The Study on Linear Mixed Parcel Unmixing for Classification
نویسنده
چکیده
: Aiming at the disadvantage of hard per-parcel classification which can't solve the difficulty of mixed parcel resulting in the low accuracy, a new method of soft per-parcel classification is presented, that is linear mixed parcel unmixing. Based on the linear spectral theory for the parcel unmixing, the predicted fraction value is assigned to a parcel. The RMSE results show that the accuracy of this method is similar to the traditional linear spectral unmxing method, and is higher than that of hard per-parcel classification. There are two advantages about this method. Firstly, this method incorporating more than one pixel information ensures the result stability. Secondly, the problem of mixed parcel, which disturbs the hard per-parcel classification, can be solved. All of above improve the per-parcel classification accuracy.
منابع مشابه
An Overview of Nonlinear Spectral Unmixing Methods in the Processing of Hyperspectral Data
The hyperspectral imagery provides images in hundreds of spectral bands within different wavelength regions. This technology has increasingly applied in different fields of earth sciences, such as minerals exploration, environmental monitoring, agriculture, urban science, and planetary remote sensing. However, despite the ability of these data to detect surface features, the measured spectrum i...
متن کاملSub-pixel classification of hydrothermal alteration zones using a kernel-based method and hyperspectral data; A case study of Sarcheshmeh Porphyry Copper Mine and surrounding area, Kerman, Iran
Remote sensing image analysis can be carried out at the per-pixel (hard) and sub-pixel (soft) scales. The former refers to the purity of image pixels, while the latter refers to the mixed spectra resulting from all objects composing of the image pixels. The spectral unmixing methods have been developed to decompose mixed spectra. Data-driven unmixing algorithms utilize the reference data called...
متن کاملAn experiment-based quantitative and comparative analysis of target detection and image classification algorithms for hyperspectral imagery
Over the past years, many algorithms have been developed for multispectral and hyperspectral image classification. A general approach to mixed pixel classification is linear spectral unmixing, which uses a linear mixture model to estimate the abundance fractions of signatures within a mixed pixel. As a result, the images generated for classification are usually gray scale images, where the gray...
متن کاملImproving Land Use/Land Cover Classification by Integrating Pixel Unmixing and Decision Tree Methods
Decision tree classification is one of the most efficient methods for obtaining land use/land cover (LULC) information from remotely sensed imageries. However, traditional decision tree classification methods cannot effectively eliminate the influence of mixed pixels. This study aimed to integrate pixel unmixing and decision tree to improve LULC classification by removing mixed pixel influence....
متن کاملSpectral unmixing versus spectral angle mapper for land degradation assessment: a case study in Southern Spain
Unlike conventional sensor systems such as Landsat-TM, Spot-MX or IRS-LISS, which acquire data in only a few spectral bands, the development of scanner systems that acquire data in many narrow-wavelength bands allows the use of almost continuous reflectance data in studies of the Earth’s surface. This not only produces laboratory-like reflectance spectra with absorption bands specific to object...
متن کامل