1、Tensor Flow
Tensor Flow is a programming system developed by
Google which represents computations as graphs.
Computation Graph is first constructed to train the neural
network and then executed using Sessions. Tensors are arrays
that represent data. Feed and Fetch operations manipulate
data. This library is typical for machine learning purpose

derived from parent Theano.
Tensorflow 是由谷歌以圖形表示計算的程式設計系統。首先構建計算圖來訓練神經網路然後使用會話執行。張量是代表資料的陣列，來操作資料。這個庫來自他的父類典型的機器學習庫Theano。
A dataset of 8,000 images should be used to train the
model. From the dataset 5,000 will be used for training and
3,000 will be used for testing. Training Dataset consist of
positive images and negative images. Training set is
constructed by tilling the Satellite images into small frames
and then positive image i.e. tile with ship is superimposed
with tile not containing any ship.
一般應使用8000張左右影象來訓練模型。拿出5000張資料集用來訓練，拿出3000張資料集用來測試。訓練資料集包括好的和壞的影象。訓練集是通過將衛星影象細分為小幀然後再生成正像即帶有船隻和不帶有船隻的。
Neural network uses Weight and biases to train the
dataset. Using back propagation algorithm their values can be
adjusted. The cross-entropy is measuring how inefficient our
predictions are for describing the truth. Tensor Flow
minimizes cross-entropy using the gradient descent algorithm
with a learning rate of 0.5. Gradient descent is a simple
procedure, where Tensor Flow simply shifts each variable a
little bit in the direction that reduces the cost. Iteration will
be performed over the training model to gain accuracy of
minimum 90%. That is out of ten vessels detected one will be
false alarm.
神經網路使用權重和偏置來訓練資料集。使用反向傳播演算法來調整引數值。交叉熵是計算效率和描述我們的真實值。Tensor Flow使用梯度下降演算法以0.5的學習速率使交叉熵最小化。梯度下降是一個程式，tensorflow只是將每個變數沿著損失最小的方向移動一小步。將在訓練模型上進行迭代以獲得至少90％的精確度。就是說檢測的十艘船裡面有一艘船會有錯誤警報。
After training, System is given input image to detect the ship
from entire matrix of image. Detected vessel will be enclosed
by rectangle.
訓練後，系統會從整個影象矩陣提供輸入影象以檢測船隻。檢測到的船隻將按矩形被封閉。
Accuracy of System can be calculated by using test images. Softmax Regression technique is used to get probability of tile containing ship in range of 0 and 1[10]. If there is more than 90% probability that given tile has ship, then that tile will be highlighted.
可以通過使用測試影象來計算系統的準確性。 Softmax迴歸技術用於獲取包含船舶的圖片在0到1之間的概率。 如果給定的圖片有上船的可能性超過90％，則該圖片將突出顯示。
Another research paper defines image features by their morphological intrinsic characteristics, instead of using their boundary . The method is focused on image structural
information which is collected by applying morphological
operators with a multi-scale approach. Concepts of
morphological profile and derivative of morphological
profile(DMP) are used to create a feature vector from a single
image.
另一篇研究論文通過影象的內在特徵來定義影象特徵，而不是使用邊界。 該方法專注於影象結構通過應用形態學收集的資訊
操作員採用多尺度方法。形態特徵的概念和形態導數經常被用來從單張圖片中創造變數。
The basic idea behind using morphological operators
is to identify even smallest of differences and features from an
image (assuming that the resolution of the image is good
enough).The paper concentrates on linear feature extraction.
One of the methods used for feature extraction is
DAFE(discriminant analysis feature extraction).However it
has a weakness that it is not directly related to the probability
of error in classification. To overcome this, the authors have
proposed another method known as DBFE (decision boundary
feature extraction).The authors showed that DBFE overcomes
many problems and have extended its application to neural
networks.
使用形態運算子的基本思想就是要識別出最小的差異和特徵
圖片（假設圖片的解析度足夠好）。本文著重於線性特徵提取。
用於特徵提取的方法之一是DAFE（判別分析特徵提取）。
缺點是它與分類錯誤的概率沒有直接關係。 為了克服這個問題，作者提出了另一種稱為DBFE（決策邊界特徵提取）的方法。作者表明DBFE克服了許多問題並將其應用擴充套件到神經網路。
DBFE: The authors showed that discriminant
informative features and discriminant redundant features can
be extracted from the decision boundary itself. The difference
between informative and redundant features defines the
likelihood related to the decision boundary. In discriminant
informative features the likelihood can be defined by a line
normal to the decision boundary whereas in discriminant
redundant features it is defined by a line parallel to the
decision boundary. The likelihood of the two features as
shown in below figure.
DBFE：作者證明了這種區別資訊性特徵和區別性冗餘特徵可以
從決策邊界本身提取。這種資訊性和冗餘性的特徵的定義可能和決策邊界相關。在判別資訊特徵中，可能性可以由垂直於決策邊界的線定義，而在判別冗餘特徵中，可能性可以由與決策邊界平行的線定義。這兩個特徵的可能性如下圖所示。
判別資訊性示例。 在此，X表示模式，b表示常數，f（X）表示似然比，t表示決策邊界。
DBFE Procedure for Neural Networks (Two-Pattern Class
Case):
Step 1 Train the neural network using all features.
Step 2 For each training sample correctly classified as a class,
find the nearest sample correctly classified as class. Repeat
the same procedure for the samples classified as a class.
Step 3 The line connecting a pair of samples found in Step 2
must pass through the decision boundary, since the pair of
samples are correctly classified differently. By moving along
this line, find the point on the decision boundary or near the
decision boundary within a threshold.
Step 4 At each point found in Step 3, estimate the normal
vector.
DBFE程式提供了兩個神經網路示例
步驟1 使用所有的特徵訓練神經網路
步驟2 對於正確分類為類別的每個訓練樣本，請找到正確分類為類別的最近的樣本。重複相同的程式把樣本分類。
步驟3 由於正確地對這對樣本進行了不同的分類，因此在步驟2中找到的連線一對樣本的線必須穿過決策邊界。通過移動這條線，發現決策邊界或者接近決策邊界的點在一個閾值範圍內。
步驟4 對於每個在步驟3中發現的點，估計法線向量。
The rate of false alarms near the ports can thus be minimized
by verifying the output observed from tensor flow using dbfe
as second level classifier.
通過使用DBFE驗證從張量流觀察到的輸出作為二級分類器，可以將埠附近的誤報率降至最低。
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結論

