Most available studies in lithological mapping using spaceborne multispectral and hyperspectral remote sensing images employ different classification and spectral matching algorithms for performing this task; however, our experiment reveals that no single algorithm renders satisfactory results. Therefore, a new approach based on an ensemble of classifiers is presented for lithological mapping using remote sensing images in this paper, which returns enhanced accuracy. The proposed method uses a weighted pooling approach for lithological mapping at each pixel level using the agreement of the class accuracy, overall accuracy and kappa coefficient from the multi-classifiers of an image. The technique is implemented in four steps; (1) classification images are generated using a variety of classifiers; (2) accuracy assessments are performed for each class, overall classification and estimation of kappa coefficient for every classifier; (3) an overall within-class accuracy index is estimated by weighting class accuracy, overall accuracy and kappa coefficient for each class and every classifier; (4) finally each pixel is assigned to a class for which it has the highest overall within-class accuracy index amongst all classes in all classifiers. To demonstrate the strength of the developed approach, four supervised classifiers (minimum distance (MD), spectral angle mapper (SAM), spectral information divergence (SID), support vector machine (SVM)) are used on one hyperspectral image (Hyperion) and two multispectral images (ASTER, Landsat 8-OLI) for mapping lithological units of the Udaipur area, Rajasthan, western India. The method is found significantly effective in increasing the accuracy in lithological mapping.

Hyperion data contain significant amount of Salt-and-Pepper/random noise and stripes even in L1R radiance images, which give rise to false absorption features appear in the radiance spectra as well as in reflectance spectra after atmospheric correction, such features can potentially mislead spectral analysis. In this paper, we present a hybrid approach for correcting pixels with no radiance values and removing Salt-and-Pepper/random noise and stripes from Hyperion radiance image. In this study, "no-data" valued pixels are identified and corrected using local median filter, an MNF transformation is used in tandem to reduce random noise, a low-pass filtering is used to correct the bad-columns and global stripes, and a Local-Brightness-Normalization based algorithm is used to detect and correct local stripes from Hyperion L1R radiance images to get random noise free and destriped image.

Hyperion, a hyperspectral sensor, is on board the National Aeronautics and Space Administration’s Earth Observing-1 (NASA-EO-1) satellite. Hyperion sensor data are the only globally available spaceborne hyperspectral system, which provides hyperspectral Earth exploration data for better-quality characterization of Earth’s surface, particularly for the determination of the composition of surface material for geological applications¹ and natural resources assessments and managements. The distributed Hyperion datasets are radiometrically corrected and orthorectified as level 1A datasets. Level 1A (L1R) datasets are generated from level 0 datasets through the following preprocessing steps: echo correction, smear correction, background removal, bad pixel repair, radiometric correction, and finally checking the image quality.^2,3 The above corrections, in principle, are expected to recover untrustworthy/noisy pixels, though even a cursory visual inspection of a typical Hyperion L1R image shows this is not the case. Salt-and-pepper/random noise and vertical stripes (spatially nonuniformity) appear in most of the bands in Hyperion images with varying amounts. In particular, bands close to the water vapor, oxygen, and carbon dioxide absorption wavelengths are dominated by random noise as well as stripes.^4,5 Accurate detection and correction of random noise and vertical stripes are essential, because pixels could possibly exhibit spikes and spurious absorption features in spectra, which may mislead spectral analyses for qualitative and quantitative interpretations.

Optical remote sensing data are freely available on a global scale. However, the satellite image processing and analysis for quick, accurate, and precise forest above ground biomass (AGB) evaluation are still challenging and difficult. This paper is aimed to develop a novel method for precise, accurate, and quick evaluation of the forest AGB from optical remote sensing data. Typically, the ground forest AGB was calculated using an empirical model from ground data for biophysical parameters such as tree density, height, and diameter at breast height (DBH) collected from the field at different elevation strata. The ground fraction of vegetation cover (FVC) in each ground sample location was calculated. Then, the fraction of vegetation cover (FVC) from optical remote sensing imagery was calculated. In the first stage of method implementation, the relation model between the ground FVC and ground forest AGB was developed. In the second stage, the relational model was established between image FVC and ground FVC. Finally, both models were fused to derive the relational model between image FVC and forest AGB. The validation of the developed method was demonstrated utilizing Sentinel-2 imagery as test data and the Tundi reserved forest area located in the Dhanbad district of Jharkhand state in eastern India was used as the test site. The result from the developed model was ground validated and also compared with the result from a previously developed crown projected area (CPA)-based forest AGB estimation approach. The results from the developed approach demonstrated superior capabilities in precision compared to the CPA-based method. The average forest AGB estimation of the test site obtained by this approach revealed 463 tons per hectare, which matches the previous estimate from this test site.

A hybrid algorithm based on global moments matching and local brightness normalization is proposed for correcting vertical stripes in Hyperion images. Two types of vertical stripes are identified: (1) global stripes comprising entire columns of dark pixels with brightness values lower than the global brightness, and (2) local stripes comprising intermittent segments of pixels within a specific column that have lower brightness values compared with the local neighbourhood brightness. The proposed algorithm operates in four steps. First, a minimum noise fraction-transformation-based filtering is used to minimize spatially decorrelated noise. Then no-data pixels values are corrected. Next, global stripes are demarcated and corrected. Finally, local stripes are flagged and corrected. Applications of the proposed algorithm to two Hyperion datasets show significant reduction in vertical stripes