Bare soil is a critical element in the urban landscape and plays an essential role in urban environments. Yet, the separation of bare soil and other land cover types using remote sensing techniques remains a significant challenge. There are several remote sensing-based spectral indices for barren detection, but their effectiveness varies depending on land cover patterns and climate conditions. Within this research, we introduced a modified bare soil index (MBI) using shortwave infrared (SWIR) and near-infrared (NIR) wavelengths derived from Landsat 8 (OLI—Operational Land Imager). The proposed bare soil index was tested in two different bare soil patterns in Thailand and Vietnam, where there are large areas of bare soil during the agricultural fallow period, obstructing the separation between bare soil and urban areas. Bare soil extracted from the MBI achieved higher overall accuracy of about 98% and a kappa coefficient over 0.96, compared to bare soil index (BSI), normalized different bare soil index (NDBaI), and dry bare soil index (DBSI). The results also revealed that MBI considerably contributes to the accuracy of land cover classification. We suggest using the MBI for bare soil detection in tropical climatic regions.

High-resolution remote sensing image-based land-use scene classification is a difficult task, which is to recognize the semantic category of a given land-use scene image based on priori knowledge. Land-use scenes often cover multiple land-cover classes or ground objects, which makes a scene very complex and difficult to represent and recognize. To deal with this problem, this paper applies the well-known bag-of-visual-words (BOVWs) model which has been very successful in natural image scene classification. Moreover, many existing BOVW methods only use scale-invariant feature transform (SIFT) features to construct visual vocabularies, lacking in investigation of other features or feature combinations, and they are also sensitive to the rotation of image scenes. Therefore, this paper presents a concentric circle-based spatial-rotation-invariant representation strategy for describing spatial information of visual words and proposes a concentric circle-structured multiscale BOVW method using multiple features for land-use scene classification. Experiments on public land-use scene classification datasets demonstrate that the proposed method is superior to many existing BOVW methods and is very suitable to solve the land-use scene classification problem.

To obtain a complete representation of scene information in high spatial resolution remote sensing scene images, an increasing number of studies have begun to pay attention to the multiple low-level feature types-based bag-of-visual-words (multi-BOVW) model, for which the two-phase classification-based multi-BOVW method is one of the most popular approaches. However, this method ignores the information of feature significance among different feature types in the score-level fusion stage, thus affecting the classification performance of the multi-BOVW methods. To address this limitation, a feature significance-based multi-BOVW scene classification method was proposed, which integrates the information of feature separating capabilities among different scene categories into the traditional two-phase classification-based score-level fusion framework, realizing different treatments for different feature channels in classifying different scene categories. Experimental results show that the proposed method outperforms the traditional score-level fusion-based multi-BOVW methods and effectively explores the feature significance information in multiclass remote sensing image scene classification tasks.

Formulated as a pixel-level labeling task, data-driven neural segmentation models for cloud and corresponding shadow detection have achieved a promising accomplishment in remote sensing imagery processing. The limited capability of these methods to delineate the boundaries of clouds and shadows, however, is still referred to as a central issue of precise cloud and shadow detection. In this paper, we focus on the issue of rough cloud and shadow location and fine-grained boundary refinement of clouds on the dataset of Landsat8 OLI and therefore propose the Refined UNet to achieve this goal. To this end, a data-driven UNet-based coarse prediction and a fully-connected conditional random field (Dense CRF) are concatenated to achieve precise detection. Specifically, the UNet network with adaptive weights of balancing categories is trained from scratch, which can locate the clouds and cloud shadows roughly, while correspondingly the Dense CRF is employed to refine the cloud boundaries. Eventually, Refined UNet can give cloud and shadow proposals sharper and more precisely. The experiments and results illustrate that our model can propose sharper and more precise cloud and shadow segmentation proposals than the ground truths do. Additionally, evaluations on the Landsat 8 OLI imagery dataset of Blue, Green, Red, and NIR bands illustrate that our model can be applied to feasibly segment clouds and shadows on the four-band imagery data.

Previous works about spatial information incorporation into a traditional bag-of-visual-words (BOVW) model mainly consider the spatial arrangement of an image, ignoring the rich textural information in land-use remote-sensing images. Hence, this article presents a 2-D wavelet decomposition (WD)-based BOVW model for land-use scene classification, since the 2-D wavelet decomposition method does well not only in textural feature extraction, but also in the multi-resolution representation of an image, which is favourable for the use of both spatial arrangement and textural information in land-use images. The proposed method exploits the textural structures of an image with colour information transformed into greyscale. Moreover, it works first by decomposing the greyscale image into different sub-images using 2-D discrete wavelet transform (DWT) and then by extracting local features of the greyscale image and all the decomposed images with dense regions in which a given image is evenly sampled by a regular grid with a specified grid space. After that, the method generates the corresponding visual vocabularies and computes histograms of visual word occurrences of local features found in each former image. Specifically, the soft-assignment or multi-assignment (MA) technique is employed, accounting for the impact of clustering on visual vocabulary creation that two similar image patches may be clustered into different clusters when increasing the size of visual vocabulary. The proposed method is evaluated on a ground truth image dataset of 21 land-use classes manually extracted from high-resolution remote-sensing images. Experimental results demonstrate that the proposed method significantly outperforms previous methods, such as the traditional BOVW model, the spatial pyramid representation-based BOVW method, the multi-resolution representation-based BOVW method, and so on, and even exceeds the best result obtained from the creator of the land-use dataset. Therefore, the proposed approach is very suitable for land-use scene classification tasks.

Accurate prediction of future observations based on past data is the key to near real-time disturbance detection using satellite image time series (SITS). To overcome the limitations of existing methods, we present an attention-based LSTM Encoder-Decoder model in which the historical time series of a pixel is encoded with a bidirectional LSTM encoder while the future time series is produced by another LSTM decoder. An attention mechanism is integrated into the encoder-decoder model to align the input time series with the output time series and to dynamically choose the most relevant contextual information while forecasting. Based on the proposed model, we develop a framework for near real-time disturbance detection and verify its effectiveness in the case of burned area mapping. The prediction accuracy of the proposed model is evaluated using Moderate Resolution Imaging Spectroradiometer (MODIS) time series and compared with state-of-the-art models. Experimental results show that our model achieves the best results in terms of lower prediction error and higher model fitness. We also evaluate the disturbance detection ability of the proposed framework. The proposed approach improves the detection rate of disturbances while suppressing false alarms, and increases the temporal accuracy. We suggest that the proposed methods provide new tools for enhancing current early warning systems in real time.