A Micro-vibration Measurement Method of High-Resolution Space Camera Based on Target Image

Assessment and monitoring of forest structure is frequently done with absolute density measures with field forest inventory data and expansion methods. The development of basal area and the number of trees estimation functions with data derived from very high spatial resolution satellite images enable their short-term and cost-effective evaluation, allowing also the estimation for the area not requiring extrapolation methods. The functions of basal area and the number of trees per hectare are based on crown cover obtained with very high spatial resolution satellite images for two evergreen oaks and umbrella pine. The three tree species are especially important in the agroforestry systems of the Mediterranean region. The linear functions fitted for pure stands of the three species and mixed stands of cork and holm oak and of cork oak and umbrella pine showed a better performance for basal area than for the number of trees per hectare. The inclusion of dummy variables for species composition improved the accuracy of the functions.

Case instance segmentation of small farmland based on Mask R-CNN of feature pyramid network with double attention mechanism in high resolution satellite images

Agricultural intensification and efficient use and targeting of fertilizer inputs on smallholder farms is key to sustainably improve food security. The objective of this paper is to demonstrate how high-resolution satellite and unmanned aerial vehicle (UAV) images can be used to assess the spatial variability of yield, and yield response to fertilizer. The study included 48 and 50 smallholder fields monitored during the 2014 and 2015 cropping seasons south-east of Koutiala (Mali), cropped with the five major crops grown in the area (cotton, maize, sorghum, millet and peanuts). Each field included up to five plots with different fertilizer applications and one plot with farmer practice. Fortnightly, in-situ in each field data were collected synchronous with UAV imaging using a Canon S110 NIR camera. A concurrent series of very high-resolution satellite images was procured and these images were used to mask out trees. For each plot, we calculated vegetation index means, medians and coefficients of variation. Cross-validated general linear models were used to assess the predictability of relative differences in crop yield and yield response to fertilizer, explicitly accounting for the effects of fertility treatments, between-field and within-field variabilities. Differences between fields accounted for a much larger component of variation than differences between fertilization treatments.Vegetation indices from UAV images strongly related to ground cover (R2 = 0.85), light interception (R2 = 0.79) and vegetation indices derived from satellite images (R2 values of about 0.8). Within-plot distributions of UAV-derived vegetation index values were negatively skewed, and within-plot variability of vegetation index values was negatively correlated with yield. Plots on shallow soils with poor growing conditions showed the largest within-plot variability. GLM models including UAV derived estimates of light interception explained up to 78% of the variation in crop yield and 74% of the variation in fertilizer response within a single field. These numbers dropped to about 45% of the variation in yield and about 48% of the variation in fertilizer response when lumping all fields of a given crop, with Q2 values of respectively 22 and 40% respectively when tested with a leave-field-out procedure. This indicates that remotely sensed imagery doesn’t fully capture the influence of crop stress and management. Assessment of crop fertilizer responses with vegetation indices therefore needs a reference under similar management. Spatial variability in UAV-derived vegetation index values at the plot scale was significantly related to differences in yields and fertilizer responses. The strong relationships between light interception and ground cover indicate that combining vertical photographs or high-resolution remotely sensed vegetation indices with crop growth models allows to explicitly account for the spatial variability and will improve the accuracy of yield and crop production assessments, especially in heterogeneous smallholder conditions.

Spatio-temporal analysis of Kilim River using Very High-Resolution (VHR) satellite image between the years of 2005 and 2012 are vital to assess river morphological changes and erosion detection over the specific time duration. Therefore, this study utilizes remote sensing and Geographical Information System (GIS) capabilities to identify the channel migration and shifting as well as to calculate the rate of erosion and accretion. Classification of the river from Quickbird and WorldView-2 images is carry out using Maximum Likelihood Classification (MLC) technique whereas feature selection tool performs to extract river feature from the raw image. Subsequently, river feature been overlaid to identify the changes of riverbank pattern and to calculate the rate of riverbank erosion in terms of area. The result shows the total area of erosion is 27252.959 m2 and the total area of accretion is 6079.999 m2. A maximum erosion of 6993.102 m2 detected at Section E while maximum accretion of 681.026 m2 spotted at Section D. The rates of riverbank erosion are 3406.62 m2/year while the rates for accretion are 759.999 m2/years between 2005 and 2012. Hence, the output of this study enables Langkawi Development Authority (LADA) and other stakeholders to recognize the specific location, which severely affected by erosion and accretion as well as to spot the area experienced huge river channel shifting.

