LAPAN became serious about making a remote sensing satellite on its third-generation satellite. Launched a year after LAPAN-A2, the third-generation satellite, LAPAN-A3, brought LISAT as the main payload. LISAT is a multispectral camera with 4 bands (Red, Green, Blue, NIR) that can be used for land classification, agriculture monitoring, drought monitoring, and land use changing. LAPAN-A3 is the third generation of micro-satellite developed by Satellite Technology Center -LAPAN. This satellite carries a multispectral push-broom sensor that can record the earth's surface at the visible and near-infrared spectrum. This paper aims to determine object-based land cover classification in Rote Island using the LAPAN-A3 satellite image using the tree method algorithm. This classification technique is expected to increase the accuracy of land cover classification. This classification used the LAPAN-A3 satellite imagery of Rote Island. The first process was determined the segmentation with scale parameter 60, shape 0.5, and compactness 0.5. The result shows that OBIA classification on Rote Island, the area of the open land class is 233.67 km 2 , the area of the settlement is 11.57 km 2 , the body of water is 2006.21 km2, the area of low vegetation is 525.93 km 2 , the area of high vegetation is 437.5 km 2 , and there is no data (cloud and cloud shadows) on the LAPAN-A3 image of 45.78 km 2 . The accuracy values obtained were producer 86.67%, KIA 83.02%, Helden 92.86%, Short 86.7%, KIA per class 82.72%, and 85.96%. This object-based classification can meet international and national land cover classification standards, namely at 80%.

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LAPAN-A3 (LA3) data has been utilized for earth observation in monitoring natural resources. While most applications are toward land resources monitoring, recent utilization indicates the possibility of LA3 detecting oil spill events on the sea surface. This research provides information regarding the ability of sensors characteristics of LA3 to detect oil slicks and its initial results by examining multispectral bands combination using Optimum Index Factor (OIF), and Digital Number (DN) extraction is carried out on each LA3 band in water-oil-water since LA3 is not able to change DN to reflectance value. In this study, besides using LA3 data, Sentinel-2 data was also used as comparative data and results in validation. Based on the results of the OIF calculation, the combination of the Blue-Green-NIR (BGN) band has the highest value compared to other combinations. This indicates that the BGN band combination is appropriate for visualizing oil and distinguishing between oil and water. The pattern formed from the visualization results with the combination of the BGN band is silvery in crude oil and greenish in ship waste disposal. The result is also strengthened by DN extraction from slick oil samples that shows a prominent pattern on the Blue and Green bands. Finally, this study can conclude that LA3 has great potential to detect oil spills visually but still requires further research for reflectance analysis by converting the DN value into reflectance.

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This work introduces a novel noise removal algorithm for satellite imageries based on superpixel segmentation followed by statistics-based filtering. The algorithm worked in three main steps. First, the noisy input image was divided into subregions by employing simple linear iterative clustering (SLIC)-based superpixel segmentation. Then, the statistical property of each subregion was calculated, including their standard deviations and maximum values. Last, an adaptive statistics-based stripe noise removal was performed for each subregion by constructing adaptive filter sizes according to calculated properties. The algorithm was tested using real satellite imageries taken by the LAPAN-A2 and LAPAN-A3 satellites. Its performance was then compared to three existing methods in terms of image quality and computation speed. Extensive experiments on two datasets of 3-channel images captured by the LAPAN-A2 satellite showed that the algorithm was capable of reducing the stripe pattern as measured using the peak-signal-to-noise-ratio (PSNR) metric without introducing additional artifacts, which commonly appeared on over-corrected regions. Moreover, compared to existing methods, the proposed algorithm ran 42 to 103 times faster and provided better image quality by 2.46%, measured using the structural similarity metric (SSIM). The code of this work and the datasets used for the testing are publicly available on www.github.com/dancingpixel/SPSNR.