Transformers have intrigued the vision research community with their state-of-the-art performance in natural language processing. With superior performance, transformers found way field of hyperspectral image classification and achieved promising results. In this article, we harness power to conquer task unmixing propose a novel deep neural network-based model transformers. A transformer network captures nonlocal feature dependencies by interactions between patches, which are not employed...

In this article, we introduce a deep learning-based technique for the linear hyperspectral unmixing problem. The proposed method contains two main steps. First, endmembers are extracted using geometric endmember extraction method, i.e., simplex volume maximization in subspace of data set. Then, abundances estimated image prior. motivation work is to boost abundance estimation and make problem robust noise. prior uses convolutional neural network estimate fractional abundances, relying on...

In this article, we propose a minimum simplex convolutional network (MiSiCNet) for deep hyperspectral unmixing. Unlike all the learning-based unmixing methods proposed in literature, encoder–decoder architecture incorporates spatial information and geometrical of data addition to spectral information. The is incorporated using filters implicitly applying prior on abundances. exploited by incorporating volume penalty term loss function endmember estimation. This beneficial when there are no...

In this letter, we propose a sparse unmixing technique using convolutional neural network (SUnCNN) for hyperspectral images. SUnCNN is the first deep learning-based proposed unmixing. It uses encoder–decoder to generate abundances relying on spectral library. We reformulate into an optimization over network's parameters. Therefore, learns in unsupervised manner map fixed input optimum abundances. Additionally, holds sum-to-one constraint softmax activation layer. The method compared with...

Accurate anatomical matching for patient-specific electromyographic (EMG) mapping is crucial yet technically challenging in various medical disciplines. The fixed electrode construction of multielectrode arrays (MEAs) makes it nearly impossible to match an individual's unique muscle anatomy. This mismatch between the MEAs and target muscles leads missing relevant activity, highly redundant data, complicated placement optimization, inaccuracies classification algorithms. Here, we present...

This paper proposes a blind nonlinear unmixing technique for intimate mixtures using the Hapke model and convolutional neural networks (HapkeCNN). We use fully encoder-decoder deep network unmixing. Additionally, we propose novel loss function that includes three terms; 1) quadratic term based on model, captures nonlinearity, 2) reconstruction error of reflectances, to ensure fidelity reconstructed reflectance, 3) minimum volume total variation exploits geometrical information estimate...

Button mushrooms (Agaricus bisporus) grow in multilayered Dutch shelves with limited space between two shelves. As an alternative to conventional hand-picking, automated harvesting recent times has gained widespread popularity. However, of faces critical challenges the form growing environment, spaces, picking forces, and efficiency. End effectors for button are integral part process. The end developed so far oversized, bulky, slow thus unsuitable commercial mushroom applications. This paper...

The mushroom farming industry struggles to automate harvesting due limited large-scale annotated datasets and the complex growth patterns of mushrooms, which complicate detection, segmentation, pose estimation. To address this, we introduce a synthetic dataset with 40,000 unique scenes white Agaricus bisporus brown baby bella capturing realistic variations in quantity, position, orientation, stages. Our two-stage estimation pipeline combines 2D object detection instance segmentation 3D point...

Automating agricultural processes holds significant promise for enhancing efficiency and sustainability in various farming practices. This paper contributes to the automation of by providing a dedicated mushroom detection dataset related automated harvesting, 3D pose estimation, growth monitoring button produced using Agaricus Bisporus fungus. With total 2,000 images object detection, instance segmentation, estimation containing over 100,000 instances an additional 3,838 yield 8 scenes...

Due to the complex interaction of light with moist soils, soil moisture content (SMC) is hard estimate from spectral reflectance. Spectral variability, caused by variations in viewing and illumination angle between-sensor further complicates estimation. In this work, we developed a supervised methodology accurately SMC The method determines proxy for soil, making use reflectance spectra an air-dried saturated sample. made invariant angle, sensor type. next step, normalized respect...

Optical hyperspectral cameras (HSCs) capture the spectral reflectance of materials. Since many materials behave as heterogeneous intimate mixtures with which each photon interacts differently, relationship between and material composition is very complex. Quantitative validation unmixing algorithms requires high-quality ground-truth fractional abundance data, are difficult to obtain. In this work, we generated a comprehensive laboratory dataset intimately mixed mineral powders. For this,...

Due to the complex interaction of light with Earth’s surface, reflectance spectra can be described as highly nonlinear mixtures reflectances material constituents occurring in a given resolution cell hyperspectral data. Our aim is estimate fractional abundance maps materials from The main disadvantage using mixing models that model parameters are not properly interpretable terms abundances. Moreover, all dataset necessarily follow same particular model. In this work, we present supervised...

Spectral measurements are commonly applied for the nondestructive estimation of leaf parameters, such as concentrations chlorophyll a and b, carotenoid, anthocyanin, brown pigment, water content, mass per area quantification vegetation physiology. The most popular way to estimate these parameters is by using spectral indices. use biochemical models allows us full wavelength range (400-2500 nm) physically interpret result. However, their performance usually lower than that supervised machine...

Due to the complex interaction of light with mixed materials, reflectance spectra are highly nonlinearly related pure material endmember spectra, making it hard estimate fractional abundances materials. Changing illumination conditions and cross-sensor situations cause spectral variability, further complicating unmixing procedure. In this work, we propose a supervised approach unmix mineral powder mixtures, containing variability. First, estimated by calculating geodesic distances between...

Hyperspectral linear unmixing and denoising are highly related hyperspectral image (HSI) analysis tasks. In particular, with the assumption of Gaussian noise, model assumed for HSI in case low-rank is often same as one used unmixing. However, optimization criterion assumptions on constraints different. Additionally, noise reduction a preprocessing step data ignored. The main goal this paper to study experimentally influence process by: (1) investigating effect performance unmixing; (2)...

In this work, we studied the potential of visible, near-infrared, and shortwave infrared wavelength regions for monitoring oil spill incidents using optical reflectance. First, a simple physical model was designed accurate thickness volume estimation The developed method made invariant to changes in acquisition illumination conditions. next step, an algorithm based on artificial neural network detect spilled oil. training samples that are required optimize parameters were generated by...

Because of the complex interaction light with Earth surface, a hyperspectral pixel can be composed highly nonlinear mixture reflectances materials on ground. When mixing models are applied, estimated model parameters usually hard to interpret and link actual fractional abundances. Moreover, not all spectral in real scene follow same particular model. In this paper, we present supervised learning method for unmixing. method, neural network is applied learn mappings true that would obtained if...

Currently, this paper is under review in IEEE. Transformers have intrigued the vision research community with their state-of-the-art performance natural language processing. With superior performance, transformers found way field of hyperspectral image classification and achieved promising results. In article, we harness power to conquer task unmixing propose a novel deep model transformers. We aim utilize ability better capture global feature dependencies order enhance quality endmember...

In this work, we propose a supervised framework for spectral unmixing of binary intimate mixtures. The core idea is based on geodesic distance measurements and regression to estimate the fractional abundances. main assumption that reflectances mixtures form curve between two endmembers, mixture's relative position serves as an indicator its We four novel approaches approximate position. From this, abundances are obtained using Gaussian process regression. proposed simultaneously copes with...