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The aim of this study is to determine the relationship between occupants’ emotional attitude, decision behavior, and environmental cognition toward window seats and learning efficiency and the mechanism of this relationship in public spaces (represented by academic libraries). Surveys were delivered to the academic library of Shanghai Jiao Tong University. A total of 280 valid face-to-face interview questionnaires was collected and analyzed for correlation and validation of theoretical models. The results show that learning experience, as a mediator of learning efficiency, has a significant impact on the model of occupants’ attitude toward window seat consumption. The impact mechanism was determined, and it indicated that in order to improve the learning efficiency of occupants, indoor re-planning should be carried out to improve the seat satisfaction and occupancy rate. This study introduces the concepts of service design and architectural consumption and constructs an occupant emotional consumption context with the window seat as the consumption product. In addition, it also has guiding value for seat reallocation in public buildings in the COVID-19 era. This theoretical framework provides a direction for the simulation of future construction consumption behavior.

Although most of deep-learning-based hyperspectral image (HSI) classification methods achieve great performance, there still remains a challenge to utilize small-size training samples to remarkably enhance the classification accuracy. To tackle this challenge, a novel two-branch spectral–spatial-feature attention network (TSSFAN) for HSI classification is proposed in this paper. Firstly, two inputs with different spectral dimensions and spatial sizes are constructed, which can not only reduce the redundancy of the original dataset but also accurately explore the spectral and spatial features. Then, we design two parallel 3DCNN branches with attention modules, in which one focuses on extracting spectral features and adaptively learning the more discriminative spectral channels, and the other focuses on exploring spatial features and adaptively learning the more discriminative spatial structures. Next, the feature attention module is constructed to automatically adjust the weights of different features based on their contributions for classification to remarkably improve the classification performance. Finally, we design the hybrid architecture of 3D–2DCNN to acquire the final classification result, which can significantly decrease the sophistication of the network. Experimental results on three HSI datasets indicate that our presented TSSFAN method outperforms several of the most advanced classification methods.

Recently, the rapid development of multispectral imaging technology has received great attention from many fields, which inevitably involves the image transmission and storage problem. To solve this issue, a novel end-to-end multispectral image compression method based on spectral–spatial feature partitioned extraction is proposed. The whole multispectral image compression framework is based on a convolutional neural network (CNN), whose innovation lies in the feature extraction module that is divided into two parallel parts, one is for spectral and the other is for spatial. Firstly, the spectral feature extraction module is used to extract spectral features independently, and the spatial feature extraction module is operated to obtain the separated spatial features. After feature extraction, the spectral and spatial features are fused element-by-element, followed by downsampling, which can reduce the size of the feature maps. Then, the data are converted to bit-stream through quantization and lossless entropy encoding. To make the data more compact, a rate-distortion optimizer is added to the network. The decoder is a relatively inverse process of the encoder. For comparison, the proposed method is tested along with JPEG2000, 3D-SPIHT and ResConv, another CNN-based algorithm on datasets from Landsat-8 and WorldView-3 satellites. The result shows the proposed algorithm outperforms other methods at the same bit rate.