Using hyperspectral remote sensing data in urban mapping over Kuala Lumpur

Hyperspectral remote sensing has great application potential for analyzing complex urban scenes. In this study, airborne hyperspectral data over part of Kuala Lumpur, Malaysia were used to classify 14 urban classes. In order to do the classification, Support Vector Machine (SVM) was used. Some filters (Lee and Enhanced Lee) were used before performing the classification. Consequently, the results showed that the overall accuracy is improved (3%-4%) when the filters were applied to the image. The overall accuracy for classification of the study area using SVM is 89% with Kappa coefficient 0.88 without filtering. The use of Lee and Enhanced Lee filters improved the accuracy to 92 and 93.6% respectively. This study serves as a pioneering effort in the application of hyperspectral sensing for urban area in Malaysia

Development of fuzzy rule-based parameters for urban object-oriented classification using very high resolution imagery

Urban areas consist of spectrally and spatially heterogeneous features. Advanced information extraction techniques are needed to handle high resolution imageries in providing detailed information for urban planning applications. This study was conducted to identify a technique that accurately maps impervious and pervious surfaces from WorldView-2 (WV-2) imagery. Supervised per-pixel classification algorithms including Maximum Likelihood and Support Vector Machine (SVM) were utilized to evaluate the capability of spectral-based classifiers to classify urban features. Object-oriented classification was performed using supervised SVM and fuzzy rule-based approach to add spatial and texture attributes to spectral information. Supervised object-oriented SVM achieved 82.80% overall accuracy which was the better accuracy compared to supervised per-pixel classifiers. Classification based on the proposed fuzzy rule-based system revealed satisfactory output compared to other classification techniques with an overall accuracy of 87.10% for pervious surfaces and an overall accuracy of 85.19% for impervious surfaces

A review on hyperspectral remote sensing for homogeneous and heterogeneous forest biodiversity assessment

This review paper evaluates the potential of hyperspectral remote sensing for assessing species diversity in homogeneous (non-tropical) and heterogeneous (tropical) forest, an increasingly urgent task. Existing studies of species distribution patterns using hyperspectral remote sensing have used different techniques to discriminate different species, in which the wavelet transforms, derivative analysis and red edge positions are themost important of them. The wavelet transform is used based on its effectiveness and determined as the most powerful technique to identify species. Furthermore, estimations of relationships between spectral values and species distributions using chemical composition of foliage, tree phenology, selection of signature training sites based on field measured canopy composition, selection of the best wavelet coefficient and waveband regions may be useful to identify different plant species. This paper presents a summary on the feasibility, operational applications and possible strategies of hyperspectral remote sensing in forestry, especially in assessing its biodiversity. The paper also reviews the processing and analysis of techniques for hyperspectral data in discriminating different forest tree species

Compression of remote sensing data using second-generation wavelets: a review

Wavelet-based methods have been widely used for compression of remotely sensed images and data. Recently, second generation of wavelets which is based on a method called lifting has proven to be more effective than traditional wavelets as it provides lossless compression, lowers the memory usage, and is computationally faster. This study explores the literature related to applying second-generation wavelets for the compression of remote sensing data. Nevertheless, in order to compare the results of two wavelet types, some applications of traditional wavelets are also presented

Development of a generic model for the detection of roof materials based on an object-based approach using WorldView-2 satellite imagery

The detection of impervious surface (IS) in heterogeneous urban areas is one of the most challenging tasks in urban remote sensing. One of the limitations in IS detection at the parcel level is the lack of sufficient training data. In this study, a generic model of spatial distribution of roof materials is considered to overcome this limitation. A generic model that is based on spectral, spatial and textural information which is extracted from available training data is proposed. An object-based approach is used to extract the information inherent in the image. Furthermore, linear discriminant analysis is used for dimensionality reduction and to discriminate between different spatial, spectral and textural attributes. The generic model is composed of a discriminant function based on linear combinations of the predictor variables that provide the best discrimination among the groups. The discriminate analysis result shows that of the 54 attributes extracted from the WorldView-2 image, only 13 attributes related to spatial, spectral and textural information are useful for discriminating different roof materials. Finally, this model is applied to different WorldView-2 images from different areas and proves that this model has good potential to predict roof materials from the WorldView-2 images without using training data

A stable and accurate wavelet-based method for noise reduction from hyperspectral vegetation spectrum

