Iterative Self-Organizing Data Analysis Technique Algorithm Research Articles

UAV-based seagrass wrack orthophotos classification for estimating blue carbon

Seagrass wrack, the detritus from seagrass meadows that accumulates along the coast, is thought to contribute to carbon sequestration in shallow coastal areas when processed into fixed biochar. Accurate and efficient estimation methods for seagrass wrack area size and carbon content are still being explored, especially across multiple seasons. The carbon content of seagrass wrack along the Futtsu coast of Tokyo Bay, Japan, was evaluated using unmanned aerial vehicle (drone) photogrammetry and in-situ measurements of volume and density. A combination of a low-pass filter in pre-processing and the unsupervised iterative self-organizing data analysis technique algorithm (ISODATA) classification method achieved the best classification accuracy when compared with a majority filter and/or any of the three conventional supervised classifiers (support vector machine, random forest, and maximum likelihood classifier). This approach minimizes the manual labor required for determining the area size of the seagrass wrack. The Futtsu tidal flat seagrass wrack carbon for 2020 was estimated to be 3 tons C/yr, accounting for approximately 2% of the seagrass meadow net primary production. These results can greatly assist blue carbon policy implementation.

Analysis of Spatial and Temporal Variation in Water Coverage in the Sub-Lakes of Poyang Lake Based on Multi-Source Remote Sensing

As the largest freshwater lake in China, Poyang Lake is an internationally important wetland and the largest migratory bird habitat in Asia. Many sub-lakes distributed in the lake basin are seasonal lakes, which have a significant impact on hydro-ecological processes and are susceptible to various changes. In this study, using multi-source remote sensing data, a continuous time-series construction method of water coverage suitable in Poyang Lake was developed. That method combined the downscaling of the MNDWI (modified normalized difference water index) with the ISODATA (iterative self-organizing data analysis technique algorithm), and its accuracy can be up to 97% in the months when Landsat 8 is available or 87% when it is unavailable. Based on that method, the increasing variation in water coverage was observed in the sub-lakes of Poyang Lake during 2013–2020 to be within a range of 200–690 km2 normally. The center of the sub-lakes always remained inundated (>80% inundation frequency), while the surrounding areas were probably kept dry for seven months (except for June to September). The dominant influencing factors of water coverage variations were different in different hydrological periods (wet season and dry–wet season: discharge; dry season: temperature and wind speed; wet–dry season: temperature and precipitation). In addition, “returning farmland to lakes” affected the increase in the water area in the sub-lakes. This study is helpful for the management of water resources and the protection of migratory birds in the Poyang Lake region.

Sustainability of Boro rice cultivation in the canal irrigated command area of India

Abstract A comprehensive assessment and monitoring of rice intensification in canal irrigated command areas and its consequences on groundwater dynamics have enormous importance for the water–food–energy nexus. This study analyzed the spatiotemporal transition of Boro rice cultivation, canal irrigation practices during the cultivation season, and its effect on groundwater of a pioneering multipurpose water resource project in India. The rice maps from 1989 to 2018 were generated from Landsat and Sentinel-2A images using the Iterative Self-Organizing Data Analysis Technique Algorithm (ISODATA), the normalized difference vegetation index (NDVI), and the enhanced vegetation index (EVI) to understand the historical distribution of rice cultivation. The ISODATA clustering algorithm performed well compared to the NDVI and the EVI with medium-resolution satellite images. The impact of Boro rice cultivation on groundwater resources was investigated by considering GLDAS NOAH model-based crop evapotranspiration, rainfall, and canal water supply. The area under rice cultivation increased from 33 to 66% during 1989–2018 due to its higher stress tolerance ability and productivity. However, during the cultivation period, the canal water supply was limited to the upstream and middle section of the command area, irrigating only 19% of the rice-cultivated area on average. The unavailability of the canal water supply during the cultivation season has led to 31 m of groundwater level depletion, which, if unchecked, may lead to overexploitation of groundwater in the middle and lower sections of the command area. The results of this study can be used to identify vulnerable areas that require urgent attention and accurate implementation of any agricultural or hydrological policy.

