Multichannel Imagery Research Articles

Compact staring-type underwater spectral imaging system utilizing k-Nearest neighbor-based interpolation for spectral reconstruction

Besides the challenges posed by the complex optical properties of water, underwater spectral imaging face significant issues, including system bulkiness, optical complexity, and high spectral resolution requirement. This study introduces a compact staring-type underwater spectral imaging system designed to overcome these limitations. Featuring a 6-channel rotary spectral camera, the system is optimized for optical simplicity, and spectral efficiency. It employs a k-Nearest Neighbor (KNN)-based spectral reconstruction method to substantially enhance spectral resolution. Auto-focusing and image registration techniques, utilizing the Tenengrad function and Scale-Invariant Feature Transform (SIFT) feature points, ensure sharp and aligned multi-channel imagery. Experimental validation demonstrates the system’s capability to accurately reconstruct spectral reflectance data across the 400 nm to 700 nm band, significantly increasing spectral resolution from 6 to 31 channels. The spectral reflectance reconstruction results, achieving an average Root Mean Square Error (RMSE) of 0.0651 and Goodness of Fit Coefficient (GFC) of 0.9871, highlight the system’s efficiency in precise spectral analysis. This advancement in spectral resolution and adaptability to diverse underwater applications marks a significant leap forward in underwater observation and monitoring through spectral imaging.

Improved surface displacement estimation through stacking cross-correlation spectra from multi-channel imagery

Studying sporadic and complex geophysical surface flows, like earthquakes or sea surface circulation, are challenging cases. If a satellite is able to image an event, it becomes essential to pull out as much information as possible. In this contribution we demonstrate a method to increase the coverage and signal-to-noise ratio for displacement estimation, making such surface flow estimates more complete. We leverage upon the redundant offset information acquired by multi-channel push-broom imagery. The individual cross-correlation spectra (cross power spectral density; Fourier transform of the cross-correlation function) of different spectral bands are averaged in the frequency domain before sub-pixel offset-estimation by phase-plane fitting. The method is demonstrated near Kaikōura, where in 2016 a surface rupture occurred. RapidEye data from two different dates were used to reconstruct the displacement. In addition, the circulation along the coast is estimated from data from a single date where multiple spectral bands were acquired within seconds which made stacking of cross-correlation spectra possible. The demonstrated methodology is applied to data from the already decommissioned RapidEye constellation, but can be adopted to other pushbroom systems, such as the Landsat legacy or Sentinel-2.

Nonparametric Array Manifold Calibration for Ice Sheet Tomography

Manifold calibration improves parametric angle estimator accuracy and resolution performance by reducing the mismatch between the model of an array’s response to directional sources and truth. This article presents nonparametric array manifold calibration for a multichannel ice-penetrating synthetic aperture radar (SAR) sounder used for imaging subglacial morphology with parametric angle estimation in tomography. In this study, we outline a methodology for identifying scatterers at known angles from multichannel imagery by aligning our measurements to an independent fine-resolution satellite-derived digital elevation model of the Arctic that extends beyond the swath of the SAR. We adopt a support statistic based on our partial knowledge of the array response to identify approximately single-source measurements in our scenes. This technique is a departure from traditional approaches to the sounder array characterization problem that require measurements of flat, specular surface reflections from a maneuvering platform. We aggregate observations of single sources and measure manifold corrections relative to our nominal model from the principal eigenvector of our array covariance. We demonstrate the application of three measured manifolds in tomography and compare performance to a nominal manifold that assumes isotropic radiators and known array geometry. We present radar-derived topography of exposed rock and sea ice in the Canadian Arctic Archipelago under the measured and nominal manifolds and report improved vertical accuracy realized with a measured manifold model assumed by the MUltiple SIgnal Classification angle estimators in 3-D image formation.

Tropical Cyclone Intensity Estimation Using Multidimensional Convolutional Neural Network From Multichannel Satellite Imagery

Tropical Cyclone Intensity Estimation Using Multidimensional Convolutional Neural Network From Multichannel Satellite Imagery

Instance Segmentation for Large, Multi-Channel Remote Sensing Imagery Using Mask-RCNN and a Mosaicking Approach

