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Visual Instruction Tuning for Holistic and Regional remote sensing imagery Comprehension

Visual Instruction Tuning for Holistic and Regional Remote S...

引用

signal, Information and Data processing (ICSIDP), IEEE International conference on

作者： Wei Zhang Miaoxin Cai Tong Zhang Zhuang Yin Xuerui Mao Advanced Research Institute of Multidisciplinary Sciences School of Mechatronical Engineering National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing Yangtze Delta Region Academy of Beijing Institute of Technology Beijing Institute of Technology Beijing China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

Recently, the visual instruction multimodal large language models (MLLMs) have been extensively studied in the nature scenario. However, current remote sensing (RS) MLLMs mainly focus on image-level understanding and typically allow interaction only through text instructions, resulting in limited accuracy and efficiency. To address those limitations, a visual instruction model is proposed in this article to extend MLLM’s fine-grained perception ability by incorporating bounding boxes with language instruction, aiming at achieving region-level visual understanding. To achieve this goal, a visual instruction dataset featuring multi-modal box-based region-text pairs is constructed. Furthermore, the visual instructions and images are encoded by the different encoders and subsequently fed into a large language model (LLM) along with text instruction tokens for tuning. Finally, experimental results demonstrate the proposed model’s promising performance in region-level image comprehension.

关键词： Visualization Analytical models Accuracy Large language models Data processing Sensors Tuning remote sensing

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Analysis and Detection of Mucilage Bloom from Multispectral Satellite images 30

Analysis and Detection of Mucilage Bloom from Multispectral ...

引用

30th IEEE signal processing and Communications Applications conference (SIU)

作者： Kucuk, Sefa Abaci, Bahri Dede, Murat Yuksel, Seniha Esen Yilmaz, Mete Erzurum Tekn Univ Elekt & Elekt Muhendisligi Bolumu Erzurum Turkey Hacettepe Univ Elekt & Elekt Muhendisligi Bolumu TR-06800 Ankara Turkey Bursa Tekn Univ Biyomuhendislik Bolumu Bursa Turkey

ISBN: (纸本)9781665450928

In this paper, we aim to detect and observe the mucilage formations in the Sea of Marmara by means of Sentinel-2A satellite data. For this purpose, we produce mucilage index maps by utilizing the relationship between the spectral bands of Sentinel-2A. Sentinel-2A has four 10m fine bands and six 20m coarse bands. To compute the mucilage index, the spectral bands must have the same spatial resolution. Although the Sentinel-2A does not have a panchromatic band, the spatial resolution of the 20m bands can be increased to 10m thanks to its four fine bands. Based on the results of this analysis, we utilize seven of the existing image fusion approaches to enhance the spatial resolution of 20m bands to 10m. We monitor changes in mucilage formations over time with the mucilage maps acquired by using fused images through the mucilage index.

关键词： Fusion mucilage Sentinel-2A remote sensing

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FPGA design of video processing circuit based on 4Kx4K Large array infrared detector 7

FPGA design of video processing circuit based on 4Kx4K Large...

引用

7th International conference on Electromechanical Control Technology and Transportation, ICECTT 2022

作者： Wang, Lei Huang, Yi Shen, YiWei Yu, Dong Geng, Shufang Liu, Ping Beijing Institute of Space Mechanics & Electricity Beijing China

ISBN: (纸本)9781510656659

As the core component of the camera, infrared focal plane detector assembly is widely used in the field of aerospace infrared remote sensing because of its excellent performance in thermal image viewing, forward-looking early warning, prevention and control monitoring, infrared recognition and so on. Aiming at the 4kx4k large area array infrared video circuit spliced by four 2kx2k infrared detectors, firstly this paper considers the difficulties of piecewise adjustment of integration time and sampling point position delay compensation of spliced detectors from the perspective of FPGA. Then the design idea is given, including configuring and using management chip to reduce clock jitter and improve the reliability of data transmission;the functions of programmable adjustment of pixel blind coordinates and real-time inconsistency correction are realized, and the image quality is improved;using DDR2 to store inconsistency correction parameters;finally, the channel image data synthesis and transmission are completed. All functions such as focal plane control, signal processing and plug-in chip control are completed with one FPGA, which has the advantages of miniaturization and lightweight. The test shows that the design idea and scheme in this paper are feasible and practical, which provides reliable technical support for video signal processing of aerospace remote sensing large array detector, and is of great significance. © 2022 SPIE

关键词： Field programmable gate arrays (FPGA)

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Analysis of underwater noise characteristics of ships in offshore waters 2

Analysis of underwater noise characteristics of ships in off...