Semantic segmentation of remote sensing images enables in particular land-cover map generation for a given set of classes. Very recent literature has shown the superior performance of deep convolutional neural networks (DCNN) for many tasks, from object recognition to semantic labelling, including the classification of Very High Resolution (VHR) satellite images. However, while plethora of works aim at improving object delineation on geographically restricted areas, few tend to solve this classification task at very large scales. New issues occur such as intra-class class variability, diachrony between surveys, and the appearance of new classes in a specific area, that do not exist in the predefined set of labels. Therefore, this work intends to (i) perform large scale classification and to (ii) expand a set of land-cover classes, using the off-the-shelf model learnt in a specific area of interest and adapting it to unseen areas.

ABSTRACTAutomatic building extraction from High-Resolution Satellite (HRS) image has been an important field of research in the area of remote sensing. Different techniques related to radiometric, geometric, edge detection and object based have already been discussed and used by various researchers for building extraction. However, faithfulness of extraction is highly dependent on user intervention. This study proposes a novel morphological based automatic approach for extraction of buildings using HRS image. Moreover, using such an automatic approach, buildings can be detected having different size and shape. The proposed technique integrates morphological Top-hat filter, and K-means algorithm to extract buildings having bright and dark rooftops. Further, extracted bright and dark rooftop building segments have been combined together to obtain the final output that contains final extracted building segments. In order to eliminate false-detected buildings, different parameters like area, eccentricity, and axis ratio (major/minor axis) have been used. The suitability of the technique has been judged using different indicators such as completeness, correctness and quality.

With the growing number of high-resolution satellite images, the traditional image retrieval method has become a bottleneck in the massive application of high-resolution satellite images because of the low degree of automation. However, there are few studies on the automation of satellite image retrieval. This paper presents an automatic high-resolution satellite image accurate retrieval method based on effective coverage (EC) information, which is used to replace the artificial screening stage in traditional satellite image retrieval tasks. In this method, first, we use a convolutional neural network to extract the EC of each satellite image; then, we use an effective coverage grid set (ECGS) to represent the ECs of all satellite images in the library; finally, the satellite image accurate retrieval algorithm is proposed to complete the process of screening images. The performance evaluation of the method is implemented in three regions: Wuhan, Yanling, and Tangjiashan Lake. The large number of experiments shows that our proposed method can automatically retrieve high-resolution satellite images and significantly improve efficiency.

Space cameras play a pivotal role in various fields, such as astronomical exploration. When operating in orbit, these cameras encounter the relative motion between the target and the camera during the exposure, resulting in image motion, which affects the imaging quality. Therefore, it is necessary to compensate for this image motion. This paper investigates the in-orbit image motion compensation (IMC) method for space cameras with a long integration time based on a two-axis pointing platform. Firstly, the mechanical design of the camera is introduced. Secondly, the in-orbit IMC model for the camera is analyzed, and the angular motion needed for compensating for the image motion by the two-axis pointing platform are derived. Factors influencing the compensation accuracy are also analyzed. The effectiveness of the IMC model is verified through simulations. Finally, the in-orbit experimental results indicate that the energy concentration of the target star images obtained exceeded 70%, demonstrating excellent performance in space cameras and effectively enhancing imaging quality using IMC technology.

세계 각국은 우주개발에 대한 많은 관심으로 인하여 자국의 지구관측위성을 발사하고 있다. 이로 인해 고해상도 위성영상은 날로 증가하고 있다. 또한 웹 환경의 발달로 인하여 일반 사용자는 위성영상을 쉽게 접하고 있다. 본 연구에서는 위성영상의 활용성을 높이고자 위성의 센서모델링에 대한 해박한 지식이 다소 없더라도 쉽게 3차원 좌표를 획득할 수 있도록 웹 기반 위성사진측량에 대한 기초연구를 수행하였다. 웹 환경에서 고해상도 위성영상을 이용하여 3차원 좌표를 결정하기 위한 여러 요소기술을 분석하고 IKONOS 위성영상을 이용하여 사례연구를 수행하였다. 센서모델링 방법중의 하나인 평행투영모델을 적용하여 에피폴라 영상을 생성하고 웹 환경에서 3차원 위치를 결정할 수 있는 프로토타입 형태의 프로그램을 구현하였다. 이를 통해 일반 사용자도 쉽게 웹 환경에서 3차원 위치를 결정할 수 있었고 기 구축된 공간정보의 갱신과 서비스 분야에 활용될 수 있음을 알았다. Many nations have been launched their own earth observation satellites due to much interest in space exploration. Because of this, the number of high resolution satellite are increasing day by day. With the development of web environment, general users have easy access to satellite images. The research conducted a basic study about web-based satellite photogrammetry in order to determine three dimensional coordinates easily without having profound knowledge of satellite sensor modeling. Various element technologies were analyzed to decide three dimensional coordinates by using high resolution satellite images in web environment, and case studies were conducted by using IKONOS satellite images. By applying parallel projection model, which is one of sensor modeling methods, epipolar image was created and prototype form of program that can decide three dimensional location in web environment was implemented. Through this, general users could decide three dimensional location easily in web environment and knew that already existed spatial information can be renewed and used in service area.

