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Contribution of Minimum Noise Fraction Transformation of Multi-temporal RADARSAT-2 Polarimetric SAR Data to Cropland Classification
摘要： Agriculture is an important sector in Canada, and annual crop inventories are required in many agricultural applications. Multi-temporal polarimetric synthetic aperture radar (SAR) data have great potential in crop classification due to its less dependency on weather condition. This study, for the first time, investigated the effects of the Minimum Noise Fraction (MNF) transformation of multi-temporal RADARSAT-2 polarimetric SAR data on the performance of cropland classification through the discussing of the performance of different polarimetric SAR parameter sets, and the impact of the timing of RADARSAT-2 datasets in southwestern Ontario. The random forest classifier was adopted due to its excellent ability in crop classification. The results illustrated that the elements of coherency matrix performed the best in agricultural land cover classification. The multi-temporal polarimetric SAR data acquired from the end of June to November gave the best classification accuracy, and an overall accuracy of 90% can be achieved using two images acquired in the middle of September and October. The MNF transformation can further improve the classification accuracy, and this accuracy was competitive with the accuracy produced using the integration of optical and polarimetric SAR data.
关键词： Minimum Noise Fraction,RADARSAT-2,random forest classifier,polarimetric SAR,cropland classification
更新于2025-09-23 15:21:01
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Potential use of hyperspectral data to classify forest tree species
摘要： Background: Remote sensing techniques and data are becoming increasingly popular in forest management, e.g. for change detection and health condition analysis. Tree species recognition is a fundamental issue in taking forest inventories, especially in carbon budget modelling. Hyperspectral imagery provides an accurate classification results for large areas based on a relatively small amount of training data. Results: A hyperspectral image of a forest stand in north-eastern Poland taken using an AISA (Airborne Imaging Spectrometer for Application) Eagle camera was transformed to extract the most valuable spectral differences and was classified into seven tree types (birch, European beech, oak, hornbeam, European larch, Scots pine, and Norway spruce) using nine classification algorithms. The highest overall accuracy and kappa coefficient were 90.3% and 0.9 respectively using three minimum noise fraction bands and maximum likelihood classifier. Conclusions: Hyperspectral imaging of forests can be used to classify major forest tree species with a good degree of accuracy. It is time-efficient and user-friendly; however, the data and software required means that this approach is still expensive at present.
关键词： Trees,AISA,Hyperspectral classification,Minimum noise fraction
更新于2025-09-19 17:15:36
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Applications and Challenges of Geospatial Technology (Potential and Future Trends) || Application of ASTER Remote Sensing for Lithological Mapping in the Udaipur District of Rajasthan, India
摘要： Remote sensing applications for earth studies such as lithological discrimination, geological mapping and potential mineral exploration have shown great success worldwide. Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Level-1B image includes visible and near-infrared (VNIR) and shortwave infrared (SWIR) bands that have been analysed to discriminate lithology features in meta-sedimentary terrains of Aravalli Supergroup in Udaipur area of Rajasthan, India. The area comprises various types of geological settings and rock types composed of economic valuable deposits of lead, zinc, copper, micas and marbles; they show spectral reflectance distinctly in bands of VNIR and SWIR. The unique spectral signature reflected by lithological unit shows effectiveness in lithological mapping. The reflectance spectra of various rock types, namely, phyllitic dolomite, siliceous dolomite, metagreywacke, quartzite and gneiss, were collected in situ using spectroradiometer and used as reference of ASTER image for the preparation of spectral signature of different lithological units. The image is applied to analysis atmospheric correction using Fast Line-of-sight Atmospheric Analysis of Hypercubes (FLAASH) and empirical line calibration techniques to convert pixel radiance values into reflectance. A minimum noise fraction (MNF) transform is applied to identify the inherent variance of spectral reflectance and effectively discriminates various lithological units. The different types of lithological units are clearly discriminated using MNF method. Spectral Angle Mapper (SAM) classification is an effective tool for differentiating rock types and its distinct mineralogical composition from associated terrains. Spectral Angle Mapper (SAM) classification uses field-derived spectral signature to demarcate various lithological features with its spatial extent. The result shows different lithological units under Aravalli Supergroup, Banded Gneissic Complex and intrusive formations that are composed of meta-arkose, conglomerate, phyllite, mica schist, dolomite, metagreywacke and migmatites in various locations. The extracted geological features using ASTER image show strong resampling with the district resource map and validated using ground truth verification. The overall accuracy of SAM-classified map of lithological units is 73.39% and Kappa coefficient of 0.59. Mapping the lithological features using ASTER image, data coupled with MNF and SAM techniques provides relatively accurate result, and this study may be used for discrimination of lithological units with its spatial characteristics.
关键词： FLAASH,Lithological mapping,Spectral Angle Mapper,Remote sensing and GIS,ASTER,Minimum noise fraction
更新于2025-09-10 09:29:36
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Hyperspectral Image Classification Using Spatial and Edge Features Based on Deep Learning
摘要： In recent years, deep learning has been widely used in the classification of hyperspectral images and good results have been achieved. But it is easy to ignore the edge information of the image when using the spatial features of hyperspectral images to carry out the classification experiments. In order to make full use of the advantages of convolution neural network (CNN), we extract the spatial information with the method of minimum noise fraction (MNF) and the edge information by bilateral filter. The combination of the two kinds of information not only increases the useful information but also effectively removes part of the noise. The convolution neural network is used to extract features and classify for hyperspectral images on the basis of this fused information. In addition, this article also uses another kind of edge-filtering method to amend the final classification results for a better accuracy. The proposed method was tested on three public available datasets: the University of Pavia, the Salinas, and the Indian Pines. The competitive results indicate that our approach can realize a classification of different ground targets with a very high accuracy.
关键词： hyperspectral images classification,Deep learning,spatial features,convolution neural network,minimum noise fraction
更新于2025-09-04 15:30:14