Entropy is the measure of uncertainty in any data and is adopted for maximisation of mutual information in many remote sensing operations. The availability of wide entropy variations motivated us for an investigation over the suitability preference of these versions to specific operations. Methodologies were implemented in Matlab and were enhanced with entropy variations. Evaluation of various implementations was based on different statistical parameters with reference to the study area The popular available versions like Tsalli's, Shanon's, and Renyi's entropies were analysed in context of various remote sensing operations namely thresholding, clustering and registration.
Entropy is the measure of uncertainty in any data and is adopted for maximisation of mutual information in many remote sensing operations. The availability of wide entropy variations motivated us for an investigation over the suitability preference of these versions to specific operations.
Edges characterize boundaries and are therefore a problem of practical importance in remote sensing.In this paper a comparative study of various edge detection techniques and band wise analysis of these algorithms in the context of object extraction with regard to remote sensing satellite images from the Indian Remote Sensing Satellite (IRS) sensors LISS 3, LISS 4 and Cartosat1 as well as Google Earth is presented.
The motivation of this paper is to conduct a comparative study on remote sensing image classification using the morphological attribute profiles (APs) and feature profiles (FPs) generated from different types of tree structures. Over the past few years, APs have been among the most effective methods to model the image's spatial and contextual information. Recently, a novel extension of APs called FPs has been proposed by replacing pixel gray-levels with some statistical and geometrical features when forming the output profiles. FPs have been proved to be more efficient than the standard APs wh…
This paper attempts to undertake the study of three types of noise such as Salt and Pepper (SPN), Random variation Impulse Noise (RVIN), Speckle (SPKN). Different noise densities have been removed between 10% to 60% by using five types of filters as Mean Filter (MF), Adaptive Wiener Filter (AWF), Gaussian Filter (GF), Standard Median Filter (SMF) and Adaptive Median Filter (AMF). The same is applied to the Saturn remote sensing image and they are compared with one another. The comparative study is conducted with the help of Mean Square Errors (MSE) and Peak-Signal to Noise Ratio (PSNR). So as …
Scene classification is a key problem in the interpretation of high-resolution remote sensing imagery. Many state-of-the-art methods, e.g. bag-of-visual-words model and its variants, the topic models as well as deep learning-based approaches, share similar procedures: patch sampling, feature description/learning and classification. Patch sampling is the first and a key procedure which has a great influence on the results. In the literature, many different sampling strategies have been used, {e.g. dense sampling, random sampling, keypoint-based sampling and saliency-based sampling, etc. However…
Deep learning (DL) stereo matching methods gained great attention in remote sensing satellite datasets. However, most of these existing studies conclude assessments based only on a few/single stereo images lacking a systematic evaluation on how robust DL methods are on satellite stereo images with varying radiometric and geometric configurations. This paper provides an evaluation of four DL stereo matching methods through hundreds of multi-date multi-site satellite stereo pairs with varying geometric configurations, against the traditional well-practiced Census-SGM (Semi-global matching), to c…
In recent years self-supervised learning has emerged as a promising candidate for unsupervised representation learning. In the visual domain its applications are mostly studied in the context of images of natural scenes. However, its applicability is especially interesting in specific areas, like remote sensing and medicine, where it is hard to obtain huge amounts of labeled data. In this work, we conduct an extensive analysis of the applicability of self-supervised learning in remote sensing image classification. We analyze the influence of the number and domain of images used for self-superv…
This paper analyzes and compares different deep learning loss functions in the framework of multi-label remote sensing (RS) image scene classification problems. We consider seven loss functions: 1) cross-entropy loss; 2) focal loss; 3) weighted cross-entropy loss; 4) Hamming loss; 5) Huber loss; 6) ranking loss; and 7) sparseMax loss. All the considered loss functions are analyzed for the first time in RS. After a theoretical analysis, an experimental analysis is carried out to compare the considered loss functions in terms of their: 1) overall accuracy; 2) class imbalance awareness (for which…
