• Korean Journal of Remote Sensing
• /
• v.37 no.5_1
• /
• pp.999-1011
• /
• 2021

In spatio-temporal fusion aiming at predicting images with both high spatial and temporal resolutionsfrom multi-sensor images, the radiometric inconsistency between input multi-sensor images may affect prediction performance. This study investigates the effect of radiometric correction, which compensate different spectral responses of multi-sensor satellite images, on the spatio-temporal fusion results. The effect of relative radiometric correction of input images was quantitatively analyzed through the case studies using Sentinel-2, PlanetScope, and RapidEye images obtained from two croplands. Prediction performance was improved when radiometrically corrected multi-sensor images were used asinput. In particular, the improvement in prediction performance wassubstantial when the correlation between input images was relatively low. Prediction performance could be improved by transforming multi-sensor images with different spectral responses into images with similar spectral responses and high correlation. These results indicate that radiometric correction is required to improve prediction performance in spatio-temporal fusion of multi-sensor satellite images with low correlation.

• Korean Journal of Remote Sensing
• /
• v.38 no.5_1
• /
• pp.523-533
• /
• 2022

This paper presents a two-stage spatio-spectral fusion method (2SSFM) based on area-to-point regression kriging (ATPRK) to enhance spatial and spectral resolutions using multi-sensor satellite images with complementary spatial and spectral resolutions. 2SSFM combines ATPRK and random forest regression to predict spectral bands at high spatial resolution from multi-sensor satellite images. In the first stage, ATPRK-based spatial down scaling is performed to reduce the differences in spatial resolution between multi-sensor satellite images. In the second stage, regression modeling using random forest is then applied to quantify the relationship of spectral bands between multi-sensor satellite images. The prediction performance of 2SSFM was evaluated through a case study of the generation of red-edge and short-wave infrared bands. The red-edge and short-wave infrared bands of PlanetScope images were predicted from Sentinel-2 images using 2SSFM. From the case study, 2SSFM could generate red-edge and short-wave infrared bands with improved spatial resolution and similar spectral patterns to the actual spectral bands, which confirms the feasibility of 2SSFM for the generation of spectral bands not provided in high spatial resolution satellite images. Thus, 2SSFM can be applied to generate various spectral indices using the predicted spectral bands that are actually unavailable but effective for environmental monitoring.

• Journal of Sensor Science and Technology
• /
• v.21 no.6
• /
• pp.402-407
• /
• 2012

We report a Fabry-Perot interferometer (FPI)-based multi-channel micro-spectrometer used for multi-gas measurement in the spectral range of $3-5{\mu}m$ and its gas sensing performance. The fabricated infrared (IR) spectrometer consists of two parts: an FPI on the top side for selective IR filtering and a $V_2O_5$-based IR detector array on the bottom side for the detection of the filtered IR. Experimental results show that the FPI-based multi-channel gas sensor has reliability and selectivity for simultaneously detecting environmentally harmful gases such as $CH_4$, $CO_2$, $N_2O$ and CO in the spectral range of $3-5{\mu}m$. The fabricated FPI-based multi-channel gas sensor also demonstrated that a reliable and selective detection of gas concentrations ranging from 0 to 500 ppm is feasible. In addition, the electrical characteristics demonstrate a superior response performance in regards to the selectivity in the multi-target gases.

• Korean Journal of Remote Sensing
• /
• v.20 no.5
• /
• pp.337-351
• /
• 2004

Our laboratory originally developed the compact, multi-spectral, automatic aerial photographic system PKNU3 to allow greater flexibility in geological and environmental data collection. We are currently developing the PKNU3 system, which consists of a color-infrared spectral camera capable of simultaneous photography in the visible and near-infrared bands; a thermal infrared camera; two computers, each with an 80-gigabyte memory capacity for storing images; an MPEG board that can compress and transfer data to the computers in real-time; and the capability of using a helicopter platform. Before actual aerial photographic testing of the PKNU3, we experimented with each sensor. We analyzed the lens distortion, the sensitivity of the CCD in each band, and the thermal response of the thermal infrared sensor before the aerial photographing. As of September 2004, the PKNU3 development schedule has reached the second phase of testing. As the result of two aerial photographic tests, R, G, B and IR images were taken simultaneously; and images with an overlap rate of 70% using the automatic 1-s interval data recording time could be obtained by PKNU3. Further study is warranted to enhance the system with the addition of gyroscopic and IMU units. We evaluated the PKNU 3 system as a method of environmental remote sensing by comparing each chlorophyll image derived from PKNU 3 photographs. This appraisement was backed up with existing study that resulted in a modest improvement in the linear fit between the measures of chlorophyll and the RVI, NDVI and SAVI images stem from photographs taken by Duncantech MS 3100 which has same spectral configuration with MS 4000 used in PKNU3 system.

• Journal of Biosystems Engineering
• /
• v.35 no.2
• /
• pp.132-137
• /
• 2010

Site-specific N application for corn is one of the precision crop management. To implement the site-specific N application, various nitrogen stress sensing methods, including aerial image, tissue analysis, soil sampling analysis, and SPAD meter readings, have been used. Use of side-dressing, an efficient nitrogen application method than a uniform application in either late fall or early spring, relies mainly on the capability of nitrogen deficiency detection. This paper presents map-based variable rate nitrogen application based using a multi-spectral corn nitrogen deficiency(CND) sensor. This sensor assess the nitrogen stress by means of the estimated SPAD reading calculated from the corn leave reflectance. The estimated SPAD value from the CND sensor system and location information form DGPS of each field block was combined into the field map using a ArcView program. Then this map was converted into a raster file for a map-based variable rate application software. The relative SPAD (RSPAD = SPAD over reference SPAD) was investigated 2 weeks after the treatments. The results showed that the map-based variable rate application system was feasible.