引用

2nd International conference on signal image processing and Communication, ICSIPC 2022

作者： Meng, Lingyi Tang, Yunfeng Marine Acoustics and Remote Sensing Laboratory Zhejiang Ocean University Zhoushan China

ISBN: (纸本)9781510657694

Ship noise has always been one of the important sources of marine noise. In order to study the sound source characteristics of underwater radiated noise from ships, this paper is based on the monitoring data collected in the sea area of Zhoushan Zhairuo Island, to get the underwater noise signal of the measured ship under different driving speeds. Through the time-frequency domain feature analysis, the short-term energy spectrum and spectrogram of the noise signal are drawn, and the following conclusions are obtained: The frequency of ship underwater noise is mainly concentrated in the interval of 0~700Hz, with the increase of ship speed, the main frequency range of underwater noise also increases, and the noise energy at different frequencies also increases. The sound pressure level of the underwater noise of the ship selected in this experiment is in the range of 75dB~100dB. With the increase of ship speed, the sound pressure level of ship underwater noise also increases, and has a certain linear relationship. The slope estimate m is 2.442dB/knot. © 2022 SPIE.

关键词： Frequency domain analysis

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TeAw: Text-Aware Few-Shot remote sensing image Scene Classification

TeAw: Text-Aware Few-Shot Remote Sensing Image Scene Classif...

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International conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Kaihui Cheng Chule Yang Zunlin Fan Dayan Wu Naiyang Guan Defense Innovation Institute (DII) Academy of Military Sciences Beijing China Institute of Information Engineering Chinese Academy of Sciences Beijing China

The recent advance has shown that few-shot learning may be a promising way to alleviate the data reliance of remote sensing image scene classification. However, most existing works focus on extracting distinguishable features only from visual modality, while the problem of learning knowledge from multiple modalities has barely been visited. In this work, we propose a text-aware framework for few-shot remote sensing image scene classification (TeAw). Specifically, TeAw converts the class names to more detailed text descriptions and extracts text features using a pre-trained text encoder. Mean-while, TeAw obtains image features via an image encoder. Then we compute the correlation between the text and the image features, which helps the model grasp the core concept of the input image. Finally, TeAw calculates the similarity of local features between supports and queries to get the predictions. Extensive experiments show the outperformance of our TeAw compared with other SOTA methods.

关键词： Visualization Correlation Computational modeling signal processing Feature extraction Transformers Sensors

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GRAD-CAM AWARE SUPERVISED ATTENTION FOR VISUAL QUESTION ANSWERING FOR POST-DISASTER DAMAGE ASSESSMENT 29

GRAD-CAM AWARE SUPERVISED ATTENTION FOR VISUAL QUESTION ANSW...

引用

IEEE International conference on image processing (ICIP)

作者： Sarkar, Argho Rahnemoonfar, Maryam Univ Maryland Baltimore Cty Comp Vis & Remote Sensing Lab Bina Lab Baltimore MD 21228 USA

ISBN: (数字)9781665496209

ISBN: (纸本)9781665496209

In this paper, we present a Grad-Cam aware supervised attention framework for visual question answering (VQA) tasks for post-disaster damage assessment purposes. Visual-attention in visual question-answering tasks aims to focus on relevant image regions according to questions to predict answers. However, the conventional attention mechanisms in VQA work in an unsupervised manner, learning to give importance to visual contents by minimizing only task-specific loss. This approach fails to provide appropriate visual attention where the visual contents are very complex. The content and nature of UAV images in FlooNet-VQA dataset are very complex as they depict the hazardous scenario after Hurricane Harvey from a high altitude. To tackle this, we propose a supervised attention mechanism that uses explainable features from Grad-Cam to supervise visual attention in the VQA pipeline. The mechanism we propose operates in two stages. In the first stage of learning, we derived the visual explanations through Grad-Cam by training a baseline attention-based VQA model. In the second stage, we supervise our visual content for each question by incorporating the Grad-Cam explanations from the previous phase of the training process. We have improved the model performance over the state-of-the-art VQA models by a considerable margin on FloodNet dataset.

关键词： Visual Question Answering Supervised Attention Post-Disaster Assessment Natural Disaster Grad-Cam

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Enhanced Single remote sensing image Dehazing via Vision Transformer with Silencing Map Transmission and advanced image processing Techniques

Enhanced Single Remote Sensing Image Dehazing via Vision Tra...

引用

Intelligent Systems for Cybersecurity (ISCS), International conference on

作者： Sivasubramanyam Medasani B. Shireesha B. Hemanth Reddy C.Charan Teja E. Venkata Sainath Chowdary School of Engineering Electronics and Communication Engineering Mohan Babu University (Erstwhile Sree Vidyanikethan Engineering College) Tirupati India Dept. of Electronics and Communications Engineering (ECE) Erstwhile Sree Vidyanikethan Engineering College Tirupati India

