Multi-source image reconstruction: exploitation of EO-1/ALI in Landsat-7/ETM+ SLC-off gap filling

Abstract

The Landsat-7 Enhanced Thematic Mapper Plus (ETM+) is the sensor payload on the Landsat-7 satellite imager (launched on April 15th, 1999) that is a derivative of the Landsat-4 and 5 Thematic Mapper (TM) land imager sensors. Scan Line Corrector (SLC) malfunctioning appeared onboard on May 31, 2003. The SLC-Off problem was caused by failure of the SLC which compensates for the forward motion of the satellite [1]. As ETM+ is still capable of acquiring images with the SLC-Off mode, the need of applying new techniques and using other data sources to reconstruct the missed data is a challenging for scientists and final users of remotely sensed images. One of the predicted future roles of the Advanced Land Imager (ALI) onboard the Earth Observer One (EO-1) is its ability to offer a potential technological direction for Landsat data continuity missions [2]. In this regard more than the purposes of the work as fabricating the gapped area in the ETM+ the attempt to evaluate the ALI imagery ability is another noticeable point in this work. In the literature there are several techniques and algorithms for gap filling. For instance local linear histogram matching [3], ordinary kriging, and standardized ordinary cokriging [4]. Here we used the Regression Based Data Combination (RBDC) in which it is generally supposed that two data sets (i.e. Landsat/ETM+ and EO-1/ALI) in the same spectral ranges (for instance band 3 ETM+ and band 4 ALI in 0.63 - 0.69 μm) will have meaningful and useable statistical characteristics. Using this relationship the gap area in ETM+ can be filled using EO-1/ALI data. Therefore the process is based on the knowledge of statistical structures of the images which is used to reconstruct the gapped areas. This paper presents and compares four regression based techniques. First two ordinary methods with no improvement in the statistical parameters were undertaken as Scene Based (SB) and Cluster Based (CB) followed by two statistically developed algorithms including Buffer Based (BB) and Weighted Buffer Based (WBB) techniques. All techniques are executed and evaluated over a study area in Sulawesi, Indonesia. The results indicate that the WBB and CB approaches have superiority over the SB and BB methods.