification. In contrast, when image data are anomalously distributed, neural network and decision tree classifiers may demonstrate a better classification result (Pal and Mather 2003, Lu et al. Combining non‐parametric models for multisource predictive forest mapping. LITERATURE SURVEY Andre Esteva, et. Sasi Kiran1, N. Vijaya Kumar 2, N. Sashi Prabha 3, M. Kavya4 Department of Computer Science and Engineering Vidya Vikas Institute of Technology, Chevella, R.R. A comparative study of different classifiers is often conducted to find the best classification result for a specific study (Zhuang et al. They used a GoogleNet Inception v3 CNN architecture that was pretrained on approximately 1.28 Rotational transformation of remotely sensed data for land use classification. Classification approaches may vary with different types of remote‐sensing data. Status of land cover classification accuracy assessment. Comparison of IKONOS and QuickBird images for mapping mangrove species on the Caribbean coast of panama. With non‐parametric classifiers, the assumption of a normal distribution of the dataset is not required. An iterative classification approach for mapping natural resources from satellite imagery. 1982, Leprieur et al. Landsat TM‐based forest damage assessment: correction for topographic effects. In many cases, contextual‐based classifiers, per‐field approaches, and machine‐learning approaches provide a better classification result than MLC, although some tradeoffs exist in classification accuracy, time consumption, and computing resources. Those four different categories are Pre-processing, Segmentation, Optimization, and feature extraction. This literature review suggests that designing a suitable image‐processing procedure is a prerequisite for a successful classification of remotely sensed data into a thematic map. Thematic map comparison: evaluating the statistical significance of differences in classification accuracy. Two types of classification are supervised classification and unsupervised classification. Integrated analysis of spatial data from multiple sources: an overview. Previous research indicated that integration of Landsat TM and radar (Ban 2003, Haack et al. Spectral features are the most important information for image classification. A multi‐spectral classification algorithm for classifying parcels in an agricultural region. 1994, Augusteijn et al. Decision tree classification of land cover from remotely sensed data. Combining multiple classifiers: an application using spatial and remotely sensed information for land cover type mapping. Monitoring the composition of urban environments based on the vegetation‐impervious surface‐soil (VIS) model by subpixel analysis techniques. bar graph spectral plots, co‐spectral mean vector plots, two‐dimensional feature space plot, and ellipse plots) and statistical methods (e.g. The use of different seasons of remotely sensed data has proven useful for improving classification accuracy, especially for crop and vegetation classification (Brisco and Brown 1995, Wolter et al. At a continental or global scale, coarse spatial resolution data such as AVHRR, MODIS, and SPOT Vegetation are preferable. Whether parameters such as mean vector and covariance matrix are used or not. Urban built‐up land change detection with road density and spectral information from multitemporal Landsat TM data. Spatial resolution determines the level of spatial detail that can be observed on the Earth's surface. Medium spatial resolution data such as Landsat TM/ETM+ or coarse spatial resolution data such as AVHRR and MODIS are attributed to the L‐resolution model. This paper provides an overview of existing literature on vessel/ship detection and classification from optical satellite imagery. 1973, Kashyap et al. The number of spectral bands used for image classification can range from a limited number of multispectral bands (e.g. 2003). A major advantage of these fine spatial resolution images is that such data greatly reduce the mixed‐pixel problem, providing a greater potential to extract much more detailed information on land‐cover structures than medium or coarse spatial resolution data. 2004) and a support vector machine (Kim et al. 2001, Magnussen et al. Mapping deciduous forest ice storm damage using Landsat and environmental data. Integrating contextual information with per‐pixel classification for improved land cover classification. 1999a, Stuckens et al. Optimum band selection for supervised classification of multispectral data. In this paper, a CNN system embedded with an extracted hashing feature is proposed for HSI classification that utilizes the semantic information of … 2002, Zhang et al. Cihlar (2000) discussed the status and research priorities of land‐cover mapping for large areas. Two stages are involved in an object‐oriented classification: image segmentation and classification. Remotely sensed data are acquired in raster format, which represents regularly shaped patches of the Earth's surface, while most GIS data are stored in vector format, representing geographical objects with points, lines and polygons. The recognition rate improves from 97.7% in binary system to 99.9% in gray-level with modified N-best search, over a testing set with similar blur and noise condition as the training set. An integrated approach to land cover classification: an example in the Island of Jersey. Choice of a classification algorithm is generally based upon a number of factors, among which are availability of software, ease of use, and performance, measured here by overall classification accuracy. Object‐based classification of remote sensing data for change detection. 