Banana Fusarium Wilt Disease Detection by Supervised and Unsupervised Methods from UAV-Based Multispectral Imagery

Banana Fusarium wilt (BFW) is a devastating disease with no effective cure methods. Timely and effective detection of the disease and evaluation of its spreading trend will help farmers in making right decisions on plantation management. The main purpose of this study was to find the spectral features of the BFW-infected canopy and build the optimal BFW classification models for different stages of infection. A RedEdge-MX camera mounted on an unmanned aerial vehicle (UAV) was used to collect multispectral images of a banana plantation infected with BFW in July and August 2020. Three types of spectral features were used as the inputs of classification models, including three-visible-band images, five-multispectral-band images, and vegetation indices (VIs). Four supervised methods including Support Vector Machine (SVM), Random Forest (RF), Back Propagation Neural Networks (BPNN) and Logistic Regression (LR), and two unsupervised methods including Hotspot Analysis (HA) and Iterative Self-Organizing Data Analysis Technique Algorithm (ISODATA) were adopted to detect the BFW-infected canopies. Comparing to the healthy canopies, the BFW-infected canopies had higher reflectance in the visible region, but lower reflectance in the NIR region. The classification results showed that most of the supervised and unsupervised methods reached excellent accuracies. Among all the supervised methods, RF based on the five-multispectral-band was considered as the optimal model, with higher overall accuracy (OA) of 97.28% and faster running time of 22 min. For the unsupervised methods, HA reached high and balanced OAs of more than 95% based on the selected VIs derived from the red and NIR band, especially for WDRVI, NDVI, and TDVI. By comprehensively evaluating the classification results of different metrics, the unsupervised method HA was recommended for BFW recognition, especially in the late stage of infection; the supervised method RF was recommended in the early stage of infection to reach a slightly higher accuracy. The results found in this study could give advice for banana plantation management and provide approaches for plant disease detection.

Interval Prediction Method for Wind Power Based on VMD-ELM/ARIMA-ADKDE

Wind power is a new energy source, for which the forecasting accuracy is related to the feasibility of various decision-making schemes for the power grid. The random, fluctuating and uncertain characteristics of wind power make accurate point forecasting very difficult and result in low practical application value. Therefore, a novel wind-power interval prediction method is proposed in this paper. First, variational model decomposition (VMD) is applied to the historical time sequence for wind power, followed by using an extreme learning machine (ELM) to establish a prediction model for the intrinsic mode function (IMF). An autoregressive integrated moving average (ARIMA) is used to establish a prediction model for the residual components, and the predicted IMF and residual components are reconstructed to predict the wind power. Finally, iterative self-organizing data analysis technique algorithm (ISODATA) are used with the error sequence to perform clustering segmentation, and adaptive diffusion kernel density estimation (ADKDE) is used to fit the probability density function for the prediction error of each segment and thereby obtain the cumulative distribution function and the final interval prediction under a given confidence level (CL). The results of a simulation based on real wind farm data from Hunan Province, China, from January to April 2020 show that the proposed method can significantly improve interval coverage.

A Rapid Model (COV_PSDI) for Winter Wheat Mapping in Fallow Rotation Area Using MODIS NDVI Time-Series Satellite Observations: The Case of the Heilonggang Region

