Dynamic Ensemble Selection Approach for Hyperspectral Image Classification With Joint Spectral and Spatial Information

International audienceAccurate generation of a land cover map using hyperspectral data is an important application of remote sensing. Multiple classifier system (MCS) is an effective tool for hyperspec-tral image classification. However, most of the research in MCS addressed the problem of classifier combination, while the potential of selecting classifiers dynamically is least explored for hyper-spectral image classification. The goal of this paper is to assess the potential of dynamic classifier selection/dynamic ensemble selection (DCS/DES) for classification of hyperspectral images, which consists in selecting the best (subset of) optimal classifier(s) relative to each input pixel by exploiting the local information content of the image pixel. In order to have an accurate as well as com-putationally fast DCS/DES, we proposed a new DCS/DES framework based on extreme learning machine (ELM) regression and a new spectral–spatial classification model, which incorporates the spatial contextual information by using the Markov random field (MRF) with the proposed DES method. The proposed classification framework can be considered as a unified model to exploit the full spectral and spatial information. Classification experiments carried out on two different airborne hyperspectral images demonstrate that the proposed method yields a significant increase in the accuracy when compared to the state-of-the-art approaches

A real-time FPGA accelerated stream processing for hyperspectral image classification

Realizing real-time classification of hyperspectral imagery has attracted researchers across various scientific and engineering disciplines. Several time-critical applications of high-resolution hyperspectral imaging require analysis of hyperspectral imagery with minimal turnaround time. In this regard, field-programmable gate arrays (FPGAs) provide cost-effective performance over the other high-performance reconfigurable computing systems. Developing such efficient systems poses several technical challenges such as designing and validating the FPGA architecture. A rapid prototyping approach offers several benefits in designing and developing FPGA architectures. This paper proposes a rapid prototyping method for the design and implementation of a multi-class support vector machine (MSVM) algorithm for real-time hyperspectral imagery classification using a low-cost FPGA system. We have evaluated design performance in terms of overall accuracy, resource utilization as well as timing requirements using four different datasets containing airborne, drone, and ground-based hyperspectral imagery. Experimental results show that the proposed FPGA design performs classification under strict real-time constraints

Robust algorithm for multiview registration

Multiview registration is an important stage in three‐dimensional modelling pipeline. Motion averaging is an efficient approach for multiview registration which utilises the redundancy in overlap among the scans. The averaging of the underlying relative motions is performed in the corresponding Lie‐algebra elements of the SE(3) transformation matrices. However, this method is non‐robust and affected by the presence of outliers in the set of relative motions. The authors present a graph‐based approach to filter out the outliers before performing averaging of motions. The relative motions are assigned weights based on their agreement with global motions and other relative motions. The results indicate that the authors’ approach can efficiently filter out the outliers and can thus introduce robustness to multiview registration using motion averaging

Gudalur Spectral Target Detection (GST-D): A New Benchmark Dataset and Engineered Material Target Detection in Multi-Platform Remote Sensing Data

Target detection in remote sensing imagery, mapping of sparsely distributed materials, has vital applications in defense security and surveillance, mineral exploration, agriculture, environmental monitoring, etc. The detection probability and the quality of retrievals are functions of various parameters of the sensor, platform, target–background dynamics, targets’ spectral contrast, and atmospheric influence. Generally, target detection in remote sensing imagery has been approached using various statistical detection algorithms with an assumption of linearity in the image formation process. Knowledge on the image acquisition geometry, and spectral features and their stability across different imaging platforms is vital for designing a spectral target detection system. We carried out an integrated target detection experiment for the detection of various artificial target materials. As part of this work, we acquired a benchmark multi-platform hyperspectral and multispectral remote sensing dataset named as ‘Gudalur Spectral Target Detection (GST-D)’ dataset. Positioning artificial targets on different surface backgrounds, we acquired remote sensing data by terrestrial, airborne, and space-borne sensors on 20th March 2018. Various statistical and subspace detection algorithms were applied on the benchmark dataset for the detection of targets, considering the different sources of reference target spectra, background, and the spectral continuity across the platforms. We validated the detection results using the receiver operation curve (ROC) for different cases of detection algorithms and imaging platforms. Results indicate, for some combinations of algorithms and imaging platforms, consistent detection of specific material targets with a detection rate of about 80% at a false alarm rate between 10−2 to 10−3. Target detection in satellite imagery using reference target spectra from airborne hyperspectral imagery match closely with the satellite imagery derived reference spectra. The ground-based in-situ reference spectra offer a quantifiable detection in airborne or satellite imagery. However, ground-based hyperspectral imagery has also provided an equivalent target detection in the airborne and satellite imagery paving the way for rapid acquisition of reference target spectra. The benchmark dataset generated in this work is a valuable resourcefor addressing intriguing questions in target detection using hyperspectral imagery from a realistic landscape perspective