1999), and decision (Benediktsson and Kanellopoulos 1999). A comparative study of some non‐parametric spectral classifiers: application to problems with high‐overlapping training sets. For the sake of convenience, this paper groups classification approaches as per‐pixel, subpixel, per‐field, contextual‐based, knowledge‐based, and a combination of multiple classifiers. 2004b). 2000, Hubert‐Moy et al. However, the assumption of normal spectral distribution is often violated, especially in complex landscapes. An object‐specific image‐texture analysis of H‐resolution forest imagery. Vegetation in Deserts: I. A framework for selecting appropriate remotely sensed data dimensions for environmental monitoring and management. 2004). A Literature Survey on Digital Image Processing Techniques in Character Recognition of Indian Languages Dr. Jangala. The success of an image classification depends on many factors. However, a gap in performance has been brought by using neural networks. GIS plays a critical role in handling multisource data. 9906826). Classification of SPOT HRV imagery and texture features. 1982, He and Wang 1990, Unser 1995, Emerson et al. In the next section this paper tries to present those proposed systems in meaningful way. Multidate SAR/TM synergism for crop classification in western Canada. Use of probability entropy for the estimation and graphical representation of the accuracy of maximum likelihood classifications. Sources of error in accuracy assessment of thematic land‐cover maps in the Brazilian Amazon. Different approaches, such as evidential reasoning classification (Peddle et al. AVIRIS and EO‐1 Hyperion images with 224 bands). Improved forest classification in the northern lake states using multi‐temporal Landsat imagery. Although many classification approaches have been developed, which approach is suitable for features of interest in a given study area is not fully understood. Fuzzy ARTMAP supervised classification of multi‐spectral remotely‐sensed images. 2000, Wu and Linders 2000). Mixed pixels are common in these data. Spectral analysis for earth science: investigations using remote sensing data. The resulting signature contains the contributions of all materials present in the training‐set pixels, ignoring the mixed pixel problems. Uncertainty research in GIS has made good progress in the past decade, but in remote sensing, it had not obtained sufficient attention until recent years (Mowrer and Congalton 2000, Hunsaker et al. Multisource classification of complex rural areas by statistical and neural‐network approaches. A critical step is to develop suitable rules to combine the classification results from different classifiers. Due to the heterogeneity of landscapes and the limitation in spatial resolution of remote‐sensing imagery, mixed pixels are common in medium and coarse spatial resolution data. SPOT panchromatic band) and multispectral data (e.g. To learn about our use of cookies and how you can manage your cookie settings, please see our Cookie Policy. As spatial resolution increases, texture or context information becomes another important attribute to be considered. Maximum likelihood, minimum distance, artificial neural network, decision tree classifier. Using shade fraction image segmentation to evaluate deforestation in Landsat Thematic Mapper images of the Amazon region. Classification using ASTER data and SVM algorithms: the case study of Beer Sheva, Israel. In practice, the spatial resolution of the remotely sensed data, use of ancillary data, the classification system, the available software, and the analyst's experience may all affect the decision of selecting a classifier. An assessment of the effectiveness of decision tree methods for land cover classification. arithmetic combination, principal component analysis, high pass filtering, regression variable substitution, canonical variable substitution, component substitution, and wavelets), and various combinations of these methods were examined. Peddle and Ferguson (2002) examined three approaches (exhaustive search by recursion, isolated independent search, and sequential dependent search) for optimizing the selection of multisource data, and found that these approaches were applicable to a variety of data analyses. Merging of IRS LISS III and PAN data—evaluation of various methods for a predominantly agricultural area. Remotely sensed data have their own limitations. For a much more degraded testing set, it improves from 89.59% to 98.51%. The resulting signature contains the contributions of all materials present in the training pixels, but ignores the impact of the mixed pixels. A supervised contextual classifier based on a region‐growth algorithm. Experimental results show that the new system has significantly improved the performance when compared to a similar system using threshold binary images as inputs. Uncertainty may be modelled or quantified in different ways such as fuzzy and probabilistic classification techniques, or via visualization (van der Wel et al. A comparison of spatial feature extraction algorithms for land‐use classification with SPOT HRV data. Similarly, incorporating