Rapid and accurate monitoring of spatial distribution patterns of winter wheat over a long period is of great significance for crop yield prediction and farmland water consumption estimation. However, weather conditions and relatively long revisit cycles often result in an insufficient number of continuous medium-high resolution images over large areas for many years. In addition, the cropland pattern changes frequently in the fallow rotation area. A novel rapid mapping model for winter wheat based on the normalized difference vegetation index (NDVI) time-series coefficient of variation (NDVI_COVfp) and peak-slope difference index (PSDI) is proposed in this study. NDVI_COVfp uses the time-series index volatility to distinguish cultivated land from background land-cover types. PSDI combines the key growth stages of winter wheat phenology and special bimodal characteristics, substantially reducing the impact of abandoned land and other crops. Taking the Heilonggang as an example, this study carried out a rapid mapping of winter wheat for four consecutive years (2014–2017), and compared the proposed COV_PSDI with two state-of-the-art methods and traditional methods (the Spectral Angle Mapping (SAM) and the Iterative Self-Organizing Data Analysis Technique Algorithm (ISODATA)). The verification results revealed that the COV_PSDI model improved the overall accuracy (94.10%) by 4% compared with the two state-of-art methods (90.80%, 89.00%) and two traditional methods (90.70%, 87.70%). User accuracy was the highest, which was 93.74%. Compared with the other four methods, the percentage error (PE) of COV_PSDI for four years was the lowest in the same year, with the minimum variation range of PE being 1.6–3.6%. The other methods resulted in serious overestimation. This demonstrated the effectiveness and stability of the method proposed in the rapid and accurate extraction of winter wheat in a large area of fallow crop rotation region. Our study provides insight for remote sensing monitoring of spatiotemporal patterns of winter wheat and evaluation of “fallow rotation” policy implementation.

Understanding vehicles commuting pattern based on license plate recognition data

Commuting vehicles are one of the most important vehicles on the road network, especially during morning and evening peak hours. Analysis of commuting vehicles can lay foundation for policy formulation, urban planning, business decision-making. A large number of license plate recognition (LPR) detectors installed on the road network have accumulated a large amount of LPR data that contain the spatio-temporal information on vehicles. Therefore, using the LPR data, the commuting behavior of vehicles can be mined and analyzed. This study takes the LPR data of Hangzhou, China as an example, a series of algorithms are used to extract a total of nine features reflecting the commuting travel behavior using the one-month LPR data, and then the factor analysis method is used to integrate the nine extracted features into three factors. The commuting behavior is characterized from three perspectives: the stability of the vehicle travel behavior during the high frequency travel time period on working days, the stability of the vehicle travel behavior during the non high frequency travel time period, the stability of the origin and destination (OD) of a vehicle during the high frequency travel time period. Based on these three factors, three methods, including the density-based spatial clustering of applications with noise algorithm (DBSCAN), the iterative self-organizing data analysis technique algorithm (ISODATA), and the fast search and find of density peaks clustering algorithm, are used to identify commuting pattern vehicles and their results are compared. Finally, the decision tree algorithm is used to extract the commuting rule and identify commuting vehicles from the collected data. After that, The influence of different high frequency travel time period on commuting rule extraction and commuting pattern vehicle recognition is carefully analyzed. The influence of randomness on the results is also tested. The proposed commuting pattern vehicle recognition algorithm is proved to be robust. The detection frequency and the first and last detected times of commuting vehicles and non commuting vehicles are then analyzed. 78.0% of commuting vehicles were first detected between 06:30 AM and 10:30 AM on each workday on average. And 77.8% of commuting vehicles were last detected between 03:30 PM and 09:30 PM on each workday on average. However, for non commuting vehicles, the phenomenon that start or end the travel during the high frequency travel time period is not obvious. This study lays a foundation for travel policy formulation. For instance, it can provide a reference for the determination of the restricted periods when implementing the travel restriction policy. In addition, the identification of commuting vehicles is extremely useful for public transit network design and optimization, and it can help the decision-making process of a customized commuter bus. In addition, these strategies are expected to reduce the use of private cars and encourage travelers to switch to public transportation.

