Automated Calculation of Cloud Cover From RGB Composite of LandSat 8 and Diwata-1 Satellite Imagery

Mc Guillis Kim Ramos, Benjamin Joseph Jiao, Romer Kristi Aranas, Benjamin Jonah Magallon, Jerine Amado, Mark Edwin Tupas, Ayin Tamondong

This paper presents an automated process of determining cloud cover percentage that is sensor independent and works with optical RGB satellite images. The algorithm directly calculates the cloud cover percentage from raw Digital Numbers (DN) of Landsat 8 and Diwata-1 satellite images which results in shorter computation time as it only uses red, green, and blue bands. It defines cloud properties based on how clouds are distinguished by the human eye and uses the Otsu Method as a global thresholding method to separate cloud pixels. The performance of the algorithm is compared to manually generated mask, and the Automatic Cloud Cover Assessment (ACCA) algorithm on Landsat images. The algorithm captured most true cloud pixels as the ACCA without including highly reflective surface features such as the lahar and roads in the classification. However, the algorithm misclassifies thin clouds and sparse clouds. Cloud cover for Landsat image is overestimated since bright features near true cloud pixels are also misclassified as cloud. As for the implementation on the Diwata-1 images, most true cloud pixels are captured by this algorithm. This method however, underestimates cloud cover since cirrocumulus clouds or thin clouds are not identified as clouds. In conclusion, RGB band are sufficient to estimate cloud cover of satellite images with less processing steps and shorter computation time than other methods. This method can be used for fast and automated calculation of cloud cover for quicklook assessment or target capturing.

Automatic Georeferencing for Philippines' First Microstellite Diwata LCTF Payload Imagery

J. A. Amado, B.J.D. Jiao, B.J.P. Magallon, M.K.F. Ramos R.K.D. Aranas , A.M. Tamondong, and M.E.A. Tupas

Feature extraction method was used to automatically register the incoming DIWATA LCTF imagery. The feature-based detection and matching was done using Scale-Invariant Feature Transform (SIFT) and Speeded-up Robust Feature (SURF) algorithms to identify the invariant descriptors of the two same-scene images. Images were preprocessed to adjust the contrast non-uniformity inherent to LCTF images. This step enabled better discrimination of features during keypoint extraction. Random sample consensus (RANSAC) was used to eliminate fall-out matches and ensure accuracy of the feature points from which the perspective transformation parameters were derived. The transformed (slave) image was compared to the reference (master) image and sub-pixel accuracy of the keypoint locations was computed. Geometrically corrected DIWATA images were laid in cartographic coordinates by having a fully-georeferenced master image for comparison and using the keypoints as ground control points. Given the nature of the images, adjustments of characteristic thresholds (e.g. Hessian and keypoint distance) were varied to obtain the optimum matched features between two images. The DIWATA LCTF images were also matched with other images to test the sensor-invariance of the SIFT and SURF detectors.

Design of a Free and Open Source Data Processing, Archiving, and Distribution Subsytem for the Ground Receiving Station of the Philippine Scientific Earth Observation Micro-Satellite

R.K.D. Aranas, B.J.D. Jiao, B.J.P. Magallon, M.K.F. Ramos, J.A. Amado, A.M. Tamondong, and M.E.A. Tupas

The Philippines’s PHL-Microsat program aims to launch its first earth observation satellite, DIWATA, on the first quarter of 2016. DIWATA’s payload consists of a high-precision telescope (HPT), spaceborne multispectral imager (SMI) with liquid crystal tunable filter (LCTF), and a wide field camera (WFC). Once launched, it will provide information about the Philippines, both for disaster and environmental applications. Depending on the need, different remote sensing products will be generated from the microsatellite sensors. This necessitates data processing capability on the ground control segment. Rather than rely on commercial turnkey solutions, the PHL-Microsat team, specifically Project 3:DPAD, opted to design its own ground receiving station data subsystems. This paper describes the design of the data subsystems of the ground receiving station (GRS) for DIWATA. The data subsystems include: data processing subsystem for automatic calibration and georeferencing of raw images as well as the generation of higher level processed data products; data archiving subsystem for storage and backups of both raw and processed data products; and data distribution subsystem for providing a web-based interface and product download facility for the user community. The design covers the conceptual design of the abovementioned subsystems, the free and open source software (FOSS) packages used to implement them, and the challenges encountered in adapting the existing FOSS packages to DIWATA GRS requirements.

Diwata’s Payload Calibration

J. Kurihara, T. Ishida, B. Magallon, K. Vergel

The objective of this activity is to obtain the calibration constant that converts digital number to radiance.