**
Using tensor flow we aim to develop more efficient and
accurate system for detection of ships in real time. This would
therefore increase the security at the coastal areas and improve
the coastal surveillance. Also it would help to detect the lost
ships or any other objects in the ocean. Moreover, if trained
the system accordingly, the moving ships can be detected by
obtaining the videos from a respective satellite. DBFE on the
other hand would give us accuracy in detecting the ships at
ports. The most highlighted disadvantage that has emerged
through research is that the ships at port or near the dock
cannot be correctly classified most of the times. So using
DBFE would therefore solve this problem. Hence this newly
proposed system would be not only efficient but also accurate
enough to detect ships in real time.
我們使用tensorflow的目的是發展對船舶實時監測更有效和精確的系統。這將會增加沿海區域的安全性和提高沿海監測能力。他也能夠幫助檢測失聯的船隻和任何在海面的物件。而且，如果相應的訓練系統，那麼移動的船隻能夠通過各自的衛星獲取的視訊進行檢測。另一方面，DBFE將使我們能夠準確地檢測港口的船舶。
通過研究得出的最突出的缺點是，在大多數情況下，無法正確分類港口或碼頭附近的船隻。因此使用DBFE將解決這個問題。因此這個新的提議在實時船舶檢測中不僅高效而且精確度足夠好。

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