Missing Pixel Reconstruction on Landsat 8 Analysis Ready Data Land Surface Temperature Image Patches Using Source-Augmented Partial Convolution

Maosi Chen,Zhibin Sun,Wei Gao,Chelsea A Corr,Benjamin H Newell

doi:10.3390/rs12193143

Maosi Chen, Zhibin Sun + Show 3 more

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https://doi.org/10.3390/rs12193143

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Abstract

Missing pixels is a common issue in satellite images. Taking Landsat 8 Analysis Ready Data (ARD) Land Surface Temperature (LST) image as an example, the Source-Augmented Partial Convolution v2 model (SAPC2) is developed to reconstruct missing pixels in the target LST image with the assistance of a collocated complete source image. SAPC2 utilizes the partial convolution enabled U-Net as its framework and accommodates the source into the framework by: (1) performing the shared partial convolution on both the source and the target in encoders; and (2) merging the source and the target by using the partial merge layer to create complete skip connection images for the corresponding decoders. The optimized SAPC2 shows superior performance to four baseline models (i.e., SAPC1, SAPC2-OPC, SAPC2-SC, and STS-CNN) in terms of nine validation metrics. For example, the masked MSE of SAPC2 is 7%, 20%, 44%, and 59% lower than that of the four baseline models. On the six scrutinized cases, the repaired target images generated by SAPC2 have the fewest artifacts near the mask boundary and the best recovery of color scales and fine textures compared with the four baseline models.

Highlights

Satellite remote sensing systems can acquire observations/images of atmosphere, ocean, and the Earth’s surface, providing regional or even global coverages with different spatial resolutions
Chen et al (2019) [33] proposed a Source-Augmented Partial Convolution neural network, which derives from the U-Net architecture and utilizes the partial convolution based operations to handle the missing pixels in the feature extraction and target-source merging process
This paper develops a deep learning model, Source-Augmented Partial Convolution v2 (SAPC2), to reconstruct missing pixels in partially-corrupted Landsat 8 Analysis Ready Data (ARD) Land surface temperature (LST) 64 × 64 image patch with the assistance of a collocated adjacently-acquired complete ARD 8 LST image patch

Summary

Introduction

Satellite remote sensing systems can acquire observations/images of atmosphere, ocean, and the Earth’s surface, providing regional or even global coverages with different spatial resolutions. Due to satellite sensor malfunction and poor atmospheric conditions, remote sensing images often suffer from missing pixel problems, such as thick cloud cover, dead pixels, and the scan line corrector (SLC) failure [1]. The cloud fraction over land and ocean is about 55% and 72%, respectively [2]. Missing pixel problems are common for remote sensing images, which may affect subsequent image analysis and applications. Over the past several decades, accuracy of LST retrieval from satellite thermal infrared (TIR) measurements has significantly improved, missing pixel problems still exist for satellite-based LST products/images (e.g., Landsat 8 Analysis Ready Data LST tiles [5])

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