## 5 Scene-Driven Remote Sensing Image Object Detection
### Deep Networks under Scene-Level Supervision for Geospatial Object Detection  
The intrinsic difference between deep learning methods and traditional visual recognition methods is that the deep learning methods can autonomously learn feature representations from a large amount of data, without much expertise or effort in designing features. The deep learning methods can automatically learn hierarchical feature representations. In the hierarchical feature representations, a simple concept is first learned, and then complex concepts are built successively by being composed of simpler ones. As depicted in Figure 7, this feature abstraction process also accords with the human visual cognition process [89].  
In [91], weakly supervised learning (WSL) obtains the initial training examples via saliency-based self-adaptive segmentation and negative mining based on weakly labeled remote sensing images (RSIs). On the basis of the trained target detector refined in advance, a candidate-patch-based scheme was adopted so as to detect targets effectively. Analogously, weakly supervised learning (WSL) combined with high-level feature learning [12] was adopted for automatic detection of the optical remote sensing images (RSIs). Furthermore, in a bid to obtained more structural and semantic representation of the image patches, they extracted the low-level and mid-level features to capture the spatial information, and a deep Boltzmann machine (DBM) was utilized to learn the hidden patterns of the mid-level features, even though it can only detect single objects.  
[PERSON] et al. [92] analysed the drawbacks of weak supervision, which takes scenes as isolated ones and ignores the mutual cues between scene pairs. Hence, an end-to-end multi-class method based on two stages is put forward, which were intended to learn discriminative convolutional weights through exploiting the mutual information between scene pairs, as well as the class-specific activation weights, respectively. Besides, the paper proposes a multi-scale scene-sliding-voting strategy to compute the class-specific activation maps (CAM) and a new set of CAM-oriented segmentation methods  
Figure 7: Scene-level supervised object detection based on deep networks.  
aimed to detect objects. Even under weak supervision, the result of training deep networks has an encouraging result.  
To better classify, multi-scale convolutional neural networks (MCNN) [93] can also be used to extract deep features of the scale by considering the context information of the image. MCNN uses a series of images of different scales to perform a pyramid algorithm, and uses the back propagation algorithm [94] to automatically optimize various parameters in a deep network. In addition, because the non-linear feature is often better than the linear feature in representing the characteristics of the object, and in order to better classify, the extracted multi-scale spectral features become non-linear after the action of the non-linear activation function. Compared with classic SVM, whose overall accuracy (OA) is 80.13%, MCNN has an overwhelming advantage in accuracy (96.78%).