Near Real-Time Browsable Landsat-8 Imagery

Abstract

Abstract:The successful launch and operation of Landsat-8 extends the remarkable 40-year acquisition of space-based land remote-sensing data. To respond quickly to emergency needs, real-time data are directly downlinked to 17 ground stations across the world on a routine basis. With a size of approximately 1 Gb per scene, however, the standard level-1 product provided by these stations is not able to serve the general public. Users would like to browse the most up-to-date and historical images of their regions of interest (ROI) at full-resolution from all kinds of devices without the need for tedious data downloading, decompressing, and processing. This paper reports on the Landsat-8 automatic image processing system (L-8 AIPS) that incorporates the function of mask developed by United States Geological Survey (USGS), the pan-sharpening technique of spectral summation intensity modulation, the adaptive contrast enhancement technique, as well as the Openlayers and Google Maps/Earth compatible superoverlay technique. Operation of L-8 AIPS enables the most up-to-date Landsat-8 images of Taiwan to be browsed with a clear contrast enhancement regardless of the cloud condition, and in only one hour's time after receiving the raw data from the USGS Level 1 Product Generation System (LPGS). For any ROI in Taiwan, all historical Landsat-8 images can also be quickly viewed in time series at full resolution (15 m). The debris flow triggered by Typhoon Soudelor (8 August 2015), as well as the barrier lake formed and the large-scale destruction of vegetation after Typhoon Nepartak (7 July 2016), are given as three examples of successful applications to demonstrate that the gap between the user's needs and the existing Level-1 product from LPGS can be bridged by providing browsable images in near real-time.

Adaptive Contrast Enhancement of Optical Imagery Based on Level of Detail (LOD)

“…Similar to the discussion on the Sentinel Explorer, cirrocumulus clouds and haze limit the dynamic range of contrast and deteriorate the quality of the visual effect. The right panels are pansharpened using a spectral summation intensity modulation technique [26] and then LOD-based enhancement (Equation ( 1)) with a linear transition (Equation ( 2)). As a result, the improvements in the visual effect and the spatial details are apparent when comparing the right panels to the middle and left panels.…”

“…Unless it can be masked out completely, a small patch of cloud or haze will render the LCE approach invalid. As demonstrated by Liu et al [26], this problem can be tackled by masking out clouds and applying the adaptive contrast enhancement (ACE) technique [27] to those regions without cloud masking, and then adding back the cloud mask with a saturated value. The ACE technique assigns each pixel to an intensity proportional to its rank within the surrounding neighborhood.…”

“…With the same assumption, Shen et al [22] proposed a homomorphic filter-based method. Liu et al [26] employed the adaptive contrast enhancement technique and demonstrated that near-real time Landsat-8 imagery can be browsed with clear contrast enhancement regardless of the cloud condition. As summarized by Liu et al [17], most enhancement-based methods are mainly concentrated on low-resolution multispectral data.…”

Clouds Classification from Sentinel-2 Imagery with Deep Residual Learning and Semantic Image Segmentation

“…We also demonstrated that CloudNet improved the accuracy of predictions without causing performance degradation and other costs. CloudNet will be incorporated into the Open Access Satellite Image Service (OASIS, http://oasis.ncku.edu.tw) [31] to support the calculation of near real-time changes in LULC over the Taiwan area in a fully automated fashion. A total of 5,017,600 pixels (manually labeled Sentinel-2 clouds) were created for training and validation in this study, and we released the manually labeled cirrus-sufficient dataset so that researchers in related fields can effectively apply it to their own research.…”

“…CFMask is made available by the Earth Resources Observation and Science Center of the U.S. Geological Survey (USGS) in order to provide standard Landsat Level-1 data products, including cloud, cloud confidence, cloud shadow, and snow/ice masks. It has been incorporated into the L-8 Automatic Image Processing System to process all scenes of Landsat-8 imagery covering the Taiwan area on an operational basis [31]. However, CFMask has difficulties over bright targets, and thin clouds or haze are usually omitted.…”