Hyperspectral vegetation spectrum is normally contaminated with noise and the presence of noise affects the results of vegetation studies, such as species discrimination and classification, disease detection, stress assessment and the estimation of vegetation’s biophysical and biochemical characteristics. Additionally, hyperspectral signals are usually studied using the derivative analysis method that is very sensitive to noise in the data. This study investigates denoising of the hyperspectral vegetation spectrum using different wavelet-based methods. A test signal and several real-world vegetation spectra are denoised using four wavelet methods: traditional discrete wavelet transform (DWT); stationary wavelet transform (SWT); lifting wavelet transform (LWT); and a combination of SWT and LWT, which in this paper is called stationary lifting wavelet transform (SLWT). SLWT incorporates the advantages of both SWT and LWT methods, including a translation invariance property and a fast simple algorithm. Experimental results show that SLWT highly outperforms other wavelet-based methods in terms of accuracy and visual quality. Furthermore, this research reveals the following novel results: SLWT 1) for different levels of decomposition of the wavelet transform gives similar results and its denoising results is independent to the selection of decomposition level; 2) generates stable statistical results; 3) can make use of mother wavelets with small filter size (i.e., low-order mother wavelets) that are suitable for preserving subtle features in vegetation spectrum; and 4) its denoising results do not depend on the selection of the mother wavelet when applying low-order mother wavelets

Development and utilization of urban spectral library for remote sensing of urban environment

Hyperspectral technology is useful for urban studies due to its capability in examining detailed spectral characteristics of urban materials. This study aims to develop a spectral library of urban materials and demonstrate its application in remote sensing analysis of an urban environment. Field measurements were conducted by using ASD FieldSpec 3 Spectroradiometer with wavelength range from 350 to 2500 nm. The spectral reflectance curves of urban materials were interpreted and analyzed. A collection of 22 spectral data was compiled into a spectral library. The spectral library was put to practical use by utilizing the reference spectra for WorldView-2 satellite image classification which demonstrates the usability of such infrastructure to facilitate further progress of remote sensing applications in Malaysia

Assessing Accuracy of the Vertical Component of Airborne Laser Scanner for 3D Urban Infrastructural Mapping.

This study presents two methods used to measure the accuracy of the height component of Airborne Laser Scanning (ALS) data.The objectives are: to assess the accuracy of LiDAR data, to find correlation between the actual and sensor recorded height, and to explore the effectiveness of linear regression model for accuracy assessment. Field observation was carried out with Total Station as reference data and the corresponding data obtained from normalized digital surface model (n-DSM). First, statistical method was used to obtained a Root Mean Square Error (RMSE) value of 0.607 and linear accuracy of 1.18948 at 95% confidence level. Similarly, linear regression function was used to obtained RMSE value of 0.5073 and linear accuracy of 1.10999. The study shows that ALS recorded height is reliable for 3D urban mapping. A resulting correlation coefficient of 0.9919 indicates a very good agreement between the sensor recorded height and the actual height of the object (R2= 0.9839; p less than 2.2e-16). The study indicates that linear regression model is effective for assessing the accuracy of ASL data

PFR model and GiT for landslide susceptibility mapping: a case study from Central Alborz, Iran

In northern parts of Iran such as the Alborz Mountain belt, frequent landslides occur due to a combination of climate and geologic conditions with high tectonic activities. This results in millions of dollars of financial damages annually excluding casualties and unrecoverable resources. This paper evaluates the landslide susceptible areas in Central Alborz using the probabilistic frequency ratio (PFR) model and Geo-information Technology (GiT). The landslide location map in this study has been generated based on image elements interpreted from IRS satellite data and field observations. The display, manipulation and analysis have been carried out to evaluate layers such as geology, geomorphology, soil, slope, aspect, land use, distance from faults, lineaments, roads and drainages. The validation group of actual landslides and relative operation curve method has been used to increase the accuracy of the final landslide susceptibility map. The area under the curve evaluates how well the method predicts landslides. The results showed a satisfactory agreement of 91% between prepared susceptibility map and existing data on landslide locations

An integrated GIS based statistical model to compute groundwater vulnerability index for decision maker in agricultural area

The conservation areas in a plain are affected by the groundwater contamination from intense application of the fertilizers. The vulnerability of groundwater can be tested by using the DRASTIC model for the pollutants. The groundwater susceptibility to pollution in the various areas is mapped through DRASTIC model. However, the effects of pollution types and its characteristics are not considered, as this model is used without any modifications. This technique must be standardized for usage in the various aquifers and specific pollution types. The rates of DRASTIC parameters are corrected to obtain the potential for a more accurate analysis of the vulnerability pollution. The relationships between the parameters are identified with respect to the nitrate concentration in the groundwater by calculating the new rates. The methodology was applied to the selected area situated in the south eastern region of Iran at Kerman plain. Twenty-seven different locations were selected to test and analyse the nitrate concentration in the water from underground wells. The pollution in the aquifer was associated and correlated with the DRASTIC index by using the measured nitrate concentrations. The relationship between the index and the measured pollution in the Kerman plain was determined by applying the Wilcoxon rank-sum nonparametric statistical tests and the rates were calculated. It was found specifically in the agricultural areas that the modified DRASTIC model performed more efficiently than the traditional method for nonpoint source pollution, as indicated by the results. After modifications, the regression coefficients revealed that the relationship between the vulnerability index and the nitrate concentration was 77 %, while it was 37 % before the modifications were used. These statistics show that the modified DRASTIC performed far more efficiently than the original version