In repeat-pass interferometric synthetic aperture radar (In-SAR), the coherence loss derived from temporal decorrelation can be modeled using an exponential decay function. Many researches have investigated the relationships between decay parameters over various land-cover types for C-band SAR satellites. Although ALOS PALSAR provides many L-band SAR images, its revisit time was insufficient for understanding the rapid exponential drop in temporal decorrelation. Due to the lack of an appropriate archived dataset, analyzing the decay parameters for L-band SAR was a challenging task. In this study, we processed four-year interferometric pairs derived from ALOS-2 PALSAR-2 full-aperture ScanSAR images. This dataset has a 14 day minimum temporal interval. We selected Nairobi, Kenya, in the middle of the East African rift, as the study area and investigated how decay functions differ over multiple land-cover types. In addition, we compared decay models for Sentinel-1 interferograms. This study demonstrates that the decay parameters of L-band coherence completely differs across forest types, whereas C-band coherence is low coherence regardless of forest type.

The translation from Synthetic Aperture Radar (SAR) to optical imagery is vital in remote sensing. Nevertheless, this translation encompasses profound challenges because of the limited information in SAR data compared to optical images. While progress has been achieved through frameworks like pix2pix, the room for enhancement remains plentiful. We have assembled a unique dataset by harmonizing Sentinel-1 and Sentinel-2 satellite images from the SEN12MS dataset, accompanied by JAXA`s High-Resolution Land-Use and Land-Cover (LULC) Map of Japan. Our study proposes an efficient translation framework that modifies the original pix2pix architecture, by modifying the discriminator architecture to a new one that can be trained with LULC, which enables the supervision of the generator using the LULC map. Our method significantly supersedes the conventional pix2pix, indicating LULC discriminator may improve a result with a conventional one.

Deforestation rates in Brazilian Amazon continue to increase and addressing deforestation requires coordinated action at multiple levels, as well as international cooperation despite various strategies and action at multi-levels. To promote such strategies and action, this paper proposed a recurrent deep learning-based model, which predicts monthly deforestation events (will occur or not) at 1 km × 1 km meshes. The model was trained and evaluated by using about seven-year deforestation data (August 2016–September 2023) of eight areas in the Brazilian Amazon from the Real-Time Deforestation Detection System. By using the most recent two-year data, it was clear that the monthly prediction has a reasonable performance for recall, but very low precision. Thus, future work in the monthly prediction of deforestation should focus on refining model accuracy by incorporating more diverse data sources and improving temporal resolution.

Interferometric SAR time series analysis using multilooked interferograms has measured ground deformation with high accuracy over large areas, but is known to be contaminated by a systematic bias when using only short-term interferograms. This bias, also referred to as a "fading signal", has been investigated using the abundant time series data from Sentinel-1 C-band SAR, and soil moisture and biomass changes have been suggested as two possible causes for the observed bias. Several correction methods have been proposed to mitigate the phase bias, assuming a periodic observation strategy. The phase bias is supposed to increase with the wavelength, but the bias in ALOS-2 L-band SAR has not been investigated in detail because the data distribution policy restricts its users. In addition, these correction methods are not suitable for the infrequently observed data such as ALOS-2. In this paper, we investigated the systematic bias in ALOS-2 multilooked interferograms with respect to various interferometric pairs. We also corrected the phase bias using the noise-filtering technique. The bias in ALOS-2 multilooked interferograms showed >3 mm/year with an average temporal baseline of 144 days, which is equivalent to that of Sentinel-1 with the temporal baseline of 78 days. That is, the bias effect toward the temporal baseline was larger in ALOS-2 than in Sentinel-1. The noise-filtering technique could mitigate the bias with less pairs regardless of land-use.

This letter proposes a novel synthetic aperture radar (SAR) time series analysis method based on the scattering power decomposition algorithm with a reasonable ensemble average in both temporal and spatial domains. We reveal that the ensemble average is effective not only in the spatial domain but also in the temporal–spatial domains in the scattering power decomposition. That is, if we extend the ensemble average window in the temporal domain, the proposed method can accurately achieve volume scattering power with a higher spatial resolution than conventional approaches. The precise volume scattering power serves accurate forest monitoring. As an application, we performed forest disturbance detection in the Amazon rainforest using Sentinel-1 time series data. The proposed method detected the disturbances earlier, in less than 2 months, compared to other methods that take about 3 months.