Design of a Support System for Complicated Logistics Location Integrating Big Data

Logistics location is an important component of logistics planning that affects traffic pressure and vehicle emissions. To date, there has not been an adequate study of the integration of big data into the location for a complicated logistics system. This study developed a decision support system that can address location problems for complicated logistics systems, e.g., a multilevel urban underground logistics system (ULS), using logistics big data. First, information needed in the logistics location, such as the traffic performance index (TPI) and the origin/destination (OD) matrix, was collected and calculated using a big data platform, and this information was digitized and represented based on a geographic information system (GIS) tool. Second, a two-stage location model for a ULS was designed to balance the construction costs and traffic congestion. The first stage is establishing a set-covering model to identify optimum locations for secondary hubs based on the ant colony optimization algorithm, and the second stage is clustering of the secondary hubs to determine locations for primary hubs using the iterative self-organizing data analysis technique algorithm (ISODATA). Finally, the Xianlin district of Nanjing, China, was chosen as a case study to validate the effectiveness of the proposed system. The system can be used to facilitate logistics network planning and to promote the application of big data in logistics.

A Comparative Analysis of Index-Based Methods for Impervious Surface Mapping Using Multiseasonal Sentinel-2 Satellite Data

Studies have shown that Sentinel-2 images have advantages over Landsat images in impervious surface area (ISA) extraction. The performance of index-based methods can be affected by different binary methods and subject to seasonal variation. This study marks the first attempt to assess the performance of different spectral indices for ISA extraction using multiseasonal Sentinel-2 images. Specifically, five indices (i.e., the biophysical composition index calculated using the Gram-Schmidt orthogonalization method, biophysical composition index calculated using a principal component-based Procrustes analysis, Normalized Built-up Area Index (NBAI), combinational build-up index, and perpendicular impervious surface index (PISI)) and three impervious surface binary methods (i.e., Otsu's method, manual method, and Iterative Self-Organizing Data Analysis Technique Algorithm (ISODATA) classification method) were tested on multi-seasonal Sentinel-2 images in the main urban area of Wuhan, China. Results showed that PISI combined with the ISODATA classification method achieved the highest accuracy with 92.64% OA, and 0.8410 Kappa coefficient, and NBAI combined with Otsu's method achieved the lowest accuracy with 35.37% OA, and 0.013 Kappa coefficient. Regarding the seasonal sensitivity, PISI is relatively more stable than the other methods. The superior performance of PISI is largely due to its capability in separating ISA from soil and vegetation. In addition, summer is the best season to map ISA from Sentinel-2 images when the impervious surface is generally less confused with bare soil. This study serves as a reference for the selection of spectral indices for ISA extraction from Sentinel-2 images in relation to binary methods and seasonal effects.

Attribute Reduction Method Based on Sample Extraction and Priority

Abstract Attribute reduction is a key issue in the research of rough sets. Aiming at the shortcoming of attribute reduction algorithm based on discernibility matrix, an attribute reduction method based on sample extraction and priority is presented. Firstly, equivalence classes are divided using quick sort for computing compressed decision table. Secondly, important samples are extracted from compressed decision table using iterative self-organizing data analysis technique algorithm(ISODATA). Finally, attribute reduction of sample decision table is conducted based on the concept of priority. Experimental results show that the attribute reduction method based on sample extraction and priority can significantly reduce the overall execution time and improve the reduction efficiency.

Sensing Images for Assessing the Minimum Ecological Flux by Automatically Extracting River Surface Width

Global warming and economic development have intensified the evaporation and exploitation of river waters, resulting in reduced global river runoff. In minimum ecological flux management, objective determination of the minimum ecological flux and evaluation of whether a river complies with standards are urgently required. Satellite remote sensing allows for rapid, large-scale, and dynamic monitoring. Herein, the Tangmazhai cross-section of the Taizi River was analyzed using the Chinese Gaofen (GF) series satellite that comprises panchromatic multi-spectral sensors and the Sentinel-2 multi-spectral images to automatically extract the water surface width. We applied the Normalized Difference Water Index (NDWI)-Iterative Self-Organizing Data Analysis Technique Algorithm (ISODATA) to 225 cloudless scenes from January 2015 to November 2019. We proposed a method to evaluate the minimum ecological flux using water surface width. The river surface width at this location increased from January 2015 to November 2019, and all widths exceeded the minimum river surface water width for the month. The degree of the minimum ecological flux guarantee was determined to be satisfactory. Because there are less clouds and rain in the North China than South China, our results may be used for evaluating the degree of minimum ecological flux guarantee of many river sections in the north China through monthly monitoring.

