A simple and effective method for filling gaps in Landsat ETM+ SLC-off images

Satellite instruments, particularly the Landsat TM (Thematic Mapper) and ETM+ (Enhanced Thematic Mapper Plus) series of sensors, are important tools in the interdisciplinary study of tropical forests that are increasingly integrated into studies that monitor changes in vegetation cover within tropical forests and tropical protected areas, and also applied with other types of data to investigate the drivers of land cover change. However, further advances in the use of Landsat to study and monitor tropical forests and protected areas are threatened by the scan line corrector failure on the ETM+ sensor, as well as uncertainty about the continuity of the Landsat mission. Given these problems, this paper illustrates how ETM+ data were used in an interdisciplinary study that effectively monitored forest cover change in Gunung Palung National Park in West Kalimantan, Indonesian Borneo. Following 31 May 2003, when the ETM+ sensor's scan line corrector failed, we analysed how this failure impedes our ability to perform a similar study from this date onwards. This analysis uses six simulated post‐scan line corrector failure (SLC‐off) images and reveals that data gaps caused by SLC‐off introduce maximum errors of 1.47 per cent and 4.04 per cent in estimates of forest cover and rates of forest loss, respectively. The analysis also demonstrates how SLC‐off has transformed ETM+ data from a complete inventory dataset to a statistical sample with variable sample fraction, and notes how this data loss will confound the use of Landsat data to model land cover change in a spatially explicit manner. We discuss potential limited uses of SLC‐off data and suggest alternative sensors that may provide essential remotely sensed data for monitoring tropical forests in Southeast Asia.

ABSTRACTThe Landsat mission which has existed over five decades has remained at the forefront of providing consistent moderate spatial and temporal resolution optical images of the earth. The failure of the scan line corrector (SLC) on board the Landsat 7 Enhanced Thematic Mapper Plus (ETM+) in May 2003 has permanently resulted in data gaps on each Landsat 7 scene. Due to the obvious negative impacts on the image usability, a number of methods have been developed to fill the no-data areas in the image. This study assessed the performance of four Landsat 7 ETM+ SLC-off gap-filling methods in a highly heterogeneous landscape of West Africa for two different seasons (dry and rainy). The methods considered are: (1) Weighted Linear Regression (WLR) integrated with Laplacian Prior Regularization Method (LPRM), (2) Localised Linear Histogram Matching (LLHM), (3) Neighbourhood Similar Pixel Interpolator (NSPI) and (4) Geostatistical Neighbourhood Similar Pixel Interpolator (GNSPI). All the images used were Landsat 7 ETM+ SLC-off images, temporally close and from the same season for each set of time step. Visual comparison, mean, and standard deviations of the histograms of all bands of only the filled areas were used to assess the results. Additionally, overall accuracy (OA), kappa coefficient (κ), and balanced accuracy (BA) per class were used to evaluate a land use/cover (LULC) classification based on the gap-filled images. Visually, all the four methods were able to completely fill the gaps in the Landsat 7 ETM+ SLC-off image. They all look similar and spatially continuous with no anomalies or artefacts on them. The histograms from each band for only the filled areas for all the four methods also gave similar means and standard deviations in most cases. All the four gap-filling methods provided satisfactory results (OA >96% and κ> 0.937 in all methods for images in the dry season and OA >93% and κ> 0.877 for the image in the rainy season) in the land cover classification considering the complexity of the study area. But the GNSPI was superiority in all cases with the highest OA of 97.1% and κ of 0.947 in the dry season and OA of 94.6% and κ of 0.899 in the rainy season. This implies that the GNSPI is more robust in gap filling of Landsat 7 ETM+ SLC-off images than the other three methods in a heterogeneous landscape of West Africa regardless of the season. This study suggests that gap filling of Landsat 7 ETM+ SLC-off images will help to increase the number of Landsat images needed to build time-series data for a data-scarce region such as West Africa.