Abstract LiDAR odometry estimation and 3D semantic segmentation are crucial for autonomous driving, which has achieved remarkable advances recently. However, these tasks are challenging due to the imbalance of points in different semantic categories for 3D semantic segmentation and the influence of dynamic objects for LiDAR odometry estimation, which increases the importance of using representative/salient landmarks as reference points for robust feature learning. To address these challenges, we propose a saliency-guided approach that leverages attention information to improve the performance of LiDAR odometry estimation and semantic segmentation models. Unlike in the image domain, only a few studies have addressed point cloud saliency information due to the lack of annotated training data. To alleviate this, we first present a universal framework to transfer saliency distribution knowledge from color images to point clouds, and use this to construct a pseudo-saliency dataset (i.e. FordSaliency) for point clouds. Then, we adopt point cloud based backbones to learn saliency distribution from pseudo-saliency labels, which is followed by our proposed SalLiDAR module. SalLiDAR is a saliency-guided 3D semantic segmentation model that integrates saliency information to improve segmentation performance. Finally, we introduce SalLONet, a self-supervised saliency-guided LiDAR odometry network that uses the semantic and saliency predictions of SalLiDAR to achieve better odometry estimation. Our extensive experiments on benchmark datasets demonstrate that the proposed SalLiDAR and SalLONet models achieve state-of-the-art performance against existing methods, highlighting the effectiveness of image-to-LiDAR saliency knowledge transfer. Source code will be available at https://github.com/nevrez/SalLONet

In the aftermath of large-scale natural disasters, the accuracy of building damage detection (BDD) is of critical importance. Post-disaster high-resolution (post-HR) remote sensing imagery is fundamental for BDD; however, prompt acquisition of such imagery remains a significant challenge. To address this issue, we introduce a novel plug-and-play feature fusion (FF) module. This module, strategically situated between a pre-trained super-resolution (SR) model and a BDD model, ingeniously combines features from both pre-disaster high-resolution (pre-HR) and super-resolved post-disaster remote sensing imagery. The proposed approach is designed to maximize the utilization of pre-HR images, thereby enhancing BDD accuracy. Experimental validation confirms that this improvement in accuracy is attributable to the pragmatic extraction of features from pre-HR imagery, not just an increase in model complexity. Consequently, our approach holds substantial promise for real-world post-disaster scenarios and lays a solid foundation for future BDD research, demonstrating potential improvements in both efficacy and practicality.

In this paper, we propose a novel disaster damage detection method using Synthetic Aperture Radar (SAR) interferometric analysis. SAR interferometric coherence analysis is an effective method for disaster monitoring especially in the urban area, where the amplitude of SAR image changes only slightly unless the damage level is high. One drawback of the interferometric coherence-based analysis is the existence of the Cramér-Rao lower bound. That is, a small spatial window for its ensemble averaging leads significant bias while a large window makes its spatial resolution worse. To solve this problem, we propose to extend the ensemble average window towards temporal domain by increasing the number of interferometric pairs. Conventional methods which use multiple interferometric pairs firstly calculate coherence values independently. Instead, the proposed method unifies the interferograms first. We report some preliminary experimental results showing the effectiveness of the proposed method.

Building damage detection (BDD) with satellite images has been frequently adopted as an essential reference for post-disaster rescue, whereas its timeliness is significantly impacted by the long revisit time of high-resolution remote sensing satellites. Therefore, a reliable super-resolution method which is optimized for accurate and detail BDD is fundamental one for advancing the BDD analysis even when we can use only low-resolution images after a disaster. Based on Super-Resolution Generative Adversarial Network (SRGAN) and U-Net convolutional network, an efficient and novel BDD framework is proposed in this paper for obtaining upsampled BDD results from low-resolution post-disaster images. We trained the framework using two disasters from the xBD dataset and tested three different structures. The results show that our training structure based on an end-to-end framework successfully generated super-resolution BDD maps from low-resolution images, which performed significantly better than those from a two-stage training structure.

Hyperspectral Imager Suite (HISUI) was launched in December 2019 and started its operation observation from International Space Station in October 2020. HISUI data are being acquired based on data acquisition requests from HISUI project and principal investigators of HISUI Research Announcement. The total area of acquired HISUI data by December 2021 exceeded 110 M km2and the data are being used in various application studies. HISUI will be operated by FY2023. In this presentation, HISUI's instrument and operation status will be presented together with its early results in terms of geology and greenhouse gas emission.

Hyperspectral Imager Suite (HISUI) was launched in December 2019 and successfully attached to Japan Experiment Module Exposed Facility, International Space Station. Due to data communication problems, its initial check out activities have been delayed, but its first light image was acquired and released in September 2020. HISUI data delivery to Research Users will start in 1Q of Japanese FY2021 (April - June 2021).

In this paper, we report the emulation of a line-of-sight displacement observed from the past SAR satellite. Recent SAR satellites can observe the same place from multiple tracks and estimate the ground displacement three-dimensionally from interferograms. We re-project the estimated displacement to the line-of-sight displacement observed from the past SAR satellite in order to compare the current and past ground events directly without external data and models. We present the experimental results to show the applicability of the proposal.