The aim of this paper is to present a detailed step by step method for classification of high resolution satellite imagery into defined classes such as high dense vegetation, water, wet land, Agricultural land etc., using fuzzy rule set. In this area, object based analysis is used for image classification. An Objectoriented approach isbased on the segmentation that is being followed for information extraction of variety of thematic information from high-resolution satelliteimagery. In this method, fuzzy rule set is defined for every class based on the class hierarchy; every defined class is assigned associated segments with high similarity degree, using eCognition Software. Object-oriented classification rule set provide a complete accurate and efficient method of information extraction. In compared the object-oriented with traditional pixel-based classification and proved that object-oriented method has provided high accuracy classification. Nearest Neighbor (NN) classification method was used to discriminate the land cover classes. The overall accuracy is obtained 99.99% and kappa coefficient obtained 99% for the satellite image of Landsat-8. Keyword: Classification, eCognition, Fuzzy rule based, High resolution satellite image, Segmentation.

Western U.S. rangelands have been quantified as six fractional cover (0%–100%) components over the Landsat archive (1985–2018) at a 30 m resolution, termed the “Back-in-Time” (BIT) dataset. Robust validation through space and time is needed to quantify product accuracy. Here, we used field data collected concurrently with high-resolution satellite (HRS) images over multiple locations (n = 42) and years. Field observations were used to train regression tree models, predicting the component cover across each HRS image. Our objectives were to evaluate the spatial and temporal relationships between HRS and BIT component cover and compare spatio-temporal climate responses. First, for each HRS site-year (n = 77) we averaged both the HRS and BIT predictions within each site separately and regressed the averages to quantify the temporal accuracy. Next, we regressed individual pixel values of corresponding HRS and BIT predictions to quantify the spatio-temporal accuracy. Results showed strong temporal correlations with an average R2 of 0.63 and Root Mean Square Error (RMSE) of 5.47% as well as strong spatio-temporal correlations with an average R2 of 0.52 and RMSE of 7.89% across components. Our approach increased the validation sample size relative to direct comparison of field observations. Validation results showed robust spatio-temporal relationships between HRS and BIT data, providing increased user confidence in the data.

This paper presents the comparison of two different algorithms for detection and counting of individual tree crown from high resolution satellite images. Trees can be detected from the high resolution satellite images using the different algorithms like K-means clustering, Circle Unification, Watershed segmentation and Normalized Difference Vegetation Index (NDVI). In this paper Watershed segmentation algorithm and NDVI performance are improved for the detection and counting the trees in the satellite images. The fundamental process of algorithms and results are compared to find the better approach for the detection of the trees.Keywords: NDVI, Satellite Images, Trees, Watershed Segmentation

A gully erosion assessment model for the Chinese Loess Plateau based on changes in gully length and area

ABSTRACTThe earth surface changes continuously due to the natural causes and ‎human activities. New generations of satellite sensors, such as WorldView and ‎GeoEye, provide new data to better delineate, track and visualise changes in land ‎cover. A number of classes are used in the satellite images. All artefacts with ‎elevations greater than the ground surface (buildings in particular) may appear in ‎a wrong location. The correction of buildings position is an important task for ‎mapping applications. The main aim of this study is to introduce a change ‎detection approach using very high resolution satellite images (VHR) for map ‎updating. In this approach, an approximated method for building relief ‎displacement correction was developed.‎In this paper, image preprocessing was carried out and information content ‎of the satellite image was evaluated. Then change detection between GeoEye-1 ‎image and Sohag map was carried out using post-classification comparison ‎technique. After that the change map result was divided into two classes: building ‎and non-building. All objects were transformed from raster to vector format. For ‎building objects, the height was estimated. A python code was written to calculate ‎relief displacement using buildings height and shadow length. The vector layer ‎was added to update the reference map. The results showed the ability of very ‎high-resolution satellites images for updating large scale maps in Egypt. Also, the ‎approximated method for building relief displacement correction is a promising ‎method. It has RMSE accuracy of 0.95m.‎

EffCDNet: Transfer learning with deep attention network for change detection in high spatial resolution satellite images