The scan-line corrector (SLC) for the Enhanced Thematic Mapper Plus (ETM+) sensor, on board the Landsat7 satellite, have failed probably permanently since May 31, 2003. The consequence of the SLC failure (denoted SLC-off) is that approximately 20% of the pixels in an ETM+ image are not scanned, which hampers the use of the data accordingly. To improve the usability of the ETM+ SLC-off data, several researches relating to the gap-fill algorithms have been conducted with acceptable accuracy. Due to the limitation of data acquisition, e.g. temporal resolution and atmospheric condition, there are always a large number of overlapping areas filled with un-scanned pixels in two cloud-free ETM+ SLC-off images which are close in time. Consequently, the recovering procedure would be unavailable through general gap-fill algorithms. The bands similarity between China Brazil Earth Resources Satellite-02B (CBERS-02B) and Landsat7 ETM+, particularly for the visible/near-infrared bands, makes it possible to estimate the un-scanned pixels in the ETM+ SLC-off image considering the close time CBERS-02B as auxiliary data. In this paper, CBERS-02B(acquired on January 17, 2009) was taken as auxiliary data to fill the un-scanned pixels in two ETM+ SLC-off images(acquired on January 21, 2009 and November 5, 2009 respectively) with the suitable gap-fill algorithms. Four gap-fill algorithms were practiced and compared, which called simple filling (Simple), global linear histogram match (GLHM), localized linear histogram match (LLHM), and adaptive window linear histogram match (AWLHM) respectively. In contrast to Simple, GLHM and LLHM, AWLHM took into account the number of effective pixels as well as the impact of local conditions. Therefore, while ignoring computing speed or time consumption, AWLHM was a generally superior method with higher accuracy to others. Lastly, taking Xiamen Island as a study region, we used the recovered ETM+ data filled by AWLHM to extract urban impervious surface (UIS) at sub-pixel scale, adopting the selective endmember linear spectral mixture model (LSMM). The accuracy of UIS estimation was validated using a sharpened IKONOS image with spatial resolution of 1m(acquired on January 18, 2009). Results indicated that there was no significant difference between the scanned and filled (un-scanned in original ETM+ SLC-off image) pixels, in view of the estimation accuracy of UIS. In conclusion, CBERS-02B should be regarded as usefully auxiliary data so as to recover the ETM+ SLC-off image and enable more scientific use of the data.

A new geostatistical approach for filling gaps in Landsat ETM+ SLC-off images

On 31 May 2003, the scan line corrector (SLC) of the Landsat 7 Enhanced Thematic Mapper Plus (ETM+) sensor which compensates for the forward motion of the satellite in the imagery acquired failed permanently, resulting in loss of the ability to scan about 20% of the pixels in each Landsat 7 SLC-off image. This permanent failure has seriously hampered the scientific applications of ETM+ images. In this study, an innovative gap filling approach has been introduced to recover the missing pixels in the SLC-off images using multi-temporal ETM+ SLC-off auxiliary fill images. A correlation is established between the corresponding pixels in the target SLC-off image and two fill images in parallel using the multiple linear regressions (MLR) model. Simulated and actual SLC-off ETM+ images were used to assess the performance of the proposed method by comparing with multi-temporal data based methods, the LLHM method which is based on simple linear regression (SLR) model. The qualitative and quantitative evaluations indicate that the proposed method can recover the value of un-scanned pixels accurately, especially in heterogeneous landscape and even with more temporally distant fill images.

The data gaps in the Landsat 7 Enhanced Thematic Mapper Plus (ETM+) Scan Line Corrector (SLC)-off imagery as a result of SLC failure are well recognized. The degradation introduced by their use in scientific applications is concerning to Landsat users. SLC-off data gaps cause problems in many applications of ETM+ images, but no literature reported the problem in seagrass mapping. To investigate the impact of SLC-off data loss on the seagrass information extraction, two types of data were compared: (a) data with interpolation after the SLC anomaly, termed the “Interpolation ON (ION)”, and (b) the data without interpolation, termed the “Interpolation OFF (IOFF)” image, for the Sungai Pulai estuary seagrass meadows of Malaysia. Additionally, the random shifting of SLC-off stripes was tested by swipe analysis of SLC-off image pairs. Overall, the SLC-off scene analysis suggests that a gradual increase of data gaps from the central part toward the edge may cause a cumulative error of 2 % based on an object’s distance from the nadir path. The random shifting of SLC-off stripes may be completely invisible if a single SLC-off stripe passes over a targeted small seagrass meadow such as the Tanjung Adang Laut shoal, which has a spatial extent of 11.07 ha. The data gaps eventually lead to misinterpretations and produce erroneous seagrass distribution maps. The co-existence of SLC-off stripes and their random shifting phenomenon have caused non-overlapping regions between SLC-off scenes acquired on different dates. Future research should develop suitable methods for gap-filling and resolve aquatic remote sensing mapping issues by using knowledge from the present research.