Since the huge earth quake in 2011 in Japan, the Japanese government has strongly encouraged growth of renewable energy use. As a result, even we focus on only mega-solar photovoltaic (PV) power plants (> 1,000 kW), the amount of the power generation significantly increased. Because such rapid increasing of the PV power plants changes land use widely, verification of the spreading of the PV power plants should be essential for assessing economic/environmental issues. In this study, to verify the increasing of the PV power plants based on observations, we applied a method that can detect the PV power plants efficiently with a convolutional neural network to Landsat-8 images. From the detection, we successfully identified the increasing of the area of the PV power plants quantitatively at the same time identifying the location of each PV power plant in many prefectures in the capital region of Japan.

Hyperspectral Imager Suite (HISUI) was launched on Dec. 5, 2019 and successfully attached to Japan Experiment Module Exposed Facility, International Space Station. Currently, initial check out activities are ongoing, and the first light images will be released around August, 2020. The distribution of HISUI Level 1 products will be started in the first half of 2021.

This paper presents a density enhancement method for airborne LiDAR point cloud with the corresponding image based on a fused Encoder-Decoder network. Different from terrestrial indoor or outdoor scenes, the variance of objects and depth ranges in the large scale airborne data is challenging. To address the problem of objects at different scales, we propose a RGB and depth fused Encoder-Decoder structure inspired by UNet. In addition, we propose a heuristic method for refining the result if instance segmentation labels are available. Both quantitative and qualitative evaluations are performed on a dataset covering 24km2area of Osaka in Japan validates the feasibility of the proposed method for densification of point cloud in large scale environment.

We present a lightweight post-processing method to refine the semantic segmentation results of point cloud sequences. Most existing methods usually segment frame by frame and encounter the inherent ambiguity of the problem: based on a measurement in a single frame, labels are sometimes difficult to predict even for humans. To remedy this problem, we propose to explicitly train a network to refine these results predicted by an existing segmentation method. The network, which we call the P2Net, learns the consistency constraints between "coincident" points from consecutive frames after registration. We evaluate the proposed post-processing method both qualitatively and quantitatively on the SemanticKITTI dataset that consists of real outdoor scenes. The effectiveness of the proposed method is validated by comparing the results predicted by two representative networks with and without the refinement by the post-processing network. Specifically, qualitative visualization validates the key idea that labels of the points that are difficult to predict can be corrected with P2Net. Quantitatively, overall mIoU is improved from 10.5% to 11.7% for PointNet [1] and from 10.8% to 15.9% for PointNet++ [2].

We propose a neural style transfer method for colored point clouds which allows stylizing the geometry and/or color property of a point cloud from another. The stylization is achieved by manipulating the content representations and Gram-based style representations extracted from a pretrained PointNet-based classification network for colored point clouds. As Gram-based style representation is invariant to the number or the order of points, the style can also be an image in the case of stylizing the color property of a point cloud by merely treating the image as a set of pixels. Experimental results and analysis demonstrate the capability of the proposed method for stylizing a point cloud either from another point cloud or an image.

This paper presents a novel method for rare event detection from an image pair with class-imbalanced datasets. A straightforward approach for event detection tasks is to train a detection network from a large-scale dataset in an end-to-end manner. However, in many applications such as building change detection on satellite images, few positive samples are available for the training. Moreover, an image pair of scenes contains many trivial events, such as in illumination changes or background motions. These many trivial events and the class imbalance problem lead to false alarms for rare event detection. In order to overcome these difficulties, we propose a novel method to learn disentangled representations from only low-cost negative samples. The proposed method disentangles the different aspects in a pair of observations: variant and invariant factors that represent trivial events and image contents, respectively. The effectiveness of the proposed approach is verified by the quantitative evaluations on four change detection datasets, and the qualitative analysis shows that the proposed method can acquire the representations that disentangle rare events from trivial ones.

Various saliency detection algorithms from color images have been proposed to mimic eye fixation or attentive object detection response of human observers for the same scenes. However, developments on hyperspectral imaging systems enable us to obtain redundant spectral information of the observed scenes from the reflected light source from objects. A few studies using low-level features on hyper-spectral images demonstrated that salient object detection can be achieved. In this work, we proposed a salient object detection model on hyperspectral images by applying manifold ranking (MR) on self-supervised Convolutional Neural Network (CNN) features (high-level features) from unsupervised image segmentation task. Self-supervision of CNN continues until clustering loss or saliency maps converges to a defined error between each iteration. Finally, saliency estimations is done as the saliency map at last iteration when the self-supervision procedure terminates with convergence. Experimental evaluations demonstrated that proposed saliency detection algorithm on hyperspectral images is outperforming state-of-the-arts hyperspectral saliency models including the original MR based saliency model.

