Accuracy Of Land Use Classification Research Articles

A new bare-soil index for rapid mapping developing areas using LANDSAT 8 data

Abstract. One of the most basic classification tasks is to distinguish bare-soil areas from urban region. Bare-soil plays an important role in the ecosystem. It could be the reason of dust storms and the indicator of urban expansion. It is also important to monitor the bare-soil areas, but there was no good idea to automatically extraction bare-soil areas using existing method. In this work, a new bareness index (BI) has been developed and applied to map developing region in Pearl River Delta using Landsat OLI/TIRS data in 2013. The BI based on the logical combination of the Tasseled Cap transformation (TCB) and Normalized Difference Bareness Index (NDBaI). Results show that the BI not only has a good effect on the enhancement of bare soil information, but also on the inhibition of the background information, and improve the accuracy of detection. The results of this study could be of scientific and practical merits in regional remote sensing monitoring and improve the accuracy of land use classification.

Research on Classification of Remote Sensing Image Based on SVM Including Textural Features

In order to solve the problems in the traditional remote sensing image based on spectral information, such as low classification accuracy, different object with the same spectral features or the same object with the different spectral features, and limited sample quantity and so on, a remote sensing image classification method based on the support vector machine (SVM) including with textural features is proposed. Using Langqi Island of Fuzhou as experimental area, preprocessing and principal component analysis were made to initialize TM images, and the spectral features and GLCM-based textural features of ground objects were extracted and analyzed respectively. Then, the extraction, training and testing of samples based on the two types of features were finished for training various SVM classifiers, which were used for classifying land use in the experimental area. Through the maximum likelihood method, the BP neural network and the support vector machine (SVM), a crossed classification and contrast experiment was made to two different types of samples based on the simple spectral features and the features combined with texture respectively. The experimental results showed that the SVM classification method including textural features can effectively improve the accuracy of land use classification, and therefore it can be promoted better.

Identification of land cover alterations in the Alta Murgia National Park (Italy) with VHR satellite imagery

Landcoverexertsagreatinfluenceonmanybasicenvironmentalprocessesandconsequentlyanytransformation in it may have a marked impact on the environment from the local to the global scales. In multidisciplinary research contexts, satellite remote sensing offers opportunities both to evaluate the effects of these processes and to provide one of the information layers needed for designing national strategies oriented to protection and sustainableuseofourresources.Theadventofrecentsatelliteimageryhasincreasedthepossibilitytoinvestigate large-scale areas in great detail. Together with an increase in spatial and radiometric resolution, there is, usually, an increase in the variability within land parcels, generating a decrease in the accuracy of land use classification on a per-pixel basis. In order to avoid such negative impacts, an object-oriented classification methodology on IKONOS multispectral data has been implemented on the test area of the Alta Murgia National Park, in the Apulia region (Italy), where soil adaptation for agricultural practices, through rock breaking, has taken place over the last 20 years. The analysis has been conducted with a classification strategy that is able to distinguish land use functions on the basis of differences in spatial distribution and pattern of land cover forms. It consists of two phases: segmentation of the image into meaningful multipixel objects of various sizes, based on both spectral and spatial characteristics of groups of pixels; then, assignment of the segments (objects) to classes using fuzzy logic and a hierarchical decision key.

Integrating genetic algorithm method with neural network for land use classification using SZ-3 CMODIS data*

Abstract This paper presents a methodology on land use mapping using CMODIS (Chinese Moderate Resolution Imaging Spectroradiometer) data on-board SZ-3 (Shenzhou 3) spacecraft. The integrated method is composed of genetic algorithm (GA) for feature extraction and neural network classifier for land use classification. In the data preprocessing, a moment matching method was adopted to remove the stripes in the images. Then by using the reproduction, crossover and mutation operators of GA based on the mechanism of “natural selection”, and with Jeffries-Matusita distance as its discriminate rule and the training samples, the optimal band combination for land use classification was obtained. To generate a land use map, the three layers back propagation neural network classifier is used for training the samples and classification. Compared with the Maximum Likelihood classification algorithm, the results show that the accuracy of land use classification is obviously improved by using our proposed method, the sel...