• Journal of Biosystems Engineering
• /
• v.36 no.3
• /
• pp.180-186
• /
• 2011

The objective of this research was to establish the estimation method of growth information on potato using Multi-Spectral Image Sensor (MSIS) and Global Positioning System (GPS). And growth estimation map for determining a prescription map over the entire field was generated. To determine the growth model, 10 ground-truth points of areas of $4m^2$ each were selected and investigated. The growth information included stem number, crop height and SPAD value. In addition, images information involving the ground-truth points were also taken by an unmanned helicopter, and reflectance value of Green, Red, and NIR bands were calculated with image processing. Then, growth status of potato was modeled by multi-regression analysis using these reflectance value of Green, Red, and NIR. As a result, potato growth information could be detected by analyzing Green, Red, and NIR images. Stem number, crop height and SPAD value could be estimated with $R^2$ values of 0.600, 0.657 and 0.747 respectively. The generated GIS map would describe variability of the potato growth in a whole field.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
• /
• 2007.04a
• /
• pp.145-148
• /
• 2007

The Korea Multi-Purpose Satellite-2 (KOMPSAT-2) was launched in July 2006 and The main mission of the KOMPSAT-2 is a high resolution imaging for the cartography of Korea peninsula by utilizing Multi Spectral Camera (MSC) images. The camera resolutions are 1 m in panchromatic scene and 4 m in multi-spectral imaging. KOMPSAT-2 measure the position, velocity and attitude data of satellite using by star sensor, gyro sensor, and GPS sensor. This paper provides an initial geometric accuracy assessment of the KOMPSAT-2 high resolution image, both geometric Cal/Val overview.

• The Journal of Korea Robotics Society
• /
• v.17 no.3
• /
• pp.245-254
• /
• 2022

Sensor dataset for autonomous driving is one of the essential components as the deep learning approaches are widely used. However, most driving datasets are focused on typical environments such as sunny or cloudy. In addition, most datasets deal with color images and lidar. In this paper, we propose a driving dataset with multi-spectral images and lidar in adverse weather conditions such as snowy, rainy, smoky, and dusty. The proposed data acquisition system has 4 types of cameras (color, near-infrared, shortwave, thermal), 1 lidar, 2 radars, and a navigation sensor. Our dataset is the first dataset that handles multi-spectral cameras in adverse weather conditions. The Proposed dataset is annotated as 2D semantic labels, 3D semantic labels, and 2D/3D bounding boxes. Many tasks are available on our dataset, for example, object detection and driveable region detection. We also present some experimental results on the adverse weather dataset.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
• /
• v.29 no.5
• /
• pp.449-457
• /
• 2011

As automated image processing techniques have been required in multi-temporal/multi-sensor geospatial image applications, use of automated but highly invariant image matching technique has been a critical ingredient. Note that there is high possibility of geometric and spectral differences between multi-temporal/multi-sensor geospatial images due to differences in sensor, acquisition geometry, season, and weather, etc. Among many image matching techniques, the SIFT (Scale Invariant Feature Transform) is a popular method since it has been recognized to be very robust to diverse imaging conditions. Therefore, the SIFT has high potential for the geospatial image processing. This paper presents a performance test results of the SIFT on geospatial imagery by simulating various image differences such as shear, scale, rotation, intensity, noise, and spectral differences. Since a geospatial image application often requires a number of good matching points over the images, the number of matching points was analyzed with its matching positional accuracy. The test results show that the SIFT is highly invariant but could not overcome significant image differences. In addition, it guarantees no outlier-free matching such that it is highly recommended to use outlier removal techniques such as RANSAC (RANdom SAmple Consensus).

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
• /
• 2004.11a
• /
• pp.143-152
• /
• 2004

Researchers seeking geological and environmental information, depend on remote sensing and aerial photographic datum from various commercial satellites and aircraft. However, adverse weather conditions as well as equipment expense limit the ability to collect data anywhere and anytime. To allow for better flexibility in geological and environmental data collection, we have developed a compact, multi-spectral automatic Aerial Photographic system (PKNU2). This system's Multi-spectral camera can record visible (RGB) and infrared (NIR) band (3032＊2008 Pixels) images Visible and infrared band images were obtained from each camera respectively and produced color-infrared composite images to be analyzed for the purpose of the environmental monitoring. However this did not provide quality data. Furthermore, it has the disadvantage of having the stereoscopic overlap area being 60% unsatisfied due to the 12 seconds of storage time of each data The PKNU2 system in contrast, photographed photos of great capacity Thus, with such results, we have been proceeding to develop the advanced PKNU2 (PKNU3) system that consists of a color-infrared spectral camera that can photograph in the visible and near-infrared bands simultaneously using a single sensor, a thermal infrared camera, two 40G computers to store images, and an MPEG board that can compress and transfer data to the computer in real time as well as be able to be mounted onto a helicopter platform.