The building change detection, which is important for monitoring human activity in urban and sub-urban area, can be done by the using of remote sensing data. To overcome the cloud cover problem that cause the limited utilizing in optical images which is repeatedly occurred in tropical areas, we decided to use synthetic aperture radar (SAR) data, a kind of remote sensing data that does not get affected by weather condition, to fulfill this change detection purpose. In this study, Bangkok, Thailand were selected as our study area in order to demonstrate that the SAR data can be used in the area that optical data cannot be used such as tropical areas like in Thailand. As our target is to detect changes of buildings, so we have to consider separating the change that cause by seasonal and other non-target objects. To complete this purpose, we decided to use one of the deep learning techniques called U-net which is created for the image segmentation task. We have created the ground truth and used one portion for training the network and another portion for validate the result. Finally, the model that trained by our training data was able to provide promising results.

ASTER, which is a multi-band pushbroom sensor onboard Terra, has provided worthful multiband images for approximately 20 years successfully since Terra's launch in 1999. For such a long operation, maintaining accurate radiometric calibration is an important challenge to guarantee the reliability of the satellite products. In April 2003 and August 2017, ASTER observed the Moon (and deepspace) for conducting radiometric calibration with the Moon, which is called as lunar calibration. Through the lunar calibration, temporal variation of relative sensor sensitivity can be measured accurately (the order of 0.1 %), which is reasonably accurate to improve calibration results from other methods. The lunar calibration was performed to ASTER's visible and near infrared bands (Band 1, Band 2, and Band 3N) by comparing the observed brightness with simulated lunar brightness. 3, 5, and 6% sensitivity degradation were observed in Band 1, Band 2, and Band 3N of ASTER, respectively, which are basically consistent with vicarious calibration, though still further discussion is needed for combining calibration results from different methods.

Hyperspectral Imager Suite (HISUI) is a future spaceborne hyperspectral Earth imaging system being developed by Japanese Ministry of Economy, Trade, and Industry (METI). HISUI will be launched and deployed on International Space Station (ISS) for two - three year operation from 2020. Manufacturing and testing of Hyperspectral sensor were completed. Integration and test of HISUI Exposed Payload System is being conducted. HISUI data policy and research announcement as well as synergy with other earth observing instruments onboard ISS are also being discussed.

Three-dimensional (3D) Semantic segmentation of aerial derived point cloud aims at assigning each point to a semantic class such as building, tree, road, and so on. Accurate 3D-segmentation results can be used as an essential information for constructing 3D city models, for assessing the urban expansion and economical condition. However, the fine-grained semantic segmentation is a challenge in high-resolution point cloud due to irregularly distributed points unlike regular pixels of image. In this demonstration, we present a case study to apply PointNet, a novel deep learning network, to outdoor aerial survey derived point clouds by considering intensity (depth) as well as spectral information (RGB). PointNet was basically designed for indoor point cloud data based on the permutation invariance of 3D points. We firstly fuse two surveying datasets of Light Detection and ranging (LiDAR) and aerial images for generating multi-sourced aerial point clouds (RGB-DI). Then, each point of fused data is classified into a semantic class of ordinary building, public facility, apartment, factory, transportation network, park, and water by reworking PointNet. The result of our approach by using deep learning shows about 0.88 accuracy and 0.64 F-measure of semantic segmentation with the RGB-DI data we have fused. It outperforms a Support Vector Machine(SVM) approach based on geometric features of linearity, planarity, scattering, and verticality of a set of 3D points.

In this paper, we propose a method for the translation from Synthetic Aperture Radar (SAR) to optical images using conditional Generative Adversarial Networks (cGANs). Satellite images have been widely utilized for various purposes, such as natural environment monitoring (pollution, forest or rivers), transportation improvement and prompt emergency response to disasters. However, the obscurity caused by clouds leads to unstable monitoring of the ground situation while using the optical camera. Images captured by a longer wavelength are introduced to reduce the effects of clouds. In particular, SAR images are known to be nearly unaffected by clouds and are often used for stably observing the ground situation. On the other hand, SAR images have lower spatial resolution and visibility than optical images. Therefore, we propose a deep neural network that generates optical images from SAR images. Finally, we confirm the feasibility of the proposed network on a dataset consisting of optical images and the corresponding SAR images.

