Mapping and dynamic monitoring of military training-induced vegetation cover loss using Sentinel-2 images and method comparison

Xu, Xiaoyu; Ban, Bibek; Howard, Heidi R.; Chen, Shu; Wang, Guangxing

doi:10.1007/s10661-023-10918-2

Cited by 3 publications

(1 citation statement)

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“…Each pixel in the HSI consists of a different spectrum in terms of wavelength, which relates to the material composition of the features [1]. HSI, with its high spectral resolution, has been widely used in many applications such as crop monitoring [2], [3], urban development [4], [5], the mining industry [6], [7], environmental and natural resources management [8], [9], surveillance [10], [11], etc. This paper mainly focuses on hyperspectral image classification, which intends to assign every pixel in the HSI to a specific thematic class.…”

Section: Introductionmentioning

confidence: 99%

A Deep Spectral-Spatial Residual Attention Network for Hyperspectral Image Classification

Chhapariya,

Buddhiraju,

Kumar

2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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In recent years, deep learning algorithms, particularly convolutional neural networks (CNNs), have significantly improved the performance of the hyperspectral image (HSI) classification. However, due to the high dimensionality of HSI and limited training samples, the deep neural network causes model overfitting. Additionally, considering all the bands of HSI datasets equally for feature learning and being unable to distinguish between the edge and the center pixels of a neighborhood reduces classification accuracy. Thus, in this paper, we propose an end-toend deep spectral-spatial residual attention network (DSSpRAN) motivated by the attention mechanism of the human visual system for HSI classification. The DSSpRAN considers input HSI data as a 3-D cube instead of using dimensionality reduction methods. The proposed model simultaneously incorporates spectral and spatial features by considering a spectral residual attention network (SRAN) and a spatial residual attention network (SpRAN). In SRAN, the weights are assigned and learned adaptively to select essential features from each band. The SpRAN enhances the importance of classifying each nearby pixel to the center pixel. It assigns the same label as that of the center pixel to the surrounding pixels, thus limiting pixels with different labels. The proposed method has been evaluated on five different datasets to prove the state-of-the-art for various land use land cover scenarios. A comprehensive qualitative and quantitative analysis of the results shows that the proposed method significantly outperforms other state-of-the-art methods.

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