Standard Nearest-neighbour Research Articles

Change of land use / land cover in kurdistan region of Iraq: A semi-automated object-based approach

During the conflict and in the years afterward, the Kurdistan Region of Iraq (KRI) saw substantial changes in land use. The mapping and monitoring of land use/land cover (LULC) is critical for its long-term development and natural resource management. Therefore in this study, we developed a semi-automated object-based land use/land cover classification to identify and quantify LULC changes and change detection analysis in the Kurdistan Region of Iraq for the period 1990 to 2020 using Landsat satellite data (TM, ETM+, and OLI). To determine the optimum segmentation scales for each phase, we first applied and evaluated different scales of a multi-resolution segmentation technique. After that, spatial (digital elevation information) and spectral information were combined in an object-based image analysis (OBIA) technique. For each LULC class, object features were found. We then used the standard nearest neighbor (SNN) approach to derive their individual properties. Field data and validation units collected from high resolution Google earth pro and historical maps in SAS planet open source were used to conduct accuracy evaluations based on the error matrix and kappa coefficient for each reference year. With overall accuracies ranging from 86.072% to 88.9% and Kappa coefficients of 0.845–0.878, ten LULCs were effectively recorded. After that, a post classification comparison was used to perform a change analysis. The findings demonstrated that LULC change trends were notably different, as all categories were altered at different times throughout the study. Strikingly, 52.1% of the land use/land cover in the Kurdistan Region has changed between 1990 and 2020. All changes corresponded to the KRI's own challenges throughout the last three decades. The OBIA-based approaches and features, we conclude, have a lot of potential for LULC mapping and monitoring. The results shall support state institutions and experts to manage the land in a more controlled way.

An Evaluation of Pixel-based and Object-based Classification Methods for Land Use Land Cover Analysis Using Geoinformatic Techniques

Land use land cover (LULC) classification is a valuable asset for resource managers; in many fields of study, it has become essential to monitor LULC at different scales. As a result, the primary goal of this work is to compare and contrast the performance of pixel-based and object-based categorization algorithms. The supervised maximum likelihood classifier (MLC) technique was employed in pixel-based classification, while multi-resolution segmentation and the standard nearest neighbor (SNN) algorithm were employed in object-based classification. For the urban and suburban parts of Kolhapur, the Resourcesat-2 LISS-IV image was used, and the entire research region was classified into five LULC groups. The performance of the two approaches was examined by comparing the classification results. For accuracy evaluation, the ground truth data was used, and confusion matrixes were generated. The overall accuracy of the object-based methodology was 84.66%, which was significantly greater than the overall accuracy of the pixel-based categorization methodology, which was 72.66%. The findings of this study show that object-based classification is more appropriate for high-resolution Resourcesat-2 satellite data than MLC of pixel-based classification.

Multi-script text versus non-text classification of regions in scene images

Text versus non-text region classification is an essential but difficult step in scene-image analysis due to the considerable shape complexity of text and background patterns. There exists a high probability of confusion between background elements and letter parts. This paper proposes a feature-based classification of image blocks using the color autocorrelation histogram (CAH) and the scale-invariant feature transform (SIFT) algorithm, yielding a combined scale and color-invariant feature suitable for scene-text classification. For the evaluation, features were extracted from different color spaces, applying color-histogram autocorrelation. The color features are adjoined with a SIFT descriptor. Parameter tuning is performed and evaluated. For the classification, a standard nearest-neighbor (1NN) and a support-vector machine (SVM) were compared. The proposed method appears to perform robustly and is especially suitable for Asian scripts such as Kannada and Thai, where urban scene-text fonts are characterized by a high curvature and salient color variations.

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.

An improved distance-based relevance feedback strategy for image retrieval

Most CBIR (content based image retrieval) systems use relevance feedback as a mechanism to improve retrieval results. NN (nearest neighbor) approaches provide an efficient method to compute relevance scores, by using estimated densities of relevant and non-relevant samples in a particular feature space. In this paper, particularities of the CBIR problem are exploited to propose an improved relevance feedback algorithm based on the NN approach. The resulting method has been tested in a number of different situations and compared to the standard NN approach and other existing relevance feedback mechanisms. Experimental results evidence significant improvements in most cases.

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

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.

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.

COEVOLUTION OF NEAREST NEIGHBOR CLASSIFIERS

This paper presents experiments of Nearest Neighbor (NN) classifier design using different evolutionary computation methods. Through multiobjective and coevolution techniques, it combines genetic algorithms and genetic programming to both select NN prototypes and design a neighborhood proximity measure, in order to produce a more efficient and robust classifier. The proposed approach is compared with the standard NN classifier, with and without the use of classic prototype selection methods, and classic data normalization. Results on both synthetic and real data sets show that the proposed methodology performs as well or better than other methods on all tested data sets.