ancillary data in a classification procedure is an effective way to improve classification accuracy. Tau coefficients for accuracy assessment of classification of remote sensing data. 1998a). Literature Survey There are a lot of researches in the way of visual features extraction: for example texture It can be applied to encrypt sensor data, image, sensitive bio-medical information, etc. However, the variation in the dimensionality of a dataset and the characteristics of training and testing sets may lessen the accuracy of image classification (Foody and Arora 1997). Crisp and fuzzy competitive learning networks for supervised classification of multispectral IRS scenes. Identifying the weakest links in the chain and then reducing the uncertainties are critical for improvement of classification accuracy. For example, with high spatial resolution data such as IKONOS and SPOT 5 HRG, the severe impact of the shadow problem resulting from topography and vegetation stand structures and the wide spectral variation within the land‐cover classes may outweigh the advantages from high spatial resolution if a per‐pixel, spectral‐based classification is used for these image classifications. Evaluation of classification results is an important process in the classification procedure. nonlinearity, randomness, balancedness etc.). Radiometric corrections of topographically induced effects on Landsat TM data in alpine environment. Data fusion or integration of multisensor or multiresolution data takes advantage of the strengths of distinct image data for improvement of visual interpretation and quantitative analysis. Similarly, recreational grass is often found in residential areas, but pasture and crops are largely located away from residential areas, with sparse houses and a low population density. 1994, Flygare 1997, Sharma and Sarkar 1998, Keuchel et al. colour composite, intensity‐hue‐saturation or IHS, and luminance‐chrominance), statistical/numerical methods (e.g. 2002, Lloyd et al. Approaches to fractional land cover and continuous field mapping: a comparative assessment over the BOREAS study region. Knowledge‐based techniques for multisource classification. 1998b, Rashed et al. Use of the average mutual information index in evaluating classification error and consistency. For example, Lunetta and Balogh (1999) compared single‐ and two‐date Landsat 5 TM images (spring leaf‐on and fall leaf‐off images) for a wetland mapping in Maryland, USA and Delaware, USA and found that multitemporal images provided better classification accuracies than single‐date imagery alone. Linear mixing and the estimation of ground cover proportions. 1999, Foody 2004a). However, some new problems associated with fine spatial resolution image data emerge, notably the shadows caused by topography, tall buildings, or trees, and the high spectral variation within the same land‐cover class. Successful classification of images results in filtering out irrelevant images which improves the performance of such systems. 2001, Lucieer and Kraak 2004). Image classification is a complex process that may be affected by many factors. Deep Learning - A Literature survey 1. 1993, Roberts et al. This paper examines current practices, problems, and prospects of image classification. Topographic normalization in rugged terrain. Mapping montane tropical forest successional stage and land use with multi‐date Landsat imagery. Geographical information systems (GIS) provide a means for implementing per‐field classification through integration of vector and raster data (Harris and Ventura 1995, Janssen and Molenaar 1995, Dean and Smith 2003). 6. 1993, Yocky 1996), and SPOT multispectral and panchromatic bands (Garguet‐Duport et al. It is necessary for future research to develop guidelines on the applicability and capability of major classification algorithms. Integration of remote sensing, geographical information systems (GIS), and expert system emerges as a new research frontier. 1998). The authors wish to acknowledge the support from the Center for the Study of Institutions, Population, and Environmental Change (CIPEC) at Indiana University, through funding from the National Science Foundation (grant NSF SBR no. Making full use of these characteristics is an effective way to improve classification accuracy. experimental results on Caltech-101 and 7-classes image dataset demonstrate that the classification accuracy improves about 10% by the proposed method. Recommended articles lists articles that we recommend and is powered by our AI driven recommendation engine. 2004). In order to have better image classification a suitable RS data needs to be collected, which depends upon strength and weakness of generally may be based on single pixel, seed or sensor data. A modified contextual classification technique for remote sensing data. Due to the complexity of biophysical environments, spectral confusion is common among land‐cover classes. On the other hand, the complexity of forest stand structure and associated canopy shadows may lead to DN saturation, especially in optical‐sensed data (Steininger 2000, Lu et al. An overview of uncertainty in optical remotely sensed data for ecological applications. 2003) to enhance classifications. This is not always feasible due to several factors, such as expensiveness of labeling process or difficulty of correctly classifying data even for the experts. 