UNIFORM-1 is a small satellite that helps in the prevention of severe fire hazards by detecting the wildfire at an early stage. UNIFORM-l is equipped with an un-cooled bolometer imager (BOL), which observes temperature distributions of the earth surface. Uniquely, BOL is not equipped with any shutter mechanism to reduce the risk associated with the shutter that impacts the ability to conduct any observation in space. As a trade-off of shutter exclusion, BOL saw the Sun in its field of view (FOV) many times and the sunlight damage caused large brightness temperature error (60 K) in BOL images. To reduce the error, we have proposed a new calibration procedure utilizing deepspace observations, which enables the measurement of the increase in digital count offset due to the sunlight in a BOL image. After the correction, we successfully reduced the damage, and the relative accuracy of brightness temperature improved to more than 2 K, which is enough to detect a wildfire with a size smaller than one-pixel scale.

Hyperspectral Imager Suite (HISUI) is a future spaceborne hyperspectral Earth imaging system being developed by Japanese Ministry of Economy, Trade, and Industry (METI). HISUI will be launched and deployed on International Space Station (ISS) for two - three year operation from 2020. Manufacturing and testing of HISUI Flight Model were almost completed, and design of HISUI Exposed Payload System is being conducted. HISUI data policy and research announcement as well as synergy with other earth observing instruments onboard ISS are also being discussed.

This paper presents an efficient object detection method from satellite imagery. Among a number of machine learning algorithms, we proposed a combination of two convolutional neural networks (CNN) aimed at high precision and high recall, respectively. We validated our models using golf courses as target objects. The proposed deep learning method demonstrated higher accuracy than previous object identification methods.

Land cover types classification and investigating the temporal changes are considered as the common application of remote sensing. Water body classification is one of the most basic classification tasks which analyze the occurrence of water on the earth surface. However, common remote sensing practices such as thresholding, spectral analysis, and statistical approaches alone are not sufficient to produce globally reliable water classification. Therefore, this research developed a formula which could effectively classify water in a global scale. Further improvements to the classification applied by developing an optimal threshold generation method, hill-shade, Volcanic Soil Mask (VSM) etc. The results are showing significant improvements compared to previous researches.

Many works have been done on salient object detection using supervised or unsupervised approaches on colour images. Recently, a few studies demonstrated that efficient salient object detection can also be implemented by using spectral features in visible spectrum of hyperspectral images from natural scenes. However, these models on hyperspectral salient object detection were tested with a very few number of data selected from various online public dataset, which are not specifically created for object detection purposes. Therefore, here, we aim to contribute to the field by releasing a hyperspectral salient object detection dataset with a collection of 60 hyperspectral images with their respective ground-truth binary images and representative rendered colour images (sRGB). We took several aspects in consideration during the data collection such as variation in object size, number of objects, foreground-background contrast, object position on the image, and etc. Then, we prepared ground truth binary images for each hyperspectral data, where salient objects are labelled on the images. Finally, we did performance evaluation using Area Under Curve (AUC) metric on some existing hyperspectral saliency detection models in literature.

A wheeled inverted pendulum cannot balance without control, and it is important to save motion in a control range. In some situations, however, to avoid a collision, the maximum acceleration and deceleration must be applied. A motion-planning method for a wheeled inverted pendulum (“body” hereafter)-which uses full performance from all attitudes under hardware constraints-was developed. The developed method involves two steps. First, a linear combination of zeroth to fourth differentials of parameters based on the motion equation of a wheeled inverted pendulum is obtained. Second, the arbitrary attitude of the wheeled inverted pendulum and hardware constraints are expressed on a phase plane containing angular rate of body motion and an angular inclination of the body. By executing these two steps, a motion plan can be expressed on the phase plane. A simulation based on a model confirmed that the movement plan formulated by this technique can be followed with no error.

We demonstrate how to utilize a two-dimensional image sensor onboard a satellite for determining its attitude. The method is based on image matching between satellite images and Earth surfaces. To achieve this, image feature extraction and robust estimation techniques are employed. Experimental results showed that the accuracy of attitude determination is about 0.02° if the satellite position has been precisely determined. The contribution of this paper is not on technical novelty, as the method has already been described in [1], but on additional examples illustrating the utility of the method.

We developed a simple algorithm for detecting hot spots using Sentinel-2A data, modifying a method for Landsat 8 data. The empirical hot spot detection equation was developed using apparent spectral reflectances at band 8a (0.87 μm) and band 12 (2.2 μm). Due to the improved spatial resolution, false detections often occur in inclined flat surfaces, such as roofs and solar panels. To avoid such false detection, we defined a new detection equation with empirical thresholds additionally using an apparent spectral reflectance at band 11 (1.6 μm) based on typical spectral characteristics of surface materials and the blackbody assumption. The proposed algorithm successfully reduced the occurrence of false detection in urban areas.