Open quarries are at the same time a necessity but also a source of pollution. Necessity as they supply the necessary fuel for energy production and source of pollution as they affect biodiversity, vegetation cover and threaten water resources. The objective of this work is to indicate a monitoring methodology using Landsat ETM SLC off imagery. On May 31, 2003, the Scan Line Corrector (SLC), which compensates for the forward motion of Landsat 7, failed. Without an operating SLC, the Enhanced Thematic Mapper Plus (ETM+) line of sight now traces a zig-zag pattern along the satellite ground track. As a result, imaged area is duplicated, with width that increases towards the scene edge. An estimated twenty-two percent of any given scene is lost because of the SLC failure. The maximum width of the data gaps along the edge of the image would be equivalent to one full scan line, or approximately 390 to 450 meters. The precise location of the missing scan lines will vary from scene to scene. In this study a gap filling technique for Landsat ETM SLC off imagery is evaluated. Different Landsat 7 ETM+ images SLC off were restored and then compared to historical data and data from other sensors. The restored images have been used in order to monitor the expansion of an open quarry in western Peloponnese and the results are presented.

The scan-line corrector (SLC) of the Landsat 7 Enhanced Thematic Mapper Plus (ETM+) sensor failed in May 2003, and this abnormal functioning of SLC resulted in about 22% of the pixels per scene without being scanned. By filling the un-scanned gap by a good technique will help in more use of ETM+ data for many scientific applications. While there have been a number of approaches developed to fill in the data gaps in ETM+ imagery, each method has shortcomings, especially they require SLC-on (images acquired before SLC-off anomaly) imagery for the same location to fill the gaps in SLC-off (images acquired after SLC anomaly) image. To overcome such shortcomings this study proposes an alternative interpolation method based on the partial derivative. This case study shows that this technique is very much useful to interpolate the missing pixel values in the SLC-off ETM+ data.

On May 31, 2003, the scan line corrector (SLC) of the Enhanced Thematic Mapper Plus (ETM+) on-board the Landsat-7 satellite failed, resulting in strips of data lost in all ETM+ images acquired since then. In this paper, we proposed a novel inpainting algorithm for recovering the ETM+ SLC-off images. The two slopes of the boundaries of each missing stripe were extracted through the Hough transform, ignoring the slope of the edge of the strip that overlaps the edge of the image. An adaptive dictionary was then developed and trained using ETM+ SLC-on images acquired before May 31, 2003 so that the physical characteristics and geometric features of the ground coverage of the data-missing strips can be considered during recovery. To make the algorithm computationally efficient, data-missing strips were repaired along their slope directions by using the logdet $\left ({\cdot }\right)$ low-rank nonconvex model along with the dictionary. The algorithm was tested using the simulated ETM+ SLC-off images created from a multiband ETM+ SLC-on image file and compared to the high accuracy low-rank tensor completion (HaLRTC), logDet, and tensor nuclear norm (TNN) algorithms. The results show that the ETM+ images restored using the new algorithm have lower RMSE, higher PSNR and structure similarity (SSIM) values, and better visualization. These results indicate that the new algorithm performs better than the other three algorithms and can efficiently and accurately restore the data-missing stripes.

Since 2003, the permanent failure of the scan line corrector (SLC) of the Landsat Enhanced Thematic Mapper Plus (ETM+) sensor has seriously limited the scientific applications and usability of ETM+ data. While a number of methods have been conducted to fill the regular un-scanned locations in ETM+ SLC-off images, only a few researches have been developed to recover the large gap areas in such images. In this study, an innovative gap filling method has been introduced to reconstruct the large gap locations in SLC-off images via multi-temporal auxiliary fill images. A correlation is established between the corresponding pixels in the target SLC-off image and two auxiliary fill images in parallel using the multiple linear regression (MLR) model in two successive steps. In the first step, almost half the gap locations have been recovered using the MLR model, then in the second step a weighted multiple linear regression (WMLR) algorithm is proposed to recover the remaining missing values. The simulated and actual case studies show that the proposed approach may provide a powerful tool for recovering the large gaps in SLC-off images, especially when there is a long time interval between the auxiliary fill images and the target SLC-off image.