Accubits Technologies Inc（Accubits科技公司）；Acharya Narendra Dev College, University of Delhi（德里大学阿查里雅·纳伦德拉·德夫学院）；University of Calgary（卡尔加里大学）
Efficiently implementing remote sensing image classification with high spatial resolution imagery can provide a significant value in Land Use and Land Cover (LULC) classification. The new advances in remote sensing and deep learning technologies have facilitated the extraction of spatiotemporal information for LULC classification. Moreover, the diverse disciplines of science, including remote sensing, have utilised tremendous improvements in image classification by Convolutional Neural Networks (CNNs) with transfer learning. In this study, instead of training CNNs from scratch, the transfer le…
We explore the use of convolutional neural networks for the semantic classification of remote sensing scenes. Two recently proposed architectures, CaffeNet and GoogLeNet, are adopted, with three different learning modalities. Besides conventional training from scratch, we resort to pre-trained networks that are only fine-tuned on the target data, so as to avoid overfitting problems and reduce design time. Experiments on two remote sensing datasets, with markedly different characteristics, testify on the effectiveness and wide applicability of the proposed solution, which guarantees a significa…
Comparative and Interpretative Analysis of CNN and Transformer Models in Predicting Wildfire Spread Using Remote Sensing Data
Imperial College London（帝国理工学院）；University of Cambridge（剑桥大学）；University of Waterloo（滑铁卢大学）；Institut Polytechnique de Paris（巴黎综合理工学院）；EDF R&D（法国电力公司研发部门）；CEREA；ENPC（巴黎路桥学院）
IRISA - Université Bretagne Sud（法国布列塔尼南部大学IRISA研究所）；Gebze Technical University（格兹技术大学）；University of Trento（特伦托大学）
Morphological attribute profiles (APs) are among the most effective methods to model the spatial and contextual information for the analysis of remote sensing images, especially for classification task. Since their first introduction to this field in early 2010's, many research studies have been contributed not only to exploit and adapt their use to different applications, but also to extend and improve their performance for better dealing with more complex data. In this paper, we revisit and discuss different developments and extensions from APs which have drawn significant attention from res…
Nowadays, the hyperspectral remote sensing imagery HSI becomes an important tool to observe the Earth's surface, detect the climatic changes and many other applications. The classification of HSI is one of the most challenging tasks due to the large amount of spectral information and the presence of redundant and irrelevant bands. Although great progresses have been made on classification techniques, few studies have been done to provide practical guidelines to determine the appropriate classifier for HSI. In this paper, we investigate the performance of four supervised learning algorithms, na…
South China Agricultural University（华南农业大学）；Pazhou Lab（琶洲实验室）
Effectively classifying remote sensing scenes is still a challenge due to the increasing spatial resolution of remote imaging and large variances between remote sensing images. Existing research has greatly improved the performance of remote sensing scene classification (RSSC). However, these methods are not applicable to cross-domain few-shot problems where target domain is with very limited training samples available and has a different data distribution from source domain. To improve the model's applicability, we propose the feature-wise transformation module (FTM) in this paper. FTM transf…
Though semantic segmentation has been heavily explored in vision literature, unique challenges remain in the remote sensing domain. One such challenge is how to handle resolution mismatch between overhead imagery and ground-truth label sources, due to differences in ground sample distance. To illustrate this problem, we introduce a new dataset and use it to showcase weaknesses inherent in existing strategies that naively upsample the target label to match the image resolution. Instead, we present a method that is supervised using low-resolution labels (without upsampling), but takes advantage …
RS-YOLOX: A High Precision Detector for Object Detection in Satellite Remote Sensing Images
School of Information Science and Engineering, Yunnan University（云南大学信息科学与工程学院）；Yunnan Key Laboratory of Intelligent Systems and Computing（云南省智能系统与计算重点实验室）
In remote sensing, images acquired by various earth observation satellites tend to have either a high spatial and low spectral resolution or vice versa. Pansharpening is a technique which aims to improve spatial resolution of multispectral image. The challenges involve in the pansharpening are not only to improve the spatial resolution but also to preserve spectral quality of the multispectral image. In this paper, various pansharpening algorithms are discussed and classified based on approaches they have adopted. Using MATLAB image processing toolbox, several state-of-art pan-sharpening algor…
ONERA（法国航空航天研究院）；University Paris Saclay（巴黎-萨克雷大学）；Univ. Bretagne Sud（南布列塔尼大学）；IRISA（法国信息与随机系统研究所）