Coal fire detection and evolution of trend analysis based on CBERS-04 thermal infrared imagery

Coal fire hazards not only causes loss of coal resources but also induces serious geological hazards and environmental degeneration. Wuda Coalfield (China), known for its widespread coal fires, was used as the study area for this study. Owing to an inherent noise problem, CBERS (China & Brazil Earth Resource Satellite)-04 thermal infrared images have not been explored and applied in the field of coal fire detection. An adaptive-edge-threshold (AET) method was proposed to delineate coal fire maps based on nighttime CBERS-04 data from 2015 to 2020. Iterative Self-Organizing Data Analysis Technique Algorithm (ISODATA) was employed to cluster coal fire spots into different classes, then coal fire propagation directions were generated by connecting the clustering centers of the same class in chronological order. The results of quantitative analysis of coal fires showed that AET method achieved the highest accuracy among four coal fire recognition algorithms; the accuracy of AET in field verification reached 81.25%. The total area of coal fires increased by 137%, while the total intensity of coal fires decreased by 18% from 2015 to 2020. Overall accuracies of ISODATA clustering coal fire spots were between 90.25% and 100.00%, the Kappa coefficients were between 0.82 and 1.00. The evolution trend of coal fires obtained based on this clustering accuracy is reliable. The evolution trend of coal fires is as follows: coal fires of Class I spread to the northwest; coal fires of Class II first propagated to the northwest, then to the south and then to the west; coal fires of Class III essentially stayed in the same place; coal fires of Class IV spread to the northwest; coal fires of Class V spread to the southeast and then to the northeast.

High-Frequency Glacial Lake Mapping Using Time Series of Sentinel-1A/1B SAR Imagery: An Assessment for the Southeastern Tibetan Plateau.

Glacial lakes are an important component of the cryosphere in the Tibetan Plateau. In response to climate warming, they threaten the downstream lives, ecological environment, and public infrastructures through outburst floods within a short time. Although most of the efforts have been made toward extracting glacial lake outlines and detect their changes with remotely sensed images, the temporal frequency and spatial resolution of glacial lake datasets are generally not fine enough to reflect the detailed processes of glacial lake dynamics, especially for potentially dangerous glacial lakes with high-frequency variability. By using full time-series Sentinel-1A/1B imagery over a year, this study presents a new systematic method to extract the glacial lake outlines that have a fast variability in the southeastern Tibetan Plateau with a time interval of six days. Our approach was based on a level-set segmentation, combined with a median pixel composition of synthetic aperture radar (SAR) backscattering coefficients stacked as a regularization term, to robustly estimate the lake extent across the observed time range. The mapping results were validated against manually digitized lake outlines derived from Gaofen-2 panchromatic multi-spectral (GF-2 PMS) imagery, with an overall accuracy and kappa coefficient of 96.54% and 0.95, respectively. In comparison with results from classical supervised support vector machine (SVM) and unsupervised Iterative Self-Organizing Data Analysis Technique Algorithm (ISODATA) methods, the proposed method proved to be much more robust and effective at detecting glacial lakes with irregular boundaries that have similar backscattering as the surroundings. This study also demonstrated the feasibility of time-series Sentinel-1A/1B SAR data in the continuous monitoring of glacial lake outline dynamics.

Unsupervised Segmentation of Remote Sensing Images using FD Based Texture Analysis Model and ISODATA

In this paper, an unsupervised segmentation methodology is proposed for remotely sensed images by using Fractional Differential (FD) based texture analysis model and Iterative Self-Organizing Data Analysis Technique Algorithm (ISODATA). Essentially, image segmentation is used to assign unique class labels to different regions of an image. In this work, it is transformed into texture segmentation by signifying each class label as a unique texture class. The FD based texture analysis model is suggested for texture feature extraction from images and ISODATA is used for segmentation. The proposed methodology was first implemented on artificial target images and then on remote sensing images from Google Earth. The results of the proposed methodology are compared with those of the other texture analysis methods such as LBP (Local Binary Pattern) and NBP (Neighbors based Binary Pattern) by visual inspection as well as using classification measures derived from confusion matrix. It is justified that the proposed methodology outperforms LBP and NBP methods.