Assessment of various parameters on 3D semantic object-based point cloud labelling on urban LiDAR dataset

Semantic labelling of LiDAR point cloud is critical for effective utilization of 3D points in numerous applications. 3D segmentation, incorporation of ancillary data, feature extraction and classification are the key stages in object-based point cloud labelling. The choice of algorithms and tuning parameters adopted in these stages has substantial impact on the quality of results from object-based point cloud labelling. This paper critically evaluates the performance of object-based point cloud labelling as a function of different 3D segmentation approaches, incorporation of spectral data and computational complexity of the point cloud. The designed experiments are implemented on the datasets provided by the ISPRS and the results are independently validated by the ISPRS. Results indicate that aggregation of dense point cloud into higher-level object analogue (e.g. supervoxels) before 3D segmentation stage offers superior labelling results and best computational performance compared to the popular surface growing-based approaches

Semi-empirical model for upscaling leaf spectra (SEMULS): a novel approach for modeling canopy spectra from in situ leaf reflectance spectra

The use of in situ hyperspectral reflectance and bio-physical measurements has been increasing in forestry. Due to limited physical accessibility in a forest environment, most of the reflectance measurements of trees are acquired at a leaf or bunch of leaves level. A few radiative transfer models are available for upscaling leaf spectra to canopy level. While these models are sophisticated, they retrieve canopy spectra based on certain assumptions. We propose ‘semi-empirical model for upscaling leaf spectra (SEMULS)’ which upscales in situ leaf spectra to canopy level based on the relationship between leaf spectra and its bio-physical parameters. The performance of the model has been quantitatively validated by comparing the upscaled canopy spectra with spectra from – CHRIS hyperspectral imagery acquired concurrently and from the PROSAIL model. Results indicate that the SEMULS retrievals are comparable with image spectra and PROSAIL with additional advantages of not requiring scene-dependent geometric-radiometric parameters and assumptions

Flexible atmospheric compensation technique (FACT): a 6S based atmospheric correction scheme for remote sensing data

Atmospheric correction is an important pre-processing step in various spatio-temporal and multi-sensor remote sensing data analyzes based applications. Absolute atmospheric corrections are carried out using physical based models, generally known as radiative transfer (RT) codes such as MODerate resolution atmospheric TRANsmission (MODTRAN), Second Simulation of the Satellite Signal in the Solar Spectrum (6S) etc. Most of the available atmospheric correction schemes are commercially off-the-shelf and use patented RT codes. The objective of the present work is to develop an open-end atmospheric correction scheme, named as Flexible Atmospheric Compensation Technique (FACT), based on open source 6S RT code. The proposed FACT scheme utilizes look-up architecture for simulating the outputs of 6S RT code for various input parameters’ combination. Input parameters such as initial visibility, columnar water vapour are estimated using the dark object and the Continuum Interpolated Band Ratio (CIBR) methods respectively. The proposed FACT scheme has been evaluated exhaustively using spatio-spectral statistical error measures such as Spatial-Root Mean Square Error (S-RMSE), Spatial-Mean Absolute Error (S-MAE) and spectral-RMSE by comparing the performance with widely used Fast Line-of-sight Atmospheric Analysis of Hypercubes (FLAASH) and the customized NASA JPL’s atmospheric correction scheme. Datasets from hyperspectral (AVIRIS-NG and Hyperion) and multispectral (LANDSAT-8 OLI and WorldView-3) remote sensors were chosen for comparative analysis of the developed atmospheric correction scheme against other atmospheric correction schemes. Results confirm that the proposed FACT scheme offers accuracy of about 95% for hyperspectral imaging sensors and close to 98% for multispectral imaging sensors when compared with FLAASH. Despite marginal disagreements for certain land cover features at the water vapour absorbing spectral regions, we find the proposed FACT scheme a plausible option for carrying out absolute atmospheric correction of various operational remote imaging sensors