In recent years, more and more small satellites have been launched and operated for various purposes. Because of their sever restrictions of payload weight and cost, a radiometric calibration approach which does not need any special instrument has been desired. In this study, we demonstrate a radiometric calibration approach for Hodoyoshi-1, a Japanese small satellite, based on Moon observations utilizing SLENE/SP Lunar surface reflectance model. The model can simulate any satellite Moon observation considering observation geometry, and can be used for evaluating the consistency of the observed Moon brightness, which allows us to measure a time variation of a sensor sensitivity. Through Hodoyoshi-1's three Moon observations, gradual increasing in relative sensor sensitivities were confirmed.

Hyperspectral Imager Suite (HISUI) is a future spaceborne hyperspectral Earth imaging system being developed by Japanese Ministry of Economy, Trade, and Industry (METI). HISUI will be launched and deployed on International Space Station (ISS) for three year operation from 2019. In FY2016, manufacturing and testing of HISUI Flight Model and design of HISUI Exposed Payload System were conducted. HISUI data policy and research announcement as well as synergy with other earth observing instruments onboard ISS are also being discussed.

The Earth observation satellites have been monitoring the earth's surface for a long time, and the images taken by the satellites contain large amounts of valuable data. However, it is extremely hard work to manually analyze such huge data. Thus, a method of automatic object detection is needed for satellite images to facilitate efficient data analyses. This paper describes a new image feature extended from higher-order local autocorrelation to the object detection of multispectral satellite images. The feature has been extended to extract spectral inter-relationships in addition to spatial relationships to fully exploit multispectral information. The results of experiments with object detection tasks conducted to evaluate the effectiveness of the proposed feature extension indicate that the feature realized a higher performance compared to existing methods.

The thermal infrared camera with the uncooled microbolometer array based on commercial products has been developed in a laboratory of a Japanese university and mounted to a 50-kg class small satellite specialized for discovering wildfire. It has been launched in 2014 and successfully detected considerable hotspots not only wildfire but also volcanoes. Brightness temperature derived from observation has been verified, and the scale of observed wildfire has been provisionally presumed; the smallest wildfire ever detected has a flame zone less than ~300 m2and the fire radiative power = ~35.4 mW. It is 1/30th the size of the initial requirement estimated in the design process. Our thermal infrared camera developed in a short time with low cost has attained enough ability to discover small wildfire which is suppressive at initial attack.

We develop a fully automated algorithm for determining the geological ages by crater counting from the digital terrain model (DTM) and the digital elevation model (DEM) taken by remote-sensing observations. The algorithm is based on the rotational pixel swapping method, which uses a multiplication operation between the original DTM/DEM data and the rotated data to detect impact craters. Our method does not need binarization and/or noise reduction, because noise components are automatically erased. We show that our method can detect not only simple craters but also complex circular structures such as imperfect, degraded, or overlapping craters. We demonstrate that this method succeeds in the automatic detection of hundreds to thousands of impact craters, and the estimated ages are consistent with those by manual counting in previous works. In addition, it is shown that the calculation time by this method is more than several hundred times faster than by previous methods.

We present an approach for the detection of buildings in multispectral satellite images. Unlike 3-channel RGB images, satellite imagery contains additional channels corresponding to different wavelengths. Approaches that do not use all channels are unable to fully exploit these images for optimal performance. Furthermore, care must be taken due to the large bias in classes, e.g., most of the Earth is covered in water and thus it will be dominant in the images. Our approach consists of training a Convolutional Neural Network (CNN) from scratch to classify multispectral image patches taken by satellites as whether or not they belong to a class of buildings. We then adapt the classification network to detection by converting the fully-connected layers of the network to convolutional layers, which allows the network to process images of any resolution. The dataset bias is compensated by subsampling negatives and tuning the detection threshold for optimal performance. We have constructed a new dataset using images from the Landsat 8 satellite for detecting solar power plants and show our approach is able to significantly outperform the state-of-the-art. Furthermore, we provide an indepth evaluation of the seven different spectral bands provided by the satellite images and show it is critical to combine them to obtain good results.

Hyperspectral Imager Suite (HISUI) is a future spaceborne hyperspectral Earth imaging system being developed by Japanese Ministry of Economy, Trade, and Industry (METI). HISUI project is currently being promoted by three organizations each of which has a contract with METI together with several scientists from universities and national research institutes. Recently, it was decided to deploy HISUI on International Space Station. The current status of HISUI project will be introduced in the presentation.

