Soil texture is important for many environmental processes. In this paper, we study the classification of soil texture based on hyperspectral data. We develop and implement three 1-dimensional (1D) convolutional neural networks (CNN): the LucasCNN, the LucasResNet which contains an identity block as residual network, and the LucasCoordConv with an additional coordinates layer. Furthermore, we modify two existing 1D CNN approaches for the presented classification task. The code of all five CNN approaches is available on GitHub (Riese, 2019). We evaluate the performance of the CNN approaches and compare them to a random forest classifier. Thereby, we rely on the freely available LUCAS topsoil dataset. The CNN approach with the least depth turns out to be the best performing classifier. The LucasCoordConv achieves the best performance regarding the average accuracy. In future work, we can further enhance the introduced LucasCNN, LucasResNet and LucasCoordConv and include additional variables of the rich LUCAS dataset.
Karlsruhe Institute of Technology (KIT)（卡尔斯鲁厄理工学院）
In this contribution, we investigate the potential of hyperspectral data combined with either simulated ground penetrating radar (GPR) or simulated (sensor-like) soil-moisture data to estimate soil moisture. We propose two simulation approaches to extend a given multi-sensor dataset which contains sparse GPR data. In the first approach, simulated GPR data is generated either by an interpolation along the time axis or by a machine learning model. The second approach includes the simulation of soil-moisture along the GPR profile. The soil-moisture estimation is improved significantly by the fusi…
Institute of Photogrammetry and Remote Sensing, Karlsruhe Institute of Technology（卡尔斯鲁厄理工学院摄影测量与遥感研究所）
In this paper, we investigate the potential of estimating the soil-moisture content based on VNIR hyperspectral data combined with LWIR data. Measurements from a multi-sensor field campaign represent the benchmark dataset which contains measured hyperspectral, LWIR, and soil-moisture data conducted on grassland site. We introduce a regression framework with three steps consisting of feature selection, preprocessing, and well-chosen regression models. The latter are mainly supervised machine learning models. An exception are the self-organizing maps which combine unsupervised and supervised lea…
Istituto Nazionale di Fisica Nucleare（意大利国家核物理研究所）；Università Milano-Bicocca（米兰比可卡大学）
In this paper we propose an adaptive deep neural architecture for the prediction of multiple soil characteristics from the analysis of hyperspectral signatures. The proposed method overcomes the limitations of previous methods in the state of art: (i) it allows to predict multiple soil variables at once; (ii) it permits to backtrace the spectral bands that most contribute to the estimation of a given variable; (iii) it is based on a flexible neural architecture capable of automatically adapting to the spectral library under analysis. The proposed architecture is experimented on LUCAS, a large …
SpecBPP: A Self-Supervised Learning Approach for Hyperspectral Representation and Soil Organic Carbon Estimation
Center for Vision, Speech, and Signal Processing University Of Surrey Guildford, UK（视觉、语音和信号处理中心 英国萨里大学 Guildford分校）；Centre for Environmental Health and Engineering University Of Surrey Guildford, UK（环境健康与工程中心 英国萨里大学 Guildford分校）
Reflectance Multispectral Imaging for Soil Composition Estimation and USDA Texture Classification
Department of Electrical and Electronic Engineering, University of Peradeniya, Peradeniya 20400, Sri Lanka（电子与电气工程系，珀达尼亚大学）；Multidisciplinary AI Research Center, University of Peradeniya, Peradeniya 20400, Sri Lanka（多学科人工智能研究中心，珀达尼亚大学）；Department of Electrical and Information Engineering, University of Ruhuna, Hapugala, Galle 80000, Sri Lanka（电子与信息工程系，鲁胡纳大学）；Department of Civil Engineering, University of Peradeniya, Peradeniya 20400, Sri Lanka（土木工程系，珀达尼亚大学）
Recently, convolutional neural networks (CNN) have obtained promising results in single-image SR for hyperspectral pansharpening. However, enhancing CNNs' representation ability with fewer parameters and a shorter prediction time is a challenging and critical task. In this paper, we propose a novel multi-spectral image fusion method using a combination of the previously proposed 3D CNN model VolumeNet and 2.5D texture transfer method using other modality high resolution (HR) images. Since a multi-spectral (MS) image consists of several bands and each band is a 2D image slice, MS images can be …
Our overarching goal is to develop an accurate and explainable model for plant disease identification using hyperspectral data. Charcoal rot is a soil borne fungal disease that affects the yield of soybean crops worldwide. Hyperspectral images were captured at 240 different wavelengths in the range of 383 - 1032 nm. We developed a 3D Convolutional Neural Network model for soybean charcoal rot disease identification. Our model has classification accuracy of 95.73\% and an infected class F1 score of 0.87. We infer the trained model using saliency map and visualize the most sensitive pixel locati…
Hamedan University of Technology（哈马丹工业大学）；Tarbiat Modares University（塔比亚特·莫达雷斯大学）
This paper proposes a spatial feature extraction method based on energy of the features for classification of the hyperspectral data. A proposed orthogonal filter set extracts spatial features with maximum energy from the principal components and then, a profile is constructed based on these features. The important characteristic of the proposed approach is that the filter sets coefficients are extracted from statistical properties of data, thus they are more consistent with the type and texture of the remotely sensed images compared with those of other filters such as Gabor. To assess the per…