Since 2003, the scan line corrector (SLC) of the Landsat 7 Enhanced Thematic Mapper Plus (ETM+) sensor has failed permanently, inhibiting the retrieval or scanning of 22% of the pixels in each Landsat 7 SLC‐off image. This utter failure has seriously limited the scientific applications and usability of ETM+ data. Precise and complete recovery of the missing pixels for the Landsat 7 SLC‐off images is a challenging issue and developing an efficient gap‐fill algorithm with improved ETM+ data usability has been ever‐demanding. In this study, a new gap filling method has been introduced to reconstruct the SLC‐off images via multi‐temporal SLC‐off auxiliary fill images. A correlation is established between the corresponding pixels in the target SLC‐off image and two auxiliary fill images in parallel using the multiple linear regressions model. Both simulated and actual defective Landsat 7 images were tested to assess the performance of the proposed model by comparing with two multi‐temporal data based methods, the local linear histogram matching method and Neighbourhood Similar Pixel Interpolator method. The quantitative evaluations indicate that the proposed method makes an accurate estimate of the missing values even for more temporally distant fill images.

Geostatistical interpolation of SLC-off Landsat ETM+ images

Present study is an attempt to analyse the dynamics of land use / land cover using modern geospatial techniques of Remote Sensing and GIS in Takula Block of District Almora, Uttarakhand, India. The Landsat TM (Thematic Mapper) satellite images for year 1990 , Landsat ETM+ (Enhanced Thematic Mapper Plus) images for years 2005 and training data collected through field visit were used to analyse the dynamics of land use / land cover from 1999 to 2005 over a 15 year of period. Maximum Likelihood Algorithm was used for image classification in ERDAS 9.3. Mapping and analysis of land use / land cover classes were performed in ArcGIS 9.1 software. Five classes of land use / land cover (namely: forest, croplands, water bodies, built-up structures and fallow land) were mapped and analysed in the study area. The study reveals that the land use / land cover changes have occurred in forest (− 6.28%), croplands (+7.99%), built-up structures (1.22%) fallow land (−2.97%) and water body (0.04%). The study also highlights the importance of digital change detection techniques in sustainable land use planning and development for Takula Block.

Filling damaged pixels in satellite images is a key task present in many Remote Sensing applications. As a representative example of image restoration issue, we can refer to the failure of the Scan Line Corrector (SLC) on board the Landsat Enhanced Thematic Mapper Plus (ETM +) sensor, in which 22% of the scanned pixels in the SLC-off images were missed, thus creating unexpected stipe-type gaps in the scenes. In order to improve the usability of ETM + SLC-off data in a straightforward manner, in this paper we propose a unified methodology that automatically segments and repairs Landsat-7 scenes occluded by stripes. The proposed framework combines Morphology-based filtering, anisotropic diffusion and block-based pixel replication as an effective, fully unsupervised restoration methodology designed to cope with different gap sizes in Landsat images. Our approach does not require having as input data any prior gap mask, side reference image or time-dependent frames of the same scene to work properly. As shown in the experimental results, the current methodology performs adequately for a variety of multispectral remote sensing images with different stripe-size thicknesses and heterogeneous segments. We attest to the accuracy and robustness of our end-to-end framework throughout a variety of qualitative and quantitative evaluations involving state-of-the-art restoration methods.

Both Terra MODIS and Landsat 7 (L7) Enhanced Thematic Mapper Plus (ETM+) have been successfully operating for over 15 years to collect valuable measurements of the earth’s land, ocean, and atmosphere. The land-viewing bands of both sensors are widely used in several scientific products such as surface reflectance, normalized difference vegetation index, enhanced vegetation index etc. A synergistic use of the multi-temporal measurements from both sensors can greatly benefit the science community. Previous effort from the MODIS Characterization Support Team (MCST) was focused on comparing the top-of-atmosphere reflectance of the two sensors over Libya 4 desert target. Uncertainties caused by the site/atmospheric BRDF, spectral response mismatch, and atmospheric water-vapor were also characterized. In parallel, an absolute calibration approach based on empirical observation was also developed for the Libya 4 site by the South Dakota State University’s (SDSU) Image Processing Lab. Observations from Terra MODIS and Earth Observing One (EO-1) Hyperion were used to model the Landsat ETM+ TOA reflectance. Recently, there has been an update to the MODIS calibration algorithm, which has resulted in the newly reprocessed Collection 6 Level 1B calibrated products. Similarly, a calibration update to some ETM+ bands has also resulted in long-term improvements of its calibration accuracy. With these updates, calibration differences between the spectrally matching bands of Terra MODIS and L7 ETM+ over the Libya 4 site are evaluated using both approaches.