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Forest logging detection is important for sustainable forest management. The traditional optical satellite images with visible and near-infrared bands showed the ability to identify intensive timber logging. However, less intensive logging is still difficult to detect with coarse spatial resolution such as Landsat or high spatial resolution in fewer spectral bands. Although more high-resolution remote sensing images containing richer spectral bands can be easily obtained nowadays, the questions of whether they facilitate the detection of logging patterns and which spectral bands are more effective in detecting logging patterns, especially in selective logging, remain unresolved. Therefore, this paper aims to evaluate the combinations of visible, near-infrared, red-edge, and short-wave infrared bands in detecting three different logging intensity patterns, including unlogged (control check, CK), selective logging (SL), and clear-cutting (CC), in north subtropical plantation forests with the random forest algorithm using Sentinel-2 multispectral imagery. This study aims to explore the recognition performance of different combinations of spectral bands (visual (VIS) and near-infrared bands (NIR), VIS, NIR combined with red-edge, VIS, NIR combined with short-wave infrared bands (SWIR), and full-spectrum bands combined with VIS, NIR, red edge and SWIR) and to determine the best spectral variables to be used for identifying logging patterns, especially in SL. The study was conducted in Taizishan in Hubei province, China. A total of 213 subcompartments of different logging patterns were collected and the random forest algorithm was used to classify logging patterns. The results showed that full-spectrum bands which contain the red-edge and short-wave infrared bands improve the ability of conventional optical satellites to monitor forest logging patterns and can achieve an overall accuracy of 85%, especially for SL which can achieve 79% and 64% for precision and recall accuracy, respectively. The red-edge band (698–713 nm, B5 in Sentinel-2), short-wave infrared band (2100–2280 nm, B12 in Sentinel-2), and associated vegetation indices (NBR, NDre2, and NDre1) enhance the sensitivity of the spectral information to logging patterns, especially for the SL pattern, and the precision and recall accuracy can improve by 10% and 6%, respectively. Meanwhile, both clear-cutting and unlogged patterns could be well-classified whether adding a red-edge or SWIR band or both in VIS and NIR bands; the best precision and recall accuracies for clear-cutting were enhanced to 97%, 95% and 81%, 91% for unlogged, respectively. Our results demonstrate that the optical images have the potential ability to detect logging patterns especially for the clear-cutting and unlogged patterns, and the selective logging detection accuracy can be improved by adding red-edge and short-wave infrared spectral bands. [ABSTRACT FROM AUTHOR]

Multispectral (MS) and hyperspectral (HS) images have been successfully and widely used in remote sensing applications such as target detection, change detection, and anomaly detection. In this paper, we aim at reviewing recent change detection papers and raising some challenges and opportunities in the field from a practitioner's viewpoint using MS and HS images. For example, can we perform change detection using synthetic hyperspectral images? Can we use temporally-fused images to perform change detection? Some of these areas are ongoing and will require more research attention in the coming years. Moreover, in order to understand the context of our paper, some recent and representative algorithms in change detection using MS and HS images are included, and their advantages and disadvantages will be highlighted. [ABSTRACT FROM AUTHOR]

Recently, land cover change detection has become a research focus of remote sensing. To obtain the change information from remote sensing images at fine spatial and temporal resolutions, subpixel change detection is widely studied and applied. In this paper, a new subpixel change detection method based on radial basis function (RBF) for remote sensing images is proposed, in which the abundance image difference measure (AIDM) is designed and utilized to enhance the subpixel mapping (SPM) by borrowing the fine spatial distribution of the fine spatial resolution image to decrease the influence of the spectral unmixing error. First, the fine and coarse spatial resolution images are used to develop subpixel change detection. Second, linear spectral mixing modeling and the degradation procedure are conducted on the coarse and fine spatial resolution image to produce two temporal abundance images, respectively. Then, the designed AIDM is utilized to enhance the RBF-based SPM by comparing the two temporal abundance images. At last, the proposed RBF-AIDM method is applied for SPM and subpixel change detection. The synthetic images based on Landsat-7 Enhanced Thematic Mapper Plus (ETM+) and real case images based on two temporal Landsat-8 Operational Land Imager (OLI) images and one Moderate Resolution Imaging Spectroradiometer (MODIS) image are undertaken to validate the proposed method. The experimental results indicate that the proposed method can sufficiently decrease the influence of the spectral unmixing error and improve the subpixel change detection results. [ABSTRACT FROM AUTHOR]

Adversarial training has demonstrated advanced capabilities for generating image models. In this paper, we propose a deep neural network, named a classified adversarial network (CAN), for multi-spectral image change detection. This network is based on generative adversarial networks (GANs). The generator captures the distribution of the bitemporal multi-spectral image data and transforms it into change detection results, and these change detection results (as the fake data) are input into the discriminator to train the discriminator. The results obtained by pre-classification are also input into the discriminator as the real data. The adversarial training can facilitate the generator learning the transformation from a bitemporal image to a change map. When the generator is trained well, the generator has the ability to generate the final result. The bitemporal multi-spectral images are input into the generator, and then the final change detection results are obtained from the generator. The proposed method is completely unsupervised, and we only need to input the preprocessed data that were obtained from the pre-classification and training sample selection. Through adversarial training, the generator can better learn the relationship between the bitemporal multi-spectral image data and the corresponding labels. Finally, the well-trained generator can be applied to process the raw bitemporal multi-spectral images to obtain the final change map (CM). The effectiveness and robustness of the proposed method were verified by the experimental results on the real high-resolution multi-spectral image data sets. [ABSTRACT FROM AUTHOR]