Deep convolutional neural networks have promoted significant progress in building extraction from high-resolution remote sensing imagery. Although most of such work focuses on modifying existing image segmentation networks in computer vision, we propose a new network in this paper, Deep Encoding Network (DE-Net), that is designed for the very problem based on many lately introduced techniques in image segmentation. Four modules are used to construct DE-Net: the inception-style downsampling modules combining a striding convolution layer and a max-pooling layer, the encoding modules comprising six linear residual blocks with a scaled exponential linear unit (SELU) activation function, the compressing modules reducing the feature channels, and a densely upsampling module that enables the network to encode spatial information inside feature maps. Thus, DE-Net achieves state-of-the-art performance on the WHU Building Dataset in recall, F1-Score, and intersection over union (IoU) metrics without pre-training. It also outperformed several segmentation networks in our self-built Suzhou Satellite Building Dataset. The experimental results validate the effectiveness of DE-Net on building extraction from aerial imagery and satellite imagery. It also suggests that given enough training data, designing and training a network from scratch may excel fine-tuning models pre-trained on datasets unrelated to building extraction.

Geo-parcel based crop identification plays an important role in precision agriculture. It meets the needs of refined farmland management. This study presents an improved identification procedure for geo-parcel based crop identification by combining fine-resolution images and multi-source medium-resolution images. GF-2 images with fine spatial resolution of 0.8 m provided agricultural farming plot boundaries, and GF-1 (16 m) and Landsat 8 OLI data were used to transform the geo-parcel based enhanced vegetation index (EVI) time-series. In this study, we propose a piecewise EVI time-series smoothing method to fit irregular time profiles, especially for crop rotation situations. Global EVI time-series were divided into several temporal segments, from which phenological metrics could be derived. This method was applied to Lixian, where crop rotation was the common practice of growing different types of crops, in the same plot, in sequenced seasons. After collection of phenological features and multi-temporal spectral information, Random Forest (RF) was performed to classify crop types, and the overall accuracy was 93.27%. Moreover, an analysis of feature significance showed that phenological features were of greater importance for distinguishing agricultural land cover compared to temporal spectral information. The identification results indicated that the integration of high spatial-temporal resolution imagery is promising for geo-parcel based crop identification and that the newly proposed smoothing method is effective.

Accurate crop classification is the basis of agricultural research, and remote sensing is the only effective measuring technique to classify crops over large areas. Optical remote sensing is effective in regions with good illumination; however, it usually fails to meet requirements for highly accurate crop classification in cloud-covered areas and rainy regions. Synthetic aperture radar (SAR) can achieve active data acquisition by transmitting signals; thus, it has strong resistance to cloud and rain interference. In this study, we designed an improved crop planting structure mapping framework for cloudy and rainy regions by combining optical data and SAR data, and we revealed the synchronous-response relationship of these two data types. First, we extracted geo-parcels from optical images with high spatial resolution. Second, we built a recurrent neural network (RNN)-based classifier suitable for remote sensing images on the geo-parcel scale. Third, we classified crops based on the two datasets and established the network. Fourth, we analyzed the synchronous response relationships of crops based on the results of the two classification schemes. This work is the basis for the application of remote sensing data for the fine mapping and growth monitoring of crop planting structures in cloudy and rainy areas in the future.

Thin clouds in remote sensing images increase the radiometric distortion of land surfaces. The identification of pixels contaminated by thin clouds, known as the thin-cloud mask, is an important preprocessing procedure to guarantee the proper utilization of data. However, failure to effectively separate thin clouds and high-reflective land-cover features causes thin-cloud masks to remain a challenge. To overcome this problem, we developed a thin-cloud masking method for remote sensing images based on sparse dark pixel region detection. As a result of the effect of scattering, the path radiance is added to the radiance recorded by the sensor in the thin-cloud area, which causes the number of dark pixels in the thin-cloud area to be much less than that in the clear area. In this study, the area of a Thiessen polygon (a nonparametric measure) is used to evaluate the density of local dark pixels, and the region with the sparse dark pixel is selected as the thin-cloud candidate. Then, thin-cloud and clear areas are used as samples to train the background suppression haze thickness index (BSHTI) transform parameters, and convert the original multiband images into single-band images. Finally, an accurate thin-cloud mask is obtained for every buffered thin-cloud candidate, via the segmentation of the BSHTI band. Additionally, the multispectral images obtained by the Wide Field View (WFV), on board the Chinese GaoFen1, and the Operational Land Imager (OLI), on board the Landsat 8, are employed to evaluate the performance of the method. The results reveal that the proposed approach can obtain a thin-cloud mask with a high true-value ratio and detection ratio. Thin-cloud masks can satisfy various application demands.

Under increasingly low urban land resources, carrying out roof greening to exploit new green space is a good strategy for sustainable development. Therefore, it is necessary to evaluate the suitability of roof greening for buildings in cities. However, most current evaluation methods are based on qualitative and conceptual research. In this paper, a methodological framework for roof greening suitability evaluation is proposed based on the basic units of building roofs extracted via deep learning technologies. The building, environmental and social criteria related to roof greening are extracted using technologies such as deep learning, machine learning, remote sensing (RS) methods and geographic information system (GIS) methods. The technique for order preference by similarity to an ideal solution (TOPSIS) method is applied to quantify the suitability of each roof, and Sobol sensitivity analysis of the score results is conducted. The experiment on Xiamen Island shows that the final evaluation results are highly sensitive to the changes in weight of the green space distance, population density and the air pollution level. This framework is helpful for the quantitative and objective development of roof greening suitability evaluation.

Precise vegetation maps of mountainous areas are of great significance to grasp the situation of an ecological environment and forest resources. In this paper, while multi-source geospatial data can generally be quickly obtained at present, to realize effective vegetation mapping in mountainous areas when samples are difficult to collect due to their perilous terrain and inaccessible deep forest, we propose a novel and intelligent method of sample collection for machine-learning (ML)-based vegetation mapping. First, we employ geo-objects (i.e., polygons) from topographic partitioning and constrained segmentation as basic mapping units and formalize the problem as a supervised classification process using ML algorithms. Second, a previously available vegetation map with rough-scale label information is overlaid on the geo-object-level polygons, and candidate geo-object-based samples can be identified when all the grids’ labels of vegetation types within the geo-objects are the same. Third, various kinds of geo-object-level features are extracted according to high-spatial-resolution remote sensing (HSR-RS) images and multi-source geospatial data. Some unreliable geo-object-based samples are rejected in the candidate set by comparing their features and the rules based on local expert knowledge. Finally, based on these automatically collected samples, we train the model using a random forest (RF)-based algorithm and classify all the geo-objects with labels of vegetation types. A case experiment of Taibai Mountain in China shows that the methodology has the ability to achieve good vegetation mapping results with the rapid and convenient sample collection scheme. The map with a finer geographic distribution pattern of vegetation could clearly promote the vegetation resources investigation and monitoring of the study area; thus, the methodological framework is worth popularizing in the mapping areas such as mountainous regions where the field survey sampling is difficult to implement.