Change detection (CD) is the process of identifying changes in the category or attributes of ground objects by observing remote sensing images (RSI) taken at different times. In recent years, transformers have shown great potential in CD. However, current transformer-based CD networks have not fully exploited the capabilities of the transformer, especially when fusing features from bi-temporal and multi-stages. In this work, a pure transformer-based CD network (TFN) is built to fuse features better. Specifically, we build a Siamese CD network based on the transformer. Bi-temporal features are fused through a Shifted Window Fusion Model (SWFM) to address the misalignment between the features. In the decoding phase, a Multi-Scale Transformer Decoder (MSTD) is introduced to generate more complete change masks. The proposed method is validated on the WHU and SECOND datasets, demonstrating state-of-the-art performance (SOTA).

This study investigates the potential of using satellite-retrieved biophysical variables to address the scarcity of agricultural management data when modeling crop productivity across heterogeneous fields with a terrestrial biosphere model (TBM). A two-season field trial was conducted in Spain, providing various combinations of nitrogen (N) fertilization and irrigation levels. The crop responses to these management levels were found to be well represented by the Leaf Area Index (LAI) retrieved from the Sentinel-2 data. The satellite-retrieved LAI was then incorporated into a terrestrial biosphere model to estimate crop biomass. This satellite-derived model produced accurate biomass estimates with an overall R2of 0.52 and RMSE of 269.7 g m-2(42.6%), with no prior knowledge of management practices nor local calibration. This study confirms the capability of satellite remote sensing to capture crop responses to management practices and highlights its potential to optimize resource use efficiency in agricultural systems.

Stripe noise still affects full-spectrum airborne hyperspectral imager (FAHI) images after laboratory radiometric calibration, which seriously affects the subsequent applications of the imager. Therefore, two state-of-the-art methods, median linear correction (MLC) and Fourier transform filtering (FTF), were proposed to restore FAHI images, and the residual stripes were removed in most cases. However, these methods have their own limitations. For instance, the restored image has a slight “shadow” in cases where the high-response digital numbers (DNs) of the detector are aligned with the flight direction. This letter proposes a new method based on improved statistics to restore FAHI images. In this method, the hyperspectral image data from the adjacent flight paths is used to obtain the uniform response DNs for nearly identical low and high irradiances. Subsequently, a statistics-based MLC method is used to eliminate the stripe noise. To quantitatively evaluate the restoration results, we compared results with MLC and FTF methods. The change in mean value and mean relative deviation of the proposed method for the high-response DNs area of the image is 0.25% and 0.97%, respectively, better than that of the other two methods. The experimental results demonstrate that the proposed method is effective for removing stripe noise and preserving accurate image information of push-broom hyperspectral imagery.

Airborne hyperspectral images are used for crop identification with a high classification accuracy because of their high spectral resolution, spatial resolution, and signal-to-noise ratio (SNR). However, the tradeoffs between the three core parameters of a hyperspectral imager (SNR, spatial resolution, and spectral resolution) should be considered for designing an efficient imaging system. Only a few reported studies on the analysis of the impact of SNR on identification accuracy are available. Further, the tradeoffs and mutual interactions among these parameters are rarely considered. In this empirical study, our aim was to understand the relationship among the core parameters and their effects on crop identification accuracy by analyzing the tradeoffs and mutual interactions among these parameters. We analyzed the hyperspectral images of a typical plain agricultural area in Xiongan, China, acquired by the newly developed sensor airborne multimodular imaging spectrometer (AMMIS). The fundamental images were transformed to form datasets with different ranges of spectral resolution, spatial resolution, and SNR using data reconstruction methods. We adopted the classification and regression tree (CART), random forest (RF), and k-nearest neighbor (kNN) classifiers, and observed the overall accuracy (OA) across the degraded hyperspectral datasets. The experimental results indicated that the OA decreased with a decreasing SNR. As the spectral resolution became coarser, the OA first increased, plateaued, and then decreased. However, the OA increased with decreasing spatial resolution. This study was performed with the goal of bridging the knowledge gap between the back-end hyperspectral sensor designing and its front-end applications.