ISBN: (数字)9798350375237

ISBN: (纸本)9798350375244

- This research provides an improved single remote sensing picture dehazing method using sophisticated image processing techniques and Vision Transformer with Silencing Map Transmission. The dehazing output generation, morphological analysis, dark channel prior, silencing map transmission, recurrent neural networks (RNN), and input image processing are some of the components that are included in the suggested method. The objective is to far outperform current techniques in terms of dehazed image quality. The initial action in the process is getting ready the input image, after which morphological analysis is performed to determine the properties of the haze. The key characteristics of the haze are retrieved by using the dark channel before. Transmitting silencing maps improves our comprehension of scene depth, which is important for precise dehazing. RNN is used to learn from sequential data and propagate information effectively. Clearer and more aesthetically pleasing photos are produced by the dehazing output, which is generated based on the learned attributes. Mean Squared Error (MSE), Peak signal-to-Noise Ratio (PSNR), and Structural Similarity Index Measure (SSIM), and Time of Execution (TOE) are among the metrics used to assess the efficacy of the suggested approach. The supremacy of the suggested strategy is shown in contrast to current approaches. The trial results demonstrate considerable improvements in PSNR, SSIM, and TOE measures across a variety of test images. In particular, the suggested approach outperforms current methods in terms of PSNR improvements of up to 4.23 dB and TOE reductions of up to 2.58 seconds. To sum up, the enhanced single remote sensing picture dehazing approach that has been described provides significant improvements in both image quality and processing efficiency. As such, it is a promising solution that might be used for a variety of remote sensing applications.

关键词： image quality Recurrent neural networks PSNR image processing Current measurement Measurement uncertainty Transformers

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Cross-Band Fusion for Multispectral Scene Classification

Cross-Band Fusion for Multispectral Scene Classification

引用

signal, Information and Data processing (ICSIDP), IEEE International conference on

作者： Jin Li Yu Liu Zhizhuo Jiang Xueqian Wang Bolun Zheng Shenzhen International Graduate School Tsinghua University Shenzhen China Department of Electronic Engineering Tsinghua University Beijing China School of Automation Hangzhou Dianzi University Hangzhou China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

With the daily acquisition of vast amounts of remote sensing images by satellites, effectively utilizing these images for Earth observation has become a significant research focus. Multispectral image scene classification plays a fundamental role in advancing the understanding of remote sensing imagery. Existing methods for multispectral image scene classification do not sufficiently explore the interrelationships between different bands, which limits the performance of models in this task. Unlike these, we propose a cross-band fusion method for multispectral scene classification. Specifically, we employ two appropriate visual backbones to extract band-level visual features from different bands, followed by a cross-band fusion module to fuse the features from various bands adaptively. This approach fully explores the complementary information between different bands, achieving enhanced accuracy in multispectral scene classification. Extensive experiments on a commonly used dataset BigEarthNet validate that our proposed cross-band fusion method outperforms state-of-the-art (SOTA) methods.

关键词： Earth Scene classification Visualization Adaptation models Satellites Fuses Feature extraction Data processing Sensors remote sensing

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A Water Flare Avoidance Method of the Agile Commercial Optical remote sensing Satellite image Based on Observation Geometry Data Optimization 12th

A Water Flare Avoidance Method of the Agile Commercial Optic...

引用

12th International conference on signal and Information processing, Networking and Computers, ICSINC 2024

作者： Deng, Chao Zeng, Yong Zhang, Shuai Hao, Dengcheng He, Huaying China Centre for Resources Satellite Data and Application Beijing100094 China

ISBN: (纸本)9789819649662

When commercial remote sensing satellites image water areas, if the satellite observation vector and the solar incident light vector satisfy the condition of specular or near-specular reflection, it is effortless for images of water areas to have excessive brightness or even overflow to pollute the adjacent pixels. The current observation geometric model of water flare is only applicable to the ocean flare, and it could not dispel the inland water flare phenomenon in the commercial remote sensing agile satellite imaging process. This paper proposes a method of effectively avoiding the flare phenomenon in the water region of remote sensing satellite images. By analyzing the relationship between the observation vector of the satellite imaging view axis and the sun light irradiation vector, it provides an imaging angle reference for the commercial remote sensing agile satellite imaging of the earth. This method can effectively eliminate the flare phenomenon in the water area due to the specular or near-specular reflection and guarantee the quality of the commercial remote sensing satellite image of the water application. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Shape optimization

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UAV remote sensing image DEHAZING BASED ON SALIENCY GUIDED TWO-SCALE TRANSMISSION CORRECTION

UAV REMOTE SENSING IMAGE DEHAZING BASED ON SALIENCY GUIDED T...

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IEEE International conference on image processing (ICIP)

作者： Zhang, Kemeng Zheng, Ruohui Ma, Sijia Zhang, Libao Beijing Normal Univ Sch Artificial Intelligence Beijing 100875 Peoples R China

ISBN: (纸本)9781665441155

Current dehazing methods for unmanned aerial vehicle (UAV) remote sensing images often hold problems of texture detail loss in highlight regions and color distortions. This is mainly due to incorrect estimation of the transmission. In this paper, we propose a UAV dehazing method based on saliency guided two-scale transmission correction. Firstly, we propose a dehaze-driven frequency domain saliency model to detect highlight regions of hazy UAV images for better transmission correction. Secondly, we introduce a two-scale correction method to estimate the transmission map with more accurate texture details. We also introduce a suppression parameter to further suppress color distortions and energy over-reduction. Finally, the saliency map is taken as a weight of transmission correction to avoid texture detail loss and color distortions, especially in highlights. Compared with state-of-the-art methods, our method shows better visual effect and detail visibility, especially for UAV images with highlight regions.

关键词： Dehazing remote sensing unmanned aerial vehicle saliency detection transmission correction

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