We propose a new automatic method called the rotational pixel swapping (RPSW) method to detect circular features in binary images of remote sensing images. The method is based on a multiplication operation between the original image and the rotated images. We show that the RPSW selectively enhances rotational symmetric patterns and weakens nonrotational symmetric patterns, including noise components, without any noise reduction processes. The method can detect not only simple circles but also more complex circular features such as incomplete ring structures or several concentric rings. Furthermore, we demonstrate that the RPSW provides the stable detection of circular features such as terrestrial impact structures, which are irregular imperfect circular shapes, in binary images based on Earth-observation satellite images. The RPSW would provide a potential method of future surveys or statistical studies using huge data sets of multiband or hyperspectral images obtained by Earth-observation satellites.

The Spectral Profiler (SP) is a visible-near infrared spectrometer onboard the Japanese Selenological and Engineering Explorer (SELENE), which was launched in 2007 and observed the Moon until June 2009. The SP consists of two gratings and three linear-array detectors: VIS (0.5-1.0 μm), NIR 1 (0.9- 1.7 μm), and NIR 2 (1.7-2.6 μm). In this paper, we propose a new method for radiometric calibration of NIR 2, specifically for the dark output (background) estimate, which is different from the previous method used for VIS and NIR 1. We show that the reflectance spectra of NIR 2 derived from the new radiometric calibration show less noise than those of the previous method. Based on an analysis of the reflectance spectra at exposure sites of the end-member minerals on the lunar surface, we demonstrated that the spectral features of the 2-μm band in the NIR 2 spectra are consistent with those expected from the minerals inferred from the features of the 1-μm band in the VIS and NIR 1 spectra. Finally, we examined the repeatability of the radiometric calibration of NIR 2 using the SP data near the Apollo 16 landing site observed at four different times. The typical difference in the reflectance at wavelengths <;~2.1 μm was a few percent, which is within the uncertainty due to the error in the background estimate, suggesting that there was no significant change in the sensitivity of NIR 2 over the mission period.

This paper proposes a new static simultaneous optimization of dispatching and conflict-free routing for unidirectional Overhead Hoist Transport (OHT) systems. Our objective is to minimize the entire transportation time where the given transportation tasks are finished. A hybrid system modeling is applied for OHT system that is constituted by the OHT vehicles at the lower level and governed by the upper level scheduler. In the proposed modeling, it is clarified that the simultaneous optimization problems are recast as a binary integer linear problem by using model predictive control. This paper presents a numerical example and discusses limitation of proposed modeling.

We have developed a method for radiometric calibration of HISUI's hyper and multi-spectral sensors using a lunar reflectance model developed from SELENE SP data, which involves lunar surface reflectance and photometric properties. For evaluating the utilization of the model, we simulated a lunar observation conducted by ASTER of its three visible and infrared bands and confirmed the model describes lunar surface photometric properties correctly because correlation coefficients of observed and modeled radiance exceed 0.99 for all bands. Although absolute radiance shows some discrepancy between the observed and the simulated Moon in visible band, the model is, at least, useful to evaluate relative degradation of sensors.

Hyperspectral Imager Suite (HISUI) is a future spaceborne hyperspectral and multispectral Earth imaging system being developed by Japanese Ministry of Economy, Trade, and Industry (METI). HISUI project is currently being promoted by three organizations each of which has a contract with METI together with several scientists from universities and national research institutes. The current status of HISUI project will be introduced in the presentation.

A time optimal control algorithm for quadruped robots is developed and its effectiveness is verified by experiments and simulations. The derivation of the optimal solution consists of two steps that are the speeding up of supporting legs and that of swinging legs. The way of the speeding up of swinging legs is very similar to that of a two link manipulator developed by Bobrow. In this paper, first, a method of using torque redundancy of the robot is proposed for speeding up of supporting legs with the constraints due to ZMP, the power limits of joint actuators and the limits of available friction forces. Then, an algorithm for obtaining the fastest walking pattern in a trot gait period is developed by combining the result of the supporting legs and that of the swinging legs. The obtained walking pattern is installed in a quadruped robot SONY ERS-7 and its effectiveness is verified by experiments.

The present paper describes the cross calibration of the Remote Sensing Instrument (RSI) onboard Formosat-2 using the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) onboard Terra as a reference, which has been continuously calibrated by means of the onboard and vicarious calibration methods. The data integration system for multi-satellite sensor databases on the Global Earth Observation Grid (GEO Grid) is used in order to find pairs of images acquired by both satellite optical sensors over the radiometric calibration sites on the same days. Radiative transfer simulations that consider different imaging conditions using the Second Simulation of a Satellite Signal in the Solar Spectrum (6S) code are performed based on the Moderate-resolution Imaging Spectroradiometer (MODIS) atmosphere and land products, and the optimum methodology for this cross calibration is discussed under the assumption of no ground-based measurements. The derived calibration coefficients are normalized by the values of preflight calibration and are compared with the results of in-flight cross calibration by the Centre National d'Études Spatiales (CNES) and vicarious calibration described in our previous paper. The average degradations of sensor sensitivity since preflight calibration until 2009 are estimated to be 10% (Band2), 11% (Band3), and 8% (Band4). The derived long-term degradation trends are almost consistent with the results of our vicarious calibration within the precision. The cross-calibration methodology used in the present paper facilitates the in-flight radiometric calibration of satellite optical sensors.

