This paper presents the categorization and restoration of defective lines developed in pushbroom images. About 100 of the 3000 SPOT 4 SWIR detectors malfunction, which degrades image quality. Conventional methods have difficulties in effectively detecting and restoring defective lines, because they ignore the heterogeneity of the ground surface and the presence of sporadically unstable detectors with gain and offset that vary during a scan. While all defective lines have previously been considered as a single type, here they are categorized into three types according to the variation pattern in the scanning direction: constant defective lines, irregular defective lines, and irrecoverable defective lines. The detection procedure utilizes summed data and standard deviation data that consist of abnormal peaks originating from defective lines and a slowly varying baseline reflecting the surface characteristics within the image. The defective lines are detected by finding abnormal peaks, and classified and restored by using either a moment-matching method or interpolation, depending upon their types. Three SPOT 4 images were used to test and evaluate the performance of the proposed method. From the test results, the constant defective line was the most common type, comprising about 60%, while the irregular defective lines caused serious image degradation because of the difficulty of detecting and classifying them. Commission and omission errors were less than 10% and detection accuracy was higher than 90%. The analysis of signal-to-noise ratio (SNR) showed that the low SNR created by the defective lines was effectively removed. Our method gave a significant improvement of the detection and restoration capability.

Due to the phenological variation of vegetation growth in temperate region, it is often difficult to accurately assess the surface conditions on agricultural croplands, grasslands, and disturbed forests by current multi-spectral remote sensor data. In particular, the spectral similarity between soil and dry vegetation has been primary concern to correctly appraise the surface conditions during the non-growing seasons in temperature region. This study analyzes the spectral characteristics of the mixture of dry vegetation and soil. The reflectance spectra were obtained from laboratory spectroradiometer measurement and from EO-1 Hyperion image data. The reflectance spectra of several samples having different level of dry vegetation fractions show similar pattern from both lab measurement and hyperspectral image. Shortwave IR wavelengths, in particular at wavelength near 2200nm, are more sensitive to the fraction of dry vegetation. The reflectance spectra of bare soil could be distinguished from the other surfaces covered by dry vegetation during the leaf-off season. © 2005 IEEE.