Bioclimatic Classification of Northeast Asia Reflecting Social Factors: Development and Characterization

Biodiversity is rapidly declining globally and efforts are needed to mitigate this continually increasing loss of species. Clustering of areas with similar habitats can be used to prioritize protected areas and distribute resources for the conservation of species, selection of representative sample areas for research, and evaluation of impacts due to environmental changes. In this study, Northeast Asia (NEA) was classified into 27 bioclimatic zones using statistical techniques, and it was re-clustered into 14 groups to identify the environmental characteristics of these zones. In particular, we added land cover variables into the clustering to reflect not only simple climate but also social factors influencing biological habitats. In total, 53 bioclimatic variables were constructed, and principal components were generated using correlation analysis and principal component analysis (PCA). The iterative self-organizing data analysis technique algorithm (ISODATA) was used to cluster the principal components and land cover variable. The constructed bioclimatic zones were assigned codes and descriptive names based on aridity, seasonality, and naturality, and their environmental characteristics were identified. This study is significant in that it improved the understanding of biological habitats of NEA and established a basis for monitoring the distribution of species and for efficient and systematic management of biodiversity.

Hyperspectral Image Classification Using Unsupervised Algorithms

Hyperspectral Imaging (HSI) is a process that results in collected and processed information of the electromagnetic spectrum by a specific sensor device. It’s data provide a wealth of information. This data can be used to address a variety of problems in a number of applications. Hyperspectral Imaging classification assorts all pixels in a digital image into groups. In this paper, unsupervised hyperspectral image classification algorithms used to obtain a classified hyperspectral image. Iterative Self-Organizing Data Analysis Technique Algorithm (ISODATA) algorithm and K-Means algorithm are used. Applying two algorithms on Washington DC hyperspectral image, USA, using ENVI tool. In this paper, the performance was evaluated on the base of the accuracy assessment of the process after applying Principle Component Analysis (PCA) and K-Means or ISODATA algorithm. It is found that, ISODATA algorithm is more accurate than K-Means algorithm. Since The overall accuracy of classification process using K-Means algorithm is 78.3398% and The overall accuracy of classification process using ISODATA algorithm is 81.7696%. Also the processing time increased when the number of iterations increased to get the classified image.

Unsupervised image classification over supercomputers Kraken, Keeneland and Beacon

The iterative self-organizing data analysis technique algorithm (ISODATA) was implemented over supercomputers Kraken, Keeneland and Beacon to explore scalable and high-performance solutions for image processing and analytics using emerging advanced computer architectures. When 10 classes are extracted from one 18-GB image tile, the calculation can be reduced from several hours to no more than 90 seconds when 100 CPU, GPU or MIC processors are utilized. High-performance scalability tests were further implemented over Kraken using 10,800 processors to extract various number of classes from 12 image tiles totalling 216 gigabytes. As the first geospatial computations over GPU clusters (Keeneland) and MIC clusters (Beacon), the success of this research illustrates a solid foundation for exploring the potential of scalable and high-performance geospatial computation for the next generation cyber-enabled image analytics.

