No abstract

Road information is fundamental not only in the military field but also common daily living. Automatic road extraction from a remote sensing images can provide references for city planning as well as transportation database and map updating. However, owing to the spectral similarity between roads and impervious structures, the current methods solely using spectral characteristics are often ineffective. By contrast, the detailed information discernible from the high-resolution aerial images enables road extraction with spatial texture features. In this study, a knowledge-based method is established and proposed; this method incorporates the spatial texture feature into urban road extraction. The spatial texture feature is initially extracted by the local Moran's I, and the derived texture is added to the spectral bands of image for image segmentation. Subsequently, features like brightness, standard deviation, rectangularity, aspect ratio, and area are selected to form the hypothesis and verification model based on road knowledge. Finally, roads are extracted by applying the hypothesis and verification model and are post-processed based on the mathematical morphology. The newly proposed method is evaluated by conducting two experiments. Results show that the completeness, correctness, and quality of the results could reach approximately 94%, 90% and 86% respectively, indicating that the proposed method is effective for urban road extraction.

No abstract

Soil salinization is one of the most devastating land degradation process causing agricultural yields reduction. This paper presents a hyperspectral prediction model of soil salinity using partial least squares regression (PLSR) in Tianjin costal area. Soil spectral reflectance of soil samples varying in salinity was measured using an ASD Field Spec spectrometer. The treated continuum-removed (CR) reflectance and first-order derivative reflectance (FDR) were used and compared to explore the more preferable predicting model of soil salinity, which could detect subtle differences in spectral absorption features compared with original reflectance. The results showed that the soil spectra reflectance got distinct absorption feature with peaks centred at 411 nm, 475 nm, 663 nm, 868 nm, 1100 nm ~ 1250 nm, 1400 nm, 690 nm, 1911 nm, 2206 nm and 2338 nm, representing key bands for soil salt content estimation. Through established Partial Least-Square Regression model based on treated soil spectra, the first derived-continuum-removed reflectance was the optimal spectra indexes, prediction accuracy of the optimal PLSR model was 94.4%.