In recent years, deep learning techniques revolutionized the way remote sensing data are processed. Classification of hyperspectral data is no exception to the rule, but has intrinsic specificities which make application of deep learning less straightforward than with other optical data. This article presents a state of the art of previous machine learning approaches, reviews the various deep learning approaches currently proposed for hyperspectral classification, and identifies the problems and difficulties which arise to implement deep neural networks for this task. In particular, the issues…
Institute of Electronics, Chinese Academy of Sciences（中国科学院电子学研究所）；University of Chinese Academy of Sciences（中国科学院大学）
The performance of object instance segmentation in remote sensing images has been greatly improved through the introduction of many landmark frameworks based on convolutional neural network. However, the object densely issue still affects the accuracy of such segmentation frameworks. Objects of the same class are easily confused, which is most likely due to the close docking between objects. We think context information is critical to address this issue. So, we propose a novel framework called SLCMASK-Net, in which a sequence local context module (SLC) is introduced to avoid confusion between …
Majority models of remote sensing image changing detection can only get great effect in a specific resolution data set. With the purpose of improving change detection effectiveness of the model in the multi-resolution data set, a weighted rich-scale inception coder network (WRICNet) is proposed in this article, which can make a great fusion of shallow multi-scale features, and deep multi-scale features. The weighted rich-scale inception module of the proposed can obtain shallow multi-scale features, the weighted rich-scale coder module can obtain deep multi-scale features. The weighted scale b…
MergeSAM: Unsupervised change detection of remote sensing images based on the Segment Anything Model
School of Geography and Planning, Sun Yat-sen University（中山大学地理科学与规划学院）；Unit 33269 of the PLA（中国人民解放军33269部队）；Intelligent Science & Technology Academy Limited of CASIC（中国航天科工集团智能科技研究院有限公司）
The binary segmentation of roads in very high resolution (VHR) remote sensing images (RSIs) has always been a challenging task due to factors such as occlusions (caused by shadows, trees, buildings, etc.) and the intra-class variances of road surfaces. The wide use of convolutional neural networks (CNNs) has greatly improved the segmentation accuracy and made the task end-to-end trainable. However, there are still margins to improve in terms of the completeness and connectivity of the results. In this paper, we consider the specific context of road extraction and present a direction-aware resi…
SCORE: Scene Context Matters in Open-Vocabulary Remote Sensing Instance Segmentation
Nanyang Technological University（南洋理工大学）；MoE Key Laboratory of Interdisciplinary Research of Computation and Economics, Shanghai University of Finance and Economics（上海财经大学经济与计算交叉研究重点实验室）；Institute for Infocomm Research (I 2 R), A*STAR, Singapore（新加坡资讯与通讯研究院）
School of Artificial Intelligence and School of Automation, Xi’an University of Posts and Telecommunications（人工智能学院和自动化学院，西安邮电大学）；Department of Electrical and Electronic Engineering, The Hong Kong Polytechnic University（电气电子工程系，香港理工大学）
PLA Strategic Force Information Engineering University（中国人民解放军战略支援部队信息工程大学）；Space Engineering University（航天工程大学）；State Key Laboratory of Geo-Information Engineering（地理信息工程国家重点实验室）；Beijing University of Technology（北京工业大学）；University of Trento（特伦托大学）；Beijing Institute of Remote Sensing Information（北京遥感信息研究所）；China University of Geosciences (Beijing)（中国地质大学（北京））；ETH Zurich（苏黎世联邦理工学院）
Long-range contextual information is crucial for the semantic segmentation of High-Resolution (HR) Remote Sensing Images (RSIs). However, image cropping operations, commonly used for training neural networks, limit the perception of long-range contexts in large RSIs. To overcome this limitation, we propose a Wide-Context Network (WiCoNet) for the semantic segmentation of HR RSIs. Apart from extracting local features with a conventional CNN, the WiCoNet has an extra context branch to aggregate information from a larger image area. Moreover, we introduce a Context Transformer to embed contextual…
GeoMag: A Vision-Language Model for Pixel-level Fine-Grained Remote Sensing Image Parsing
Institute of Space Earth Science, School of Frontier Sciences, Nanjing University（南京大学空间地球科学学院）；Computer Network Information Center, Chinese Academy of Sciences（中国科学院计算机网络信息中心）
Recently, deep learning technology have been extensively used in the field of image recognition. However, its main application is the recognition and detection of ordinary pictures and common scenes. It is challenging to effectively and expediently analyze remote-sensing images obtained by the image acquisition systems on unmanned aerial vehicles (UAVs), which includes the identification of the target and calculation of its position. Aerial remote sensing images have different shooting angles and methods compared with ordinary pictures or images, which makes remote-sensing images play an irrep…
