This study presents a new end-to-end change detection network, called difference-enhancement dense-attention convolutional neural network (DDCNN), that is designed for detection of changes in the bitemporal optical remote sensing images. To model the internal correlation between high-level and low-level features, a dense attention method consisting of several up-sampling attention units is proposed. Both the up-sampling spatial and up-sampling channel attention are adopted by the unit. The unit, which can use high-level features with rich category information to guide the selection of low-level features, can use the spatial context information to capture the changed features of ground objects. Furthermore, DDCNN also pays attention to the differentiating features of the bitemporal images. By introducing a DE unit, each pixel is weighted and the features are selectively aggregated. The combination of dense attention and the DE unit improves the effectiveness of the network and its accuracy in extracting the change features. The effectiveness of the proposed approach is demonstrated via five challenge data sets. The experimental results show that DDCNN achieves new state-of-the-art change detection performance on these five challenging data sets. For the seasonal change detection data set in particular, compared with the best existing change detection model, the proposed method increases the F1 score and IoU by 2.96% and 5.17%, respectively; compared with the baseline method, our method improved 3.75% and 6.50% on the F1 score and IoU, respectively.

Accurate and efficient semantic segmentation of buildings in high spatial resolution (HSR) remote sensing images is the basis for applications such as fine urban management, high-precision mapping, land resource utilization investigation, and human settlement suitability evaluation. The current building extraction methods based on deep learning can obtain high-level abstract features of images. However, due to the limitation of convolution kernel size and the vanishing gradient, the extraction of some buildings is inaccurate, and some small-volume buildings are missing as the network deepens. In this regard, we design a horizontally connected residual blocks-based multiscale attention network to achieve high-quality extraction of buildings in HSR remote sensing image. In this network, we subdivide each residual block by channel grouping and feature horizontal connection to consider the difference and saliency of feature information between channels, and then combine the output features with multiscale attention module to consider the contextual semantic relationship of different regions and integrate multilevel local and global information of buildings. A stepwise up-sampling mechanism is designed in the decoding process to finally achieve precise semantic segmentation of buildings. We conduct experiments on two public datasets and compare the proposed method with state-of-the-art semantic segmentation methods. The experiments show that our method could achieve better building extraction results in HSR remote sensing image, which proves the effectiveness of our proposed method.

Forests are an essential part of the ecosystem and play an irreplaceable role in maintaining the balance of the ecosystem and protecting biodiversity. The monitoring of forest distribution plays an important role in the conservation and management of forests. This paper analyzes and compares the performance of imagery from GF-1 WFV, Landsat 8, and Sentinel-2 satellites with respect to forest/non-forest classification tasks using the random forest algorithm (RF). The results show that in the classification task of this paper, although the differences in classification accuracy among the three satellite datasets are not remarkable, the Sentinel-2 data have the highest accuracy, GF-1 WFV the second highest, and Landsat 8 the lowest. In addition, it was found that remotely sensed data of different processing levels show little influence on the classification accuracy with respect to the forest/non-forest classification task. However, the classification accuracy of the top of the atmosphere reflectance product was the most stable, and the vegetation index has a marginal effect on the distinction between forest and non-forest areas.

Forests are the most widely distributed terrestrial vegetation type and play a significant role in the global carbon cycle and ecological diversity. Accurate and timely forest detection provides essential data for forest management and development. Current forest-related products differ in definition, accuracy, and spatial consistency, making them difficult to use. Therefore, it is necessary to map forest cover under a unified framework. However, detecting forests on a large scale requires high-quality and representative samples, which can be challenging. This study proposes a weakly supervised forest classification framework (WSFCF) that uses noisy labels. The WSFCF is designed to address label generation, correction, and sample location optimization. We employ a spectral-spatial network to extract forest cover accurately for medium-resolution forest classification. The experimental results show that the proposed method outperforms the compared methods, achieving an accuracy of 91.76% OA and 88.28% F1 score on 110 GF-1 WFV images. This supports the subsequent extraction of national-scale forest cover and encourages the mapping of China’s forest cover using GF-1 WFV images. Moreover, the proposed method produces satisfactory outcomes for objects such as water, farmland, and built-up areas within the study area, demonstrating its effectiveness and potential for transferability.

In the face of global population growth and climate change, the protection and rational utilization of cropland are crucial for food security and ecological balance. However, the complex topography and unique ecological environment of the Qinghai-Tibet Plateau results in a lack of high-precision cropland monitoring data. Therefore, this paper constructs a high-quality cropland dataset for the YarlungZangbo-Lhasa-Nyangqv River region of the Qinghai-Tibet Plateau and proposes an MSC-ResUNet model for cropland extraction based on Landsat data. The dataset is annotated at the pixel level, comprising 61 Landsat 8 images in 2023. The MSC-ResUNet model innovatively combines multiscale features through residual connections and multiscale skip connections, effectively capturing features ranging from low-level spatial details to high-level semantic information and further enhances performance by incorporating depthwise separable convolutions as part of the feature fusion process. Experimental results indicate that MSC-ResUNet achieves superior accuracy compared to other models, with F1 scores of 0.826 and 0.856, and MCC values of 0.816 and 0.847, in regional robustness and temporal transferability tests, respectively. Performance analysis across different months and band combinations demonstrates that the model maintains high recognition accuracy during both growing and non-growing seasons, despite the study area’s complex landforms and diverse crops.