Instance segmentation is the state-of-the-art in object detection, and there are numerous applications in remote sensing data where these algorithms can produce significant results. Nevertheless, one of the main problems is that most algorithms use Red, Green, and Blue (RGB) images, whereas Satellite images often present more channels that can be crucial to improve performance. Therefore, the present work brings three contributions: (a) conversion system from ground truth polygon data into the Creating Common Object in Context (COCO) annotation format; (b) Detectron2 software source code adaptation and application on multi-channel imagery; and (c) large scene image mosaicking. We applied the procedure in a Center Pivot Irrigation System (CPIS) dataset with ground truth produced by the Brazilian National Water Agency (ANA) and Landsat-8 Operational Land Imager (OLI) imagery (7 channels with 30-m resolution). Center pivots are a modern irrigation system technique with massive growth potential in Brazil and other world areas. The round shapes with different textures, colors, and spectral behaviors make it appropriate to use Deep Learning instance segmentation. We trained the model using 512 × 512-pixel sized patches using seven different backbone structures (ResNet50- Feature Pyramid Network (FPN), Resnet50-DC5, ResNet50-C4, Resnet101-FPN, Resnet101-DC5, ResNet101-FPN, and ResNeXt101-FPN). The model evaluation used standard COCO metrics (Average Precision (AP), AP50, AP75, APsmall, APmedium, and AR100). ResNeXt101-FPN had the best results, with a 3% advantage over the second-best model (ResNet101-FPN). We also compared the ResNeXt101-FPN model in the seven-channel and RGB imagery, where the multi-channel model had a 3% advantage, demonstrating great improvement using a larger number of channels. This research is also the first with a mosaicking algorithm using instance segmentation models, where we tested in a 1536 × 1536-pixel image using a non-max suppression sorted by area method. The proposed methodology is innovative and suitable for many other remote sensing problems and medical imagery that often present more channels.

Morphologically Decoupled Structured Sparsity for Rotation-Invariant Hyperspectral Image Analysis

Hyperspectral imagery has emerged as a popular sensing modality for a variety of applications, and sparsity-based methods were shown to be very effective to deal with challenges coming from high dimensionality in most hyperspectral classification problems. In this paper, we challenge the conventional approach to hyperspectral classification that typically builds sparsity-based classifiers directly on spectral reflectance features or features derived directly from the data. We assert that hyperspectral image (HSI) processing can benefit very significantly by decoupling data into geometrically distinct components since the resulting decoupled components are much more suitable for sparse representation-based classifiers. Specifically, we apply morphological separation to decouple data into texture and cartoon-like components, which are sparsely represented using local discrete cosine bases and multiscale shearlets, respectively. In addition to providing a structured sparse representation, this approach allows us to build classifiers with invariance properties specific to each geometrically distinct component of the data. The experimental results using real-world HSI data sets demonstrate the efficacy of the proposed framework for classifying multichannel imagery under a variety of adverse conditions—in particular, small training sample size, additive noise, and rotational variabilities between training and test samples.

Multiscale Tikhonov-Total Variation Image Restoration Using Spatially Varying Edge Coherence Exponent.

Edge preserving regularization using partial differential equation (PDE)-based methods although extensively studied and widely used for image restoration, still have limitations in adapting to local structures. We propose a spatially adaptive multiscale variable exponent-based anisotropic variational PDE method that overcomes current shortcomings, such as over smoothing and staircasing artifacts, while still retaining and enhancing edge structures across scale. Our innovative model automatically balances between Tikhonov and total variation (TV) regularization effects using scene content information by incorporating a spatially varying edge coherence exponent map constructed using the eigenvalues of the filtered structure tensor. The multiscale exponent model we develop leads to a novel restoration method that preserves edges better and provides selective denoising without generating artifacts for both additive and multiplicative noise models. Mathematical analysis of our proposed method in variable exponent space establishes the existence of a minimizer and its properties. The discretization method we use satisfies the maximum-minimum principle which guarantees that artificial edge regions are not created. Extensive experimental results using synthetic, and natural images indicate that the proposed multiscale Tikhonov-TV (MTTV) and dynamical MTTV methods perform better than many contemporary denoising algorithms in terms of several metrics, including signal-to-noise ratio improvement and structure preservation. Promising extensions to handle multiplicative noise models and multichannel imagery are also discussed.