Intra- and Cross-Scale Feature Interaction Network for Referring Remote Sensing Image Segmentation
School of Artificial Intelligence, Optics and Electronics (iOPEN), Northwestern Polytechnical University（人工智能、光学与电子学院（iOPEN），西北工业大学）；School of Computer Science, Northwestern Polytechnical University（计算机科学学院，西北工业大学）；department of Software, Northwest Institute of Nuclear Technology（软件系，西北核技术研究院）
The development of cross-modal retrieval systems that can search and retrieve semantically relevant data across different modalities based on a query in any modality has attracted great attention in remote sensing (RS). In this paper, we focus our attention on cross-modal text-image retrieval, where queries from one modality (e.g., text) can be matched to archive entries from another (e.g., image). Most of the existing cross-modal text-image retrieval systems in RS require a high number of labeled training samples and also do not allow fast and memory-efficient retrieval. These issues limit th…
VFM-Guided Semi-Supervised Detection Transformer under Source-Free Constraints for Remote Sensing Object Detection
School of Information and Electronics, Beijing Institute of Technology（信息与电子学院，北京理工大学）；Beijing Institute of Technology Chongqing Innovation Center（北京理工大学重庆创新中心）；National Key Laboratory for Space-Born Intelligent Information Processing（空间智能信息处理国家重点实验室）
OptiSAR-Net++: A Large-Scale Benchmark and Transformer-Free Framework for Cross-Domain Remote Sensing Visual Grounding
South China Normal University（华南师范大学）；Hong Kong University of Science and Technology（香港科技大学）；Tongji University（同济大学）；Shanghai Research Institute for Intelligent Autonomous Systems（上海自主智能无人系统科学中心）；State Key Laboratory of Intelligent Autonomous Systems（智能自主系统国家重点实验室）；Frontiers Science Center for Intelligent Autonomous Systems（智能自主系统前沿科学中心）
Wuhan University（武汉大学）；Chinese Academy of Sciences（中国科学院）；China Land Survey and Planning Institute（中国国土勘测规划院）
In recent years, large amount of high spatial-resolution remote sensing (HRRS) images are available for land-cover mapping. However, due to the complex information brought by the increased spatial resolution and the data disturbances caused by different conditions of image acquisition, it is often difficult to find an efficient method for achieving accurate land-cover classification with high-resolution and heterogeneous remote sensing images. In this paper, we propose a scheme to apply deep model obtained from labeled land-cover dataset to classify unlabeled HRRS images. The main idea is to r…
Federated Learning Across Decentralized and Unshared Archives for Remote Sensing Image Classification
Technische Universität Berlin（柏林工业大学）；BIFOLD - Berlin Institute for the Foundations of Learning and Data（柏林学习与数据基础研究所）；École Polytechnique Fédérale de Lausanne（洛桑联邦理工学院）
LithoBench: Benchmarking Large Multimodal Models for Remote-Sensing Lithology Interpretation
School of Computer Science, China University of Geosciences（中国地质大学（北京）计算机学院）；PRADA Lab, King Abdullah University of Science and Technology（科廷大学PRADA实验室）；Department of Computer Science, University of Exeter（埃克塞特大学计算机科学系）
Pennsylvania State University Applied Research Laboratory（宾夕法尼亚州立大学应用研究实验室）；National Geospatial-Intelligence Agency（国家地理空间情报局）；Massachusetts Institute of Technology（麻省理工学院）
There has been a surge in remote sensing machine learning applications that operate on data from active or passive sensors as well as multi-sensor combinations (Ma et al. (2019)). Despite this surge, however, there has been relatively little study on the comparative value of 3D surface information for machine learning classification tasks. Adding 3D surface information to RGB imagery can provide crucial geometric information for semantic classes such as buildings, and can thus improve out-of-sample predictive performance. In this paper, we examine in-sample and out-of-sample classification per…
RingMo-Aerial: An Aerial Remote Sensing Foundation Model With Affine Transformation Contrastive Learning
Aerospace Information Research Institute, Chinese Academy of Sciences（中国科学院航空信息研究所）；University of Chinese Academy of Sciences（中国科学院大学）；Key Laboratory of Target Cognition and Application Technology (TCAT), Aerospace Information Research Institute, Chinese Academy of Sciences（目标认知与应用技术重点实验室（TCAT））；Department of Electronic Engineering, Tsinghua University（清华大学电子工程系）
A Robust Multisource Remote Sensing Image Matching Method Utilizing Attention and Feature Enhancement Against Noise Interference
School of Communications and Information Engineering, Chongqing University of Posts and Telecommunications（重庆邮电大学通信与信息工程学院）；Advanced Network and Intelligent Connection Technology Key Laboratory of Chongqing Education Commission of China（重庆市教育委员会先进网络与智能连接技术重点实验室）；Chongqing Key Laboratory of Ubiquitous Sensing and Networking（重庆市普适感知与网络重点实验室）