2004) and influences the selection of classification approaches (Atkinson and Curran 1997, Atkinson and Aplin 2004). Non‐parametric classifiers do not employ statistical parameters to calculate class separation and are especially suitable for incorporation of non‐remote‐sensing data into a classification procedure. A subpixel classifier for urban land‐cover mapping based on a maximum‐likelihood approach and expert system rules. 2004). Effective use of multiple features of remotely sensed data and the selection of a suitable classification method are especially significant for improving classification accuracy. 2003) and is especially important for improving area estimation of land‐cover classes based on coarse spatial resolution data. Automated derivation of geographic window sizes for remote sensing digital image texture analysis. 1999a,b, Aplin and Atkinson 2001, Dean and Smith 2003, Lloyd et al. Classification of remotely sensed imagery using stochastic gradient boosting as a refinement of classification tree analysis. Variance estimates and confidence intervals for the Kappa measure of classification accuracy. Large area forest classification and biophysical parameter estimation using the 5‐Scale canopy reflectance model in Multiple‐Forward‐Mode. Evaluation of speckle filtering and texture analysis methods for land cover classification from SAR images. Different collection strategies, such as single pixel, seed, and polygon, may be used, but they would influence classification results, especially for classifications with fine spatial resolution image data (Chen and Stow 2002). One major drawback of subpixel classification lies in the difficulty in assessing accuracy, as discussed in §3. The semivariogram in comparison to the co‐occurrence matrix for classification of image texture. Image classification is a complex process that may be affected by many factors. However, difficulties still exist in data integration due to the differences in data structures, data types, spatial resolution, geometric characteristics, and the levels of generation (Wang and Howarth 1994). Another major drawback of the parametric classifiers lies in the difficulty of integrating spectral data with ancillary data. A sufficient number of training samples and their representativeness are critical for image classifications (Hubert‐Moy et al. Per‐field classification of land use using the forthcoming very fine spatial resolution satellite sensors: problems and potential solutions. In this literature survey, we have briefly introduced a number of typical DL models that may be used to perform RS image classification, including: CNNs, SAEs and DBNs. 2002, Podest and Saatchi 2002, Narasimha Rao et al. Mapping vegetation in a heterogeneous mountain rangeland using Landsat data: an alternative method to define and classify land‐cover units. Although much previous research and some books are specifically concerned with image classification (Tso and Mather 2001, Landgrebe 2003), a comprehensive up‐to‐date review of classification approaches and techniques is not available. Franklin and Peddle (1990) found that textures based on a grey‐level co‐occurrence matrix (GLCM) and spectral features of a SPOT HRV image improved the overall classification accuracy. Document image classification is an important step in Office Automation, Digital Libraries, and other document image analysis applications. Application of multi‐temporal Landsat 5 TM imagery for wetland identification. The difficulty in identifying suitable textures and the computation cost for calculating textures limit the extensive use of textures in image classification, especially in a large area. 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. Constructing support vector machine ensemble. When multisource data are used in a classification, parametric classification algorithms such as MLC are typically not appropriate. Airborne P‐band SAR applied to the aboveground biomass studies in the Brazilian tropical rainforest. A practical look at the sources of confusion in error matrix generation. 1994, Chavez 1996, Stefan and Itten 1997, Vermote et al. Extraction of endmembers from spectral mixtures. Evaluation of contextual, per‐pixel and mixed classification procedures applied to a subtropical landscape. These convolutional neural network models are ubiquitous in the image data space. However, the scarce availability of human experts and the fatigue and rough estimate procedures involved with them limit the … 2001, Dungan 2002). Comparison of algorithms for classifying Swedish land cover using Landsat TM and ERS‐1 SAR data. Previous research has indicated that post‐classification processing is an important step in improving the quality of classifications (Harris and Ventura 1995, Murai and Omatu 1997, Stefanov et al. Geometric processing of remote sensing images: models, algorithms and methods. For vegetation classification in mountainous areas, the integration of DEM‐related data and remotely sensed data has been proven effective for improving classification accuracy (Senoo et al. A review and analysis of back propagation neural networks for classification of remotely sensed multispectral imagery. GIS plays an important role in developing knowledge‐based classification approaches because of its capability of managing different sources of data and spatial modelling.

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