Prediction of soil fertility parameters using USB-microscope imagery and portable X-ray fluorescence spectrometry
Indian Institute of Technology Kharagpur（印度理工学院卡拉格普尔分校）；Bidhan Chandra Krishi Viswavidyalaya（比丹·钱德拉农业大学）；Washington State University（华盛顿州立大学）；Space Applications Centre, Indian Space Research Organisation（印度空间研究组织空间应用中心）；University of Guelph（圭尔夫大学）；Georgia Southern University（佐治亚南方大学）；Louisiana State University（路易斯安那州立大学）；Federal University of Lavras（拉夫拉斯联邦大学）
ENSET, Mohammed V University in Rabat（穆罕默德五世大学拉巴特高等师范技术教育学院）；National Center for Scientific and Technical Research（国家科学技术研究中心）
Dimensionality reduction is an important preprocessing step of the hyperspectral images classification (HSI), it is inevitable task. Some methods use feature selection or extraction algorithms based on spectral and spatial information. In this paper, we introduce a new methodology for dimensionality reduction and classification of HSI taking into account both spectral and spatial information based on mutual information. We characterise the spatial information by the texture features extracted from the grey level cooccurrence matrix (GLCM); we use Homogeneity, Contrast, Correlation and Energy. …
Evaluation of Deep Learning Semantic Segmentation for Land Cover Mapping on Multispectral, Hyperspectral and High Spatial Aerial Imagery
Center for Space and Remote Sensing Research, National Central University（国立中央大学太空及遥测研究中心）；Department of Computer Science and Information Engineering, National Central University（国立中央大学资讯工程学系）
Khwaja Fareed University of Engineering and Information Technology（赫瓦贾·法里德工程与信息技术大学）
Hyperspectral imaging (HSI) has been extensively utilized for a number of real-world applications. HSI classification (HSIC) is a challenging task due to high inter-class similarity, high intra-class variability, overlapping, and nested regions. A 2D Convolutional Neural Network (CNN) is a viable approach whereby HSIC highly depends on both Spectral-Spatial information, therefore, 3D CNN can be an alternative but highly computational complex due to the volume and spectral dimensions. Furthermore, these models do not extract quality feature maps and may underperform over the regions having simi…
Jai Shriram Engineering College（贾伊·施里拉姆工程学院）；Kongu Engineering College（孔古工程学院）
Hyperspectral image (HSI) classification plays a significant in the field of remote sensing due to its ability to provide spatial and spectral information. Due to the rapid development and increasing of hyperspectral remote sensing technology, many methods have been developed for HSI classification but still a lack of achieving the better performance. A Frost Filtered Scale-Invariant Feature Transformation based MultiLayer Perceptron Classification (FFSIFT-MLPC) technique is introduced for classifying the hyperspectral image with higher accuracy and minimum time consumption. The FFSIFT-MLPC te…
Hunan University（湖南大学）；Helmholtz-Zentrum Dresden-Rossendorf（亥姆霍兹德累斯顿罗森多夫研究中心）；Helmholtz Institute Freiberg for Resource Technology（亥姆霍兹弗莱贝格资源技术研究所）
The inclusion of spatial information into spectral classifiers for fine-resolution hyperspectral imagery has led to significant improvements in terms of classification performance. The task of spectral-spatial hyperspectral image classification has remained challenging because of high intraclass spectrum variability and low interclass spectral variability. This fact has made the extraction of spatial information highly active. In this work, a novel hyperspectral image classification framework using the fusion of dual spatial information is proposed, in which the dual spatial information is bui…
LGEST: Dynamic Spatial-Spectral Expert Routing for Hyperspectral Image Classification
Information Hub, The Hong Kong University of Science and Technology (Guangzhou)（香港科学与技术大学信息中心（广州））；Faculty of Geographical Science, Beijing Normal University（北京师范大学地理学部）；School of Electronic and Communication Engineering, Guangzhou University（广州大学电子与通信工程学院）
S$^2$Mamba: A Spatial-spectral State Space Model for Hyperspectral Image Classification
School of Artificial Intelligence, Xidian University（西安电子科技大学人工智能学院）；MoE Key Lab of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University（上海交通大学人工智能教育部重点实验室、人工智能研究院）
Exploring Multi-Timestep Multi-Stage Diffusion Features for Hyperspectral Image Classification
School of Information Science and Technology, Fudan University（复旦大学信息科学与技术学院）；Academy for Engineering and Technology, Fudan University（复旦大学工程与技术学院）；Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）
SSF-Net: Spatial-Spectral Fusion Network with Spectral Angle Awareness for Hyperspectral Object Tracking
School of Information and Electronics, Beijing Institute of Technology（信息与电子学院，北京理工大学）；National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing（空间智能信息处理国家级重点实验室）；Beijing Institute of Technology, Zhuhai, Guangdong（珠海，广东，北京理工大学）；Department of Electrical and Computer Engineering, University of Delaware（电气与计算机工程系，德克萨斯大学）；Department of Electrical and Computer Engineering, Mississippi State University（电气与计算机工程系，密苏里州立大学）
University of Wisconsin-Madison（威斯康星大学麦迪逊分校）；Carnegie Mellon University（卡内基梅隆大学）
We discuss basic concepts of convolutional neural networks (CNNs) and outline uses in manufacturing. We begin by discussing how different types of data objects commonly encountered in manufacturing (e.g., time series, images, micrographs, videos, spectra, molecular structures) can be represented in a flexible manner using tensors and graphs. We then discuss how CNNs use convolution operations to extract informative features (e.g., geometric patterns and textures) from the such representations to predict emergent properties and phenomena and/or to identify anomalies. We also discuss how CNNs ca…