Fusion of point-based postal data with IKONOS imagery

Research in urban remote sensing has been recently reinvigorated by both the continuing fusion with GIS and the advent of high spatial resolution satellite sensor data. Both will be examined by this paper in terms of how GIS data at the point level can assist the identification and interpretation of urban land use patterns from classified land cover. Specifically, how spatial statistics can be used to summarise the two-dimensional patterns of point data representing residential and commercial buildings. In this paper point data refer to the location of postal addresses known as ADDRESS-POINT TM and collected by the Ordnance Survey of Great Britain and COMPAS TM in Northern Ireland. Groups of these postal points are characterised using standard nearest-neighbour and linear nearest-neighbour indices in terms of the spacing and arrangement of residential and commercial buildings. The indices then form the basis for the interpretation of urban pixels classified from IKONOS imagery at the 4 m spatial resolution. In addition, the paper will outline an agenda for constructing an automated pattern recognition system that would ultimately identify and characterise the physical arrangement of buildings in terms of density (compactness versus sparseness) and linearity. Preliminary results so far are most encouraging. Using ground truth from aerial photographs at 15 cm spatial resolution, classified IKONOS imagery representing two cities in the United Kingdom, Bristol and Belfast, have been investigated. In both, spatial patterns have demonstrated the ability to identify misclassified urban pixels and characterise a variety of building arrangements. Also, using the software e-Cognition, a spatial classification based on nearest neighbour contextual rules produced accuracies of 95.4% compared to 90.7% from a multispectral-only classification. Further, more extensive testing is continuing.

Identification and characterisation of urban building patterns using IKONOS imagery and point-based postal data

Point-based data recording the location of individual buildings are not only restoring enthusiasm in urban modelling but are also playing active roles in methodologies endeavouring to interpret urban land use from high spatial resolution remotely sensed images. One such approach explores the possibility of using both individual postal points to identify the spatial location of residential and commercial buildings as well as groups of postal points to characterise neighbourhood patterns from classified imagery. Postal points refer to those collected by the Ordnance Surveys of Great Britain (dataset known as ADDRESS-POINT™) and Northern Ireland (COMPAS™). Groups of these points are characterised using standard nearest-neighbour and linear nearest-neighbour indices in terms of the spacing and arrangement of residential and commercial buildings. The indices then form the basis for the interpretation of urban pixels classified from IKONOS imagery, along with a view to developing an automated pattern recognition system that would ultimately identify and characterise the physical arrangement of buildings in terms of density (compactness versus sparseness) and linearity. Encouraging results are documented from preliminary empirical testing on two cities in the United Kingdom using digital aerial photography at 25 cm spatial resolution.

Nearest neighbor classification from multiple feature subsets

Combining multiple classifiers is an effective technique for improving accuracy. There are many general combining algorithms, such as Bagging, Boosting, or Error Correcting Output Coding, that significantly improve classifiers like decision trees, rule learners, or neural networks. Unfortunately, these combining methods do not improve the nearest neighbor classifier. In this paper, we present MFS, a combining algorithm designed to improve the accuracy of the nearest neighbor (NN) classifier. MFS combines multiple NN classifiers each using only a random subset of features. The experimental results are encouraging: On 25 datasets from the UCI repository, MFS significantly outperformed several standard NN variants and was competitive with boosted decision trees. In additional experiments, we show that MFS is robust to irrelevant features, and is able to reduce both bias and variance components of error.

Nearest neighbor classification from multiple feature subsets

Combining multiple classifiers is an effective technique for improving accuracy. There are many general combining algorithms, such as Bagging, Boosting, or Error Correcting Output Coding, that significantly improve classifiers like decision trees, rule learners, or neural networks. Unfortunately, t hese combining methods do not improve the nearest neighbor classifier. In this paper, we present MFS, a combining algorithm designed to improve the accuracy of the nearest neighbor (NN) classifier. MFS combines multiple NN classifiers each using only a random subset of features. The experimental results are encouraging: On 25 datasets from the UCI repository, MFS significantly outperformed several standard NN variants and was competitive with boosted decision trees. In additional experiments, we show that MFS is robust to irrelevant features, and is able to reduce both bias and variance components of error.

A hierarchical case representation using context guided retrieval

This paper presents a hierarchical case representation using a context guided retrieval method. The performance of this method is compared to that of a simpler flat file representation using standard nearest neighbour retrieval. The estimation of construction costs of light industrial buildings is used as the test domain. Each case in the system has approximately 400 features. These are structured into a hierarchical case representation that holds more general contextual features at its top and specific building elements at its leaves. Problems are decomposed into sub-problems and solutions recomposed into a final solution. Comparative results show that the context guided retrieval method using the hierarchical case representation is significantly more accurate than the simpler flat file representation using standard nearest neighbour retrieval.

Nearest-Neighbor Analysis of Spatial Point Patterns: Application to Biomedical Image Interpretation

Analysis of the spatial distributions of objects is fundamental to biomedical image interpretation. Nearest-neighbor (NN) methods are generally used to assess whether objects are arranged at random or in a deterministic manner. Simple standard NN techniques, however, may fail to identify complex spatial organizations. To overcome this problem the present study proposes a NN iterative algorithm that enables deterministic spatial patterns to be detected by identifying the distances between objects for which there is the greatest deviation from randomness and hence the amplitude of the areas of maximum reciprocal influence between objects. The performance of the algorithm is evaluated by applying it to both manufactured and experimental data. The manufactured data example showed that the proposed procedure produced neither false positives or negatives. The method proved to be extremely sensitive, detecting even small deviations from randomness. The experimental analysis was applied to the study of the spatial distribution of apoptotic structures in malignant neoplastic tissue. It showed that the apoptotic cells and bodies are characterized by a complex spatial pattern, and aggregate closely.