Can Spot-6/7 CNN Semantic Segmentation Improve Sentinel-2 Based Land Cover Products? Sensor Assessment and Fusion

Abstract

Abstract. Needs for fine-grained, accurate and up-to-date land cover (LC) data are important to answer both societal and scientific purposes. Several automatic products have already been proposed, but are mostly generated out of satellite sensors like Sentinel-2 (S2) or Landsat. Metric sensors, e.g. SPOT-6/7, have been less considered, while they enable (at least annual) acquisitions at country scale and can now be efficiently processed thanks to deep learning (DL) approaches. This study thus aimed at assessing whether such sensor can improve such land cover products. A custom simple yet effective U-net - Deconv-Net inspired DL architecture is developed and applied to SPOT-6/7 and S2 for different LC nomenclatures, aiming at comparing the relevance of their spatial/spectral configurations and investigating their complementarity. The proposed DL architecture is then extended to data fusion and applied to previous sensors. At the end, the proposed fusion framework is used to enrich an existing S2 based LC product, as it is generic enough to cope with fusion at distinct levels.

Assessing Accuracy of Land Cover Change Maps Derived from Automated Digital Processing and Visual Interpretation in Tropical Forests in Indonesia

“…The overall accuracy of the VI map was 80-84%, which was in contrast to previous studies claiming above 90% accuracy [8,9]. Such a discrepancy can be attributed to a more comprehensive assessment method used in our study as well as reliance on high resolution satellite such as SPOT 6/7 imagery rather than Landsat used for the accuracy assessment in previous studies [27,50].…”