Detection and Restoration of Defective Lines in the SPOT 4 SWIR Band

Abstract

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.

Parameter free image artifacts detection: a compression based approach

“…The conditional complexity K(x, y) of x related to y is defined as the length of the shortest program with which we can obtain an output x from y. An important application of this notion is to estimate the shared information between two objects: The Normalized Information Distance (NID) [1]. The NID is proportional to the length of the shortest program that can calculate x given y.…”

“…The Normalized Compression Distance NCD has a definition as follows: Since the Kolmogorov Complexity K(x) is a non-computable function, an approximation is defined by Li in [1] considering K(x) as the compressed version of x, and a lower limit of what can be achieved with the compressor C.…”

“…Previous research [1,2] use the Kolmogorov Complexity for define the Normalized information Distance NID and from there derive the Normalized Compression Distance. It is to approximate K(x) with C(x) = K(x) + k, the length of the compressed version of x obtained by a lossless compressor C plus an unknown constant k: the presence of k is necessary because it is not possible to estimate how close of K(x) is this approach.…”

“…parameter free. In the process of elaborating the standard product of a satellite image, there is a process of correction of artifacts as presented by Hyung-Sup in [1] which is an algorithm for the restoration of defectives lines; however some artifacts may be remaining after this process. The work presented in this paper is the continuation of the work by A. Mallet in [2] and [3].…”

Effective three-step method for efficient correction of stripe noise and non-uniformity in infrared remote sensing images

“…Striping effects, observed in space borne and airborne remote sensing systems, result from inconsistent responds between different detectors, photon effects, and calibration error [5].…”