Cloud Motion Analysis Using Multichannel Correlation-Relaxation Labeling

Cloud motion vectors derived from sequences of remotely sensed data are widely used by numerical weather prediction models and other meteorological and climatic applications. One approach to computing cloud motion vectors is the correlation-relaxation labeling technique, in which a set of candidate vectors for each template is refined using relaxation labeling to provide a local smoothness constraint. In this letter, an extension of the correlation-relaxation labeling framework to tracking clouds in multichannel imagery is presented. As this multichannel approach takes advantage of the diversity between channels, it has the potential for producing motion vectors with a superior quality and coverage than can be achieved by any individual channel. Results for visible and infrared images from Meteostat Second Generation confirm the benefits of the multichannel approach

A new approach to mixed pixel classification of hyperspectral imagery based on extended morphological profiles

This paper presents a new approach to the analysis of hyperspectral images, a new class of image data that is mainly used in remote sensing applications. The method is based on the generalization of concepts from mathematical morphology to multi-channel imagery. A new vector organization scheme is described, and fundamental morphological vector operations are defined by extension. Theoretical definitions of extended morphological operations are used in the formal definition of the concept of extended morphological profile, which is used for multi-scale analysis of hyperspectral data. This approach is particularly well suited for the analysis of image scenes where most of the pixels collected by the sensor are characterized by their mixed nature, i.e. they are formed by a combination of multiple underlying responses produced by spectrally distinct materials. Experimental results demonstrate the applicability of the proposed technique in mixed pixel analysis of simulated and real hyperspectral data collected by the NASA/Jet Propulsion Laboratory Airborne Visible/Infrared Imaging Spectrometer and the DLR Digital Airborne (DAIS 7915) and Reflective Optics System Imaging Spectrometers. The proposed method works effectively in the presence of noise and low spatial resolution. A quantitative and comparative performance study with regards to other standard hyperspectral analysis methodologies reveals that the combined utilization of spatial and spectral information in the proposed technique produces classification results which are superior to those found by using the spectral information alone.

Le cône sous-marin du Nil et son réseau de chenaux profonds : nouveaux résultats (campagne Fanil)

The meandrous leveed channels of the Nile Cone show clear evidence of avulsions. Their sedimentary architecture is founded on numerous stacked lens-shaped acoustic units. In the areas of the distal fan, lobe deposits are apparent from multichannel imagery. Huge debris flow deposits, sometimes associated with pockmarks, are recognized. Mud volcanoes and gas seeping are closely associated with faulting. In the East, a very long north-trending channel, originating from the Egyptian coast, merges with a network of channels, very probably originating from the Levantine coasts. Both networks outlet in the sedimentary basin located south of Cyprus.

Double-regularization approach for blind restoration of multichannel imagery

In this paper, a double-regularization (DR) approach is proposed to perform blind multichannel imagery restoration. The proposed DR approach effectively utilizes the smoothness property of image planes and blur operators. The correlations between different image planes are considered in the blind restoration process. The proposed approach minimizes a cost function that consists of a restoration error measure about each image plane and two regularization terms. The conjugate gradient type alternative-minimization (AM) strategy is derived to minimize the cost function. The developed AM strategy is simple and effective. Excellent results were obtained. The proposed approach provides a new dimension for the blind restoration of the multichannel imagery.

Physical retrievals of over-ocean rain rate from multichannel microwave imagery. Part II: Algorithm implementation

A new physical inversion-based algorithm for retrieving rain rate over the ocean with the Special Sensor Microwave Imager (SSM/I) is described. In a departure from other rain rate retrieval algorithms, the satellite observables inverted in the present algorithm are not the raw brightness temperatures but rather normalized polarizations for 19.35, 37.0, and 85.5 GHz, plus an 85.5 GHz scattering index which is sensitive primarily to ice particles aloft. The normalized polarizations are interpreted as footprint-averages of theoretically derived analytic functions of the column optical depth associated primarily with liquid water. The effective vertical depth of the rain layer is specified as a function of the SSM/I estimated column water vapor.

Corn Field Identification Accuracy Using Airborne Radar Imagery

SUMMARYFour channel synthetic aperture radar imagery was interpreted to determine corn field identification accuracies obtainable using single channel, multi-channel and multi-date radar data. Image tone and texture of agricultural fields in the training set were used to derive discrimination criteria which were applied to fields in the testing set. The confusion of other agricultural crops with corn at X-band frequencies and forest cover with corn at L-band frequencies leads to errors of omission and inclusion. Corn field identification accuracies ranged from 58 to 100% using single channel imagery. The use of multi-channel imagery reduces the identification errors and accuracies exceeding 90% were consistently obtained. Individuals lacking radar interpretation experience but familiar with tonal and textural discrimination on airphotos obtained similar results with a short training period. Only two channels (one X-band, one L-band) are needed for the discrimination of corn from all other surface cover ty...