The hyperspectral and multispectral remote sensing mission named HISUI is the Japanese next-generation Earth observation project that will be onboard ALOS-3. HISUI will be composed of hyperspectral imager (185 spectral bands in VNIR-SWIR region with 30 m spatial resolution) and multispectral imager (4 spectral bands in VNIR region with 5 m spatial resolution). To expand use of Earth observation data from HISUI, quality assurance and control of data products is indispensable, therefore, the long-term radiometric calibration has a crucial role. The objective of this research is to establish the techniques and develop the plans for conducting the in-flight radiometric calibration of HISUI with high frequency, reliability, and stability, based on the traditional in-flight radiometric calibration for multispectral sensors.

The Spectral Profiler (SP) is a visible-near infrared spectrometer on board the Japanese Selenological and Engineering Explorer, which was launched in 2007 and observed the Moon until June 2009. The SP consists of two gratings and three linear-array detectors: VIS (0.5-1.0 μm ), NIR 1 (0.9-1.7 μm), and NIR 2 (1.7-2.6 μm). In this paper, we characterize the radiometric and spectral properties of VIS and NIR 1 using in-flight observational data as well as preflight data derived in laboratory experiments using a calibrated integrating sphere. We also proposed new methods for radiometric calibration, specifically methods for nonlinearity correction, wavelength correction, and the correction of the radiometric calibration coefficients affected by the water vapor. After all the corrections, including the photometric correction, we obtained the reflectance spectra for the lunar surface. Finally, we examined the stability of the SP using the SP data near the Apollo 16 landing site observed at four different times. The difference in reflectance among these four observations was less than ~ ±1% for most of the bands, suggesting that the degradation of the SP is not significant over the mission period.

The ASTER on-demand processing service, which provides a Digital Elevation Model (DEM) and orthorectified images was developed and deployed on the GEO Grid system. It is designed to support the latest algorithms for radiometric and atmospheric corrections developed by researchers as well as the geometric correction and other DEM processing options. The functions and options in this service are developed and implemented as modules, so that they can be arranged as the user requires. Although the system is an experimental, it can provide higher quality data sets than the standard products.

In the IEEE802.11n WLAN standard, orthogonal frequency division multiplexing (OFDM) modulation is employed. Diversity techniques are implemented to overcome multipath fading in the OFDM systems. A fractional sampling (FS) scheme is one of the diversity techniques with a single antenna. This scheme also can be applied in a MIMO-OFDM system to increase its capacity. In this paper, the effect of FS in a WLAN system following the IEEE802.11n standard is investigated through experiments. Numerical results through the experiments indicate that diversity gain through FS can be obtained in the NLOS conditions.

We describe the results of the in-orbit radiometric calibration of the remote sensing instrument (RSI) onboard Formosat-2, based on the ground data collected by the advanced spaceborne thermal emission and reflection radiometer (ASTER) calibration team at Ivanpah Playa and Railroad Valley during the regular field campaigns in 2006 and 2008. These results are normalized by the preflight values provided by Astrium, France, and are compared with the results of in-flight calibrations made by Centre National d'Etudes Spatiales, France. The radiometric calibration coefficients and the long-term degrading trend of RSI are derived from the results of vicarious calibration, which estimate that the degradations of sensor sensitivity until 2008 are 2.2% (B4), 8.0% (B3), 9.6% (PAN), 10.3% (B2), and 12.0% (B1). Considering the fact that the ASTER calibration team is able to achieve an accuracy of vicarious calibration better than 5%, we estimate a 7.3% to 13.4% overestimate would be incurred by using the existing in-flight cross calibration coefficients to calculate the spectral radiances. This paper supports the use of vicarious calibration as a reliable approach for the in-orbit radiometric calibration of RSI onboard Formosat-2 on a regular basis.

We consider grammar based text compression with longest-first substitution, where non-overlapping occurrences of a longest repeating substring of the input text are replaced by a new non-terminal symbol. We present a new text compression algorithm by simplifying the algorithm presented in S. Inenaga et al., (2003). We give a new formulation of the correctness proof introducing the sparse lazy suffix tree data structure. We also present another type of longest-first substitution strategy that allows better compression. We show results of preliminary experiments comparing grammar sizes of the two versions of the longest-first strategy and the most frequent strategy