Zhe Jiang, [email protected] Abstract: My research explores novel computational techniques to map the physical cover (e.g., forests) of the earth’s surface from satellite images. Processing these images is labor-intensive and a significant burden on scientists. Existing methods ignore spatial information and assume that pixels are statistically independent. Consequently, they produce erroneous map...

Since the launch of the first land observation satellite Landsat-1 in 1972, many machine learning algorithms have been used to classify pixels in Thematic Mapper (TM) imagery. Classification methods range from parametric supervised classification algorithms such as maximum likelihood, unsupervised algorithms such as ISODAT and k-means clustering to machine learning algorithms such as artificial...

Time sequential Landsat MSS and TM images were used to map land use/cover of the Atlanta metropolitan area for the past 25 years as a component of the NASA-funded Project ATLANTA (ATlanta Land-use ANalysis: Temperature and Air-quality), which has the objective to model the impact of land use/cover change on temperature and air quality in Atlanta. This paper describes a suite of techniques that ...

New aerial cameras and new advanced geoprocessing tools improve the generation of urban land cover maps. Elevations can be derived from stereo pairs with high density, positional accuracy, and efficiency. The combination of multispectral high-resolution imagery and high-density elevations enable a unique method for the automatic generation of urban land cover maps. In the present paper, imagery...

The production of thematic maps, such as those depicting land cover, using an image classification is one of the most common applications of remote sensing. Considerable research has been directed at the various components of the mapping process, including the assessment of accuracy. This paper briefly reviews the background and methods of classification accuracy assessment that are commonly us...

Land cover change (LCC) studies are increasingly using deep learning (DL) modeling techniques. Past have leveraged temporal or spatiotemporal sequences of historical LC data to forecast changes with DL models. However, these do not adequately assess the association between neighborhood size and model capability LCCs, where refers spatial extent captured by each sample. The objectives this resea...

Novel statistical modeling and training techniques are proposed for improving classification accuracy of land cover data acquired by LandSat Thermatic Mapper (TM). The proposed modeling techniques consist of joint modeling of spectral feature distributions among neighboring pixels and partial modeling of spectral correlations across TM sensor bands with a set of semi-tied covariance matrices in...

Support vector machines represent a promising development in machine learning research that is not widely used within the remote sensing community. This paper reports the results of Multispectral(Landsat-7 ETM+) and Hyperspectral DAIS)data in which multi-class SVMs are compared with maximum likelihood and artificial neural network methods in terms of classification accuracy. Our results show th...

Use of multisource remote sensing data, particularly Synthetic Aperture Radar (SAR) and optical images, can improve performance of land cover classification since many types of information are involved in the classification process. Recently, the multiple classification systems (MCS) or ensemble classifiers has been developed and increasingly used for classifying remote sensing data. It is cons...

Numerous applications make use of data on land use and land cover (LULC). Given their importance and use, land cover data is assumed to be readily available or trivially acquired for a given landscape. Unfortunately, this is often not the case. LULC data at hand are often out-of-date, inappropriate for a particular application [1], or contain other difficulties. Thematic mapping of remotely sen...