Mapping the irrigated rice cropping patterns of the Mekong delta, Vietnam, through hyper-temporal SPOT NDVI image analysis

Successful identification and mapping of different cropping patterns under cloudy conditions of a specific crop through remote sensing provides important baseline information for planning and monitoring. In Vietnam, this information is either missing or unavailable; several ongoing projects studying options with radar to avoid earth observation problems caused by the prevailing cloudy conditions have to date produced only partial successes. In this research, optical hyper-temporal Satellite Pour l'Observation de la Terre (SPOT) VEGETATION (SPOT VGT) data (1998–2008) were used to describe and map variability in irrigated rice cropping patterns of the Mekong delta. Divergence statistics were used to evaluate signature separabilities of normalized difference vegetation index (NDVI) classes generated from the iterative self-organizing data analysis technique algorithm (ISODATA) classification of 10-day SPOT NDVI image series. Based on this evaluation, a map with 77 classes was selected. Out of these 77 mapped classes, 26 classes with prior knowledge that they represent rice were selected to design the sampling scheme for fieldwork and for crop calendar characterization. Using the collected information of 112 farmers’ fields belonging to the 26 selected classes, the map produced provides highly accurate information on rice cropping patterns (94% overall accuracy, 0.93 Kappa coefficient). We found that the spatial distributions of the triple and the double rice cropping systems are highly related to the flooding regime from the Hau and Tien rivers. Areas that are highly vulnerable to flooding in the upper part and those that are saline in the north-western part of the delta mostly have a double rice cropping system, whilst areas in the central and the south-eastern parts mostly have a triple rice cropping system. In turn, the duration of flooding is highly correlated with the decision by farmers to cultivate shorter or longer duration rice varieties. The overall spatial variability mostly coincides with administrative units, indicating that crop pattern choices and water control measures are locally synchronized. Water supply risks, soil acidity and salinity constraints and the anticipated highly fluctuating rice market prices all strongly influence specific farmers’ choices of rice varieties. These choices vary considerably annually, and therefore grown rice varieties are difficult to map. Our study demonstrates the high potential of optical hyper-temporal images, taken on a daily basis, to differentiate and map a high variety of irrigated rice cropping patterns and crop calendars at a high level of accuracy in spite of cloudy conditions.

A comparison of land unit delineation techniques for land evaluation in the Western Cape, South Africa

Land evaluation, an integral part of land use planning, has been established as one of the preferred methods to support sustainable land use management. In essence, land evaluation aims to compare and match each potential land use with the properties of individual parcels of land, also called land units. A land unit is an area that is, according to predetermined properties, different from the surrounding land and can be assumed to have homogeneous land properties (e.g. climate, soils, cover). Land components (also called landform elements, terrain units or land surface segments) are often used as land units, mainly because their boundaries frequently coincide with transitions in environmental conditions. Although land components have traditionally been delineated by studying topographical maps, interpreting aerial photographs and making field measurements, such manual mapping techniques are very time-consuming and subjective. Land component maps can be generated more objectively and faster by using computer algorithms. This paper compares the maps produced by three algorithms, namely the automated land component mapper (ALCoM), the iterative self-organizing data analysis technique algorithm (ISODATA) and multi-resolution image segmentation (MRS), to determine which technique produces the most homogenous and morphologically representative land components for an area in the Western Cape province of South Africa. The results revealed that the three methods produced significantly different land component maps. While ISODATA's units were relatively homogenous, their boundaries rarely followed morphological discontinuities. ALCoM performed better in delineating land components along terrain discontinuities, but produced relatively heterogeneous land components. Overall, MRS performed consistently well and was significantly more sensitive to morphological discontinuities than the other two methods tested. Land use managers should, however, use MRS with care as more research is needed to determine what effect its different input parameters have on land unit boundaries.

Core image segmentation method by combining watershed and ISODATA algorithm

Watershed based image segmentation can make the edge close and precise, but has the drawback of over-segmentation. ISODATA can conglomerate gravel, but has the edge bias error. Combining watersheds and ISODATA, this paper proposed an image segmentation method to solve these problems. Firstly, watershed segmentation algorithm was applied to get the initial over-segmentation image, and Iterative Self-Organizing Data Analysis Technique Algorithm (ISODATA) was applied to get the conglomeration image. Then, labeled images and inverse wavelet transform were used together to get the full-resolution image. The results based on ISODATA algorithm were applied to solve over-segmentation problem caused by watershed. Experimental results show that it is a practicable method for the image segmentation.