459 research outputs found
Image Simulation in Remote Sensing
Remote sensing is being actively researched in the fields of environment, military and urban planning through technologies such as monitoring of natural climate phenomena on the earth, land cover classification, and object detection. Recently, satellites equipped with observation cameras of various resolutions were launched, and remote sensing images are acquired by various observation methods including cluster satellites. However, the atmospheric and environmental conditions present in the observed scene degrade the quality of images or interrupt the capture of the Earth's surface information. One method to overcome this is by generating synthetic images through image simulation. Synthetic images can be generated by using statistical or knowledge-based models or by using spectral and optic-based models to create a simulated image in place of the unobtained image at a required time. Various proposed methodologies will provide economical utility in the generation of image learning materials and time series data through image simulation. The 6 published articles cover various topics and applications central to Remote sensing image simulation. Although submission to this Special Issue is now closed, the need for further in-depth research and development related to image simulation of High-spatial and spectral resolution, sensor fusion and colorization remains.I would like to take this opportunity to express my most profound appreciation to the MDPI Book staff, the editorial team of Applied Sciences journal, especially Ms. Nimo Lang, the assistant editor of this Special Issue, talented authors, and professional reviewers
The use of multispectral sensing techniques to detect ponderosa pine trees under stress from insect or pathogenic organisms Annual progress report
Ground and aerial imaging techniques to detect tree damage caused by bark beetles in forested area
RSGPT: A Remote Sensing Vision Language Model and Benchmark
The emergence of large-scale large language models, with GPT-4 as a prominent
example, has significantly propelled the rapid advancement of artificial
general intelligence and sparked the revolution of Artificial Intelligence 2.0.
In the realm of remote sensing (RS), there is a growing interest in developing
large vision language models (VLMs) specifically tailored for data analysis in
this domain. However, current research predominantly revolves around visual
recognition tasks, lacking comprehensive, large-scale image-text datasets that
are aligned and suitable for training large VLMs, which poses significant
challenges to effectively training such models for RS applications. In computer
vision, recent research has demonstrated that fine-tuning large vision language
models on small-scale, high-quality datasets can yield impressive performance
in visual and language understanding. These results are comparable to
state-of-the-art VLMs trained from scratch on massive amounts of data, such as
GPT-4. Inspired by this captivating idea, in this work, we build a high-quality
Remote Sensing Image Captioning dataset (RSICap) that facilitates the
development of large VLMs in the RS field. Unlike previous RS datasets that
either employ model-generated captions or short descriptions, RSICap comprises
2,585 human-annotated captions with rich and high-quality information. This
dataset offers detailed descriptions for each image, encompassing scene
descriptions (e.g., residential area, airport, or farmland) as well as object
information (e.g., color, shape, quantity, absolute position, etc). To
facilitate the evaluation of VLMs in the field of RS, we also provide a
benchmark evaluation dataset called RSIEval. This dataset consists of
human-annotated captions and visual question-answer pairs, allowing for a
comprehensive assessment of VLMs in the context of RS
Remote Sensing Systems Optimization for Geobase Enhancement
The U.S. Air Force is in the process of implementing GeoBase, a geographic information system (GIS), throughout its worldwide installations, Air Force GIS needs can be augmented by imagery from aerial and satellite platforms. Imagery has greatly improved over the past several years and provides high resolution coverage of features on earth. Various imagery types will significantly increase GeoBase usefulness in a range of mission requirements, Potential Air Force uses of imagery include identifying heat loss, environmental monitoring, command decision-making, and emergency response, The research develops a decision tool to determine the appropriate imagery for a given Air Force Application, Current literature identified proven imagery applications, Literature review and a 2002 Air Force Geo-Integration Office (AF/GIO) survey were used to develop a comprehensive imagery applications list that satisfies Air Force mission requirements, An imagery decision matrix was crafted that allows a user to select an application and see imagery that fulfills the requirements for the task, An imagery system key provides further details of each imagery type, The matrix was tested at three Air Force bases, Increased awareness of the possibilities of an imagery-enriched GeoBase, and the efficiency afforded by the matrix, greatly reduces the time to identify and implement imagery, Available imagery was identified for the three Air Force bases at the National Imagery and Mapping Agency (NIMA) through a government contract at no additional cost, Current IKONOS imagery of Elmendoff Air Force base was obtained for analysis and implementation into GeoBase
High-Resolution Remotely Sensed Small Target Detection by Imitating Fly Visual Perception Mechanism
The difficulty and limitation of small target detection methods for high-resolution remote sensing data have been a recent research hot spot. Inspired by the information capture and processing theory of fly visual system, this paper endeavors to construct a characterized model of information perception and make use of the advantages of fast and accurate small target detection under complex varied nature environment. The proposed model forms a theoretical basis of small target detection for high-resolution remote sensing data. After the comparison of prevailing simulation mechanism behind fly visual systems, we propose a fly-imitated visual system method of information processing for high-resolution remote sensing data. A small target detector and corresponding detection algorithm are designed by simulating the mechanism of information acquisition, compression, and fusion of fly visual system and the function of pool cell and the character of nonlinear self-adaption. Experiments verify the feasibility and rationality of the proposed small target